Generative Engine Optimization (GEO) news, analysis, and trends

15 Social Media Ideas for Small Businesses in December

Tue, 02 Dec 2025 13:12:55 GMT

Festive, engaging, and easy-to-execute content ideas to help you finish the year strong.

December is a magical month — holiday lights glow, families gather, and small businesses are busier than ever. It's also one of the best months to show up consistently on social media. With people shopping, celebrating, and searching for local recommendations, your content can help drive visibility and revenue right through the new year.

If you're a small business owner in West Georgia or beyond, here are 15 social media ideas to keep your feed fresh, festive, and full of holiday cheer this December.

�� 1. Holiday Gift Guide

Create a simple gift guide featuring your products or services.
Break it into categories like:

Gifts under $25
Stocking stuffers
The “hard to shop for” person
Gifts for teachers, moms, or coworkers

Carousel posts work great for this!

�� 2. Behind-the-Scenes: Holiday Edition

Show how you’re preparing for the holiday rush:

Packaging gifts
Decorating your shop
Prepping orders
People love the real-life moments.

❤️ 3. Customer Appreciation Post

Highlight your amazing customers with a “thank you” message.
Bonus: Feature real customer photos (with permission).

�� 4. Holiday Decor Feature

If you’ve decorated your storefront or office, share it!
Or create a "Before & After" holiday transformation video.

✨ 5. Staff Favorites

Have your team share their favorite products or services for gifting.
This builds trust and personality.

��️ 6. Shop Local Spotlight

Feature another small business you love — and tag them.
They’ll often return the favor!
Local collaboration = increased reach.

⛄ 7. Seasonal Polls & Questions

Ask your audience fun, engaging questions:

Christmas tree: real or fake?
Hot cocoa or coffee?
Favorite holiday movie?

Easy engagement boosters.

�� 8. Shipping Deadlines / Order Cut-Offs

If you sell products, don’t forget to post reminders!
People rely on these during the holiday rush.

�� 9. Holiday Playlist

Create a Spotify playlist for your brand and share the link.
Perfect for boutiques, cafés, and salons.

�� 10. Gratitude Post

December is full of emotion — share what you’re grateful for this year:

Community support
Loyal customers
Your team
Local partnerships

�� 11. Year-in-Review Highlight

Share big wins from the year:

New products
Events
Community highlights
Media features
Milestones

This builds trust and credibility.

�� 12. Local Events Calendar

Share what’s happening around West Georgia this month.
Your audience LOVES local info, and it positions you as a community leader.

�� 13. Winter Tips for Your Industry

Provide useful tips related to your business.
Examples:

Restaurants: winter recipes
Realtors: cold-weather home tips
Retail: holiday styling ideas
Gyms: staying active during the holidays

�� 14. Holiday Countdown

Create a fun "12 Days of Christmas" or “Countdown to Christmas” series with mini posts each day.

❄️ 15. “See You Next Year” Post

On December 31, post a warm, heartfelt message thanking your customers and teasing upcoming plans for the new year.

�� Final Thoughts

December is full of opportunities to connect with your audience, celebrate your community, and showcase your brand’s personality. With a thoughtful mix of festive posts, helpful info, and real-life moments, small businesses can build lasting relationships and finish the year on a strong note.

Need help creating holiday graphics, a content calendar, or templates you can reuse all month long?
Hometown Beat has your back.

How Local Businesses Can Maximize the Biggest Shopping Weekend of the Year

Tue, 18 Nov 2025 01:27:37 GMT

��️ Leveraging Black Friday, Small Business Saturday & Cyber Monday to Promote Your Small Business

The biggest shopping weekend of the year isn’t just for big-box stores and e-commerce giants — it’s a golden opportunity for small businesses to shine. Black Friday, Small Business Saturday, and Cyber Monday give local business owners three powerful, back-to-back chances to boost visibility, drive sales, and build long-lasting customer relationships.

Whether you're a retail boutique, service provider, café, or creative maker, here’s how to leverage the holiday shopping rush to grow your brand and get more customers walking through your doors (or clicking “buy now”).

�� 1. Get Clear on Your Offers

Don’t wait until Thanksgiving week to decide your promotions. Create simple, compelling offers that appeal to your audience and reinforce your brand.

Examples:

Black Friday: “Early-Bird Door Busters,” flash sales, bundles
Small Business Saturday: “Shop Local Specials,” free gift with purchase, loyalty rewards
Cyber Monday: Online-only discounts, digital gift card deals, shipping specials

�� Tip: Highlight savings AND value. Shoppers want deals, but they also want meaning — especially when choosing local.

�� 2. Market Early (and Often)

Consumers are bombarded this weekend — so plan to stand out with consistent, eye-catching marketing across your channels.

Strategies:

Post countdown graphics starting 7–10 days out
Send an email teaser with a “sneak peek”
Use Instagram Stories to build excitement
Update your Google Business Profile with holiday hours & promos
Add your offers to your website homepage

If your messaging is clear, repeated, and easy to understand, you’ll stay top-of-mind when shoppers are ready to spend.

�� 3. Create an In-Store (or Online) Experience

People remember experiences, not just sales. Give them a reason to shop with you over the big box stores.

Ideas:

Offer free hot cocoa, cookies, or a small holiday treat
Feature a “photo spot” with festive decor
Hand out reusable shopping totes with your logo
Create a “local gift guide” showcasing items at various price points
Build curated bundles for quick, ready-to-wrap gifts

Small Business Saturday is especially about community. Lean into that hometown charm.

�� 4. Collaborate With Other Local Businesses

Partnerships amplify reach without extra cost.

Collaborations that work:

Joint giveaways
Cross-store coupon swaps
Punch cards that reward shopping at multiple locations
Co-hosting a holiday market or pop-up

When businesses work together, the whole community wins — and customers love supporting a local network.

�� 5. Leverage Social Media: Show, Don’t Just Tell

Use the weekend to show your personality, your passion, and your people.

Post Ideas:

Behind-the-scenes prepping for Black Friday
Meet-the-owner spotlight
Customer testimonials
“Small Business Saturday means ____ to me”
Unboxing or product demo videos
Before/after of gift wrapping or holiday displays

And don’t forget Reels and TikToks — they’re still the fastest way to reach new customers organically.

�� 6. Encourage User-Generated Content

Your customers are your biggest cheerleaders. Let them help spread the word.

Ask them to:

Share photos shopping local
Tag your business in their posts
Use a branded hashtag
Post unboxing videos of their purchases

Run a giveaway or contest to incentivize participation.

�� 7. Make Cyber Monday Count

Cyber Monday isn’t just for global e-commerce brands — small businesses can win here too.

Try:

Online-exclusive bundles
“Spend $50, get $10 gift card” promos
Free shipping or local delivery
Digital services or subscriptions
Selling gift cards at a discount

Even service-based businesses can promote online deals on future appointments or packages.

�� 8. Keep the Momentum Going

The weekend is just the beginning. Follow up with:

Thank-you posts
Thank-you emails
“Holiday gift guide” newsletters
December promotions
Last-minute gift reminders
Customer appreciation events

Consistency builds loyalty — and loyalty builds long-term revenue.

❤️ Final Thoughts

Black Friday, Small Business Saturday, and Cyber Monday represent one of the best opportunities of the year for small businesses to increase visibility, deepen customer relationships, and boost sales.

The key is simple: Prepare, promote, engage, and follow up.
And remember — your community wants to support you. Make it easy, fun, and meaningful for them to do so.

If you need help creating graphics, marketing campaigns, or a complete holiday strategy, Hometown Beat is here to help .

2026 Social Media Strategy: What Small Business Owners Should Know

Sun, 09 Nov 2025 23:48:31 GMT

The world of social media never slows down — and as we move into 2026, the rules of engagement are evolving once again. For small business owners across West Georgia and East Alabama , this year isn’t about doing more — it’s about doing what matters better.

Whether you’re running a local café, a boutique, or a service-based business, here’s what you need to know to keep your social media strategy smart, intentional, and ahead of the curve.

�� 1. Embrace “Humanized” Content

The big trend of 2026? Authenticity still wins.
Audiences are craving more real, unscripted, and behind-the-scenes moments. Think less polished stock imagery — more honest photos of your team, your customers, and your community.

Quick Wins:

Post a short video from your shop floor or event setup.
Introduce your team members and let their personalities shine.
Share stories that connect — not just sell.

�� Tip from Hometown Beat: Real people create real engagement. Every post should sound like you, not a marketing robot.

�� 2. Short-Form Video is Still King

If your 2025 content strategy didn’t include short videos, 2026 is your year to start.
Platforms like Instagram Reels, TikTok, and YouTube Shorts continue to dominate. The algorithm favors fast, informative, and entertaining clips that stop the scroll.

Try This:

15-second product demos
Quick “how-to” clips
Local event previews or business tips

And don’t stress about high production — smartphone videos with good lighting and confidence work beautifully.

�� 3. SEO + Social Media = Power Couple

Search and social are blending fast. Platforms like Instagram, TikTok, and even Facebook are doubling down on searchable content .

This means your captions, hashtags, and bios matter more than ever.

Optimize Every Post:

Use local keywords (like Carrollton coffee shop or Villa Rica realtor).
Add searchable phrases in captions — not just hashtags.
Make your location visible and consistent across platforms.

�� Pro Move: Update your Google Business Profile regularly — it helps tie your social visibility to real local search traffic.

�� 4. Conversation > Promotion

People follow you because they want connection, not constant sales pitches. In 2026, the best brands treat social media like a two-way conversation.

Engage Authentically:

Reply to comments within 24 hours.
Ask questions in your captions.
Highlight your customers by resharing their posts.

Remember — connection creates conversion.

�� 5. Smarter Automation, Not Autopilot

AI tools are transforming how small businesses create, schedule, and analyze content — but don’t rely on automation alone. Use it to enhance your creativity, not replace it.

Best Uses for AI:

Brainstorming content ideas
Writing first drafts of captions
Tracking analytics trends

But always add your local touch. Your customers should hear your voice — not a generic one.

�� 6. Consistency Beats Perfection

In 2026, consistency matters more than viral moments.
Even one strong, thoughtful post per week can outperform daily random content.

Stay Consistent By:

Planning your content monthly
Using templates (we can help!)
Tracking which posts drive engagement or leads

❤️ 7. Community Is Your Superpower

The biggest advantage local businesses have is connection.
Your customers aren’t just followers — they’re neighbors.
In 2026, double down on community partnerships, shoutouts, and collaborations.

Host joint giveaways, tag other small businesses, or share community events.
When you lift others up, your engagement grows organically.

�� Final Thoughts

The future of social media isn’t about algorithms — it’s about authentic storytelling.
In 2026, your best marketing strategy is to stay true to your hometown roots , share your story boldly, and connect with your audience like never before.

And if you need a hand building a social media strategy that actually works — that’s what we do best.
�� Contact Hometown Beat today

5 Ways to Use User-Generated Content to Build Trust in Your Community

Tue, 04 Nov 2025 03:44:32 GMT

In today’s marketing landscape, trust isn’t built on flashy ads—it’s earned through real stories and authentic connections. That’s why user-generated content (UGC) is one of the most powerful tools in your local marketing arsenal. At Hometown Beat, we help businesses across Carrollton, GA and the greater West Georgia region harness UGC to amplify their social media, boost local SEO, and turn everyday customers into brand advocates. Whether you're running a family-owned café in Villa Rica or a service-based business in Newnan, UGC gives your marketing the credibility it needs.

Here are five ways you can start using UGC to build trust and visibility in your community:

1. Repost Customer Photos and Reviews

When your happy customers post photos of their experience with your product or service, that’s social proof you can’t buy. Reposting their content on your business’s Instagram, Facebook, or Google Business Profile is a great way to say thank you—and to show future customers that people in their neighborhood trust you.

✅ Tip: Always ask permission and tag the original poster to keep it friendly and authentic.

2. Share Local Testimonials as Short Videos or Reels

A quick 10-second video of a customer raving about your restaurant, boutique, or service can go further than a slick ad. These mini testimonials make your brand more approachable and trustworthy. Video content also performs better in Instagram and Facebook algorithms—boosting your reach.

�� Bonus: Turn a few clips into a montage for even greater impact.

3. Host a UGC Contest or Challenge

Encourage your community to post their own content with a specific hashtag tied to your business. Run a giveaway for the best photo, video, or story related to your brand. Not only does this drive engagement, it floods your feed with share-worthy content.

�� Example: “Show us how you use our product in your everyday life! Tag us and use #MyHometownBeat for a chance to win!”

4. Highlight Community Voices in Your Blog or Newsletter

Ask loyal customers to share stories or tips about your product/service that you can repurpose into a blog post. This long-form UGC not only builds deeper trust, it’s also great for SEO —especially when you include location keywords like “ Villa Rica, GA ” or “ Paulding County business owner.”

�� Pro tip: Structure these like mini-interviews for easy readability.

5. Turn UGC Into Ads (With Permission!)

User content can also fuel your next social media ad campaign . Facebook and Instagram ads using UGC tend to outperform traditional graphics because they look and feel more authentic. With permission, you can turn a customer’s raving review into a click-worthy ad that’s relatable and results-driven.

�� “I found this amazing bakery through Hometown Beat —best peach cobbler in all of West Georgia !”

Why UGC Works So Well:

It feels real. People trust other people more than they trust brands.
It taps into community pride—especially in local areas
It gives your customers a voice, which fosters loyalty and engagement.
It provides a steady stream of fresh, relevant content for your marketing.

�� Let Hometown Beat Help You Build a UGC Strategy That Works

From social media management to AIO (AI Optimization) and SEO , Hometown Beat helps West Georgia businesses like yours build real connections that drive real results. If you’re ready to amplify your marketing with user -generated content and community-based strategies, contact us today for a free digital audit!

Marketing in 2026: How to Stay Ahead of the Curve

Wed, 29 Oct 2025 21:44:13 GMT

�� The Future of Marketing Is Here — Are You Ready?

As we head into 2026, digital marketing is evolving faster than ever—driven by artificial intelligence, voice and visual search, new social platforms, and rapidly changing consumer behaviors. For businesses, staying competitive means doing more than just keeping up. It means staying ahead.

That’s where Hometown Beat comes in. As a trusted digital marketing agency in Carrollton, GA , we specialize in helping local businesses future-proof their strategies with smart tools and cutting-edge solutions like AI Optimization (AIO) , SEO , website design , and social media management tailored to your local audience.

�� 1. Embrace AI-Powered Marketing (AIO)

Artificial Intelligence Optimization (AIO) is no longer just a trend—it’s a must-have. AIO ensures your business content is discoverable by AI tools like ChatGPT, Gemini, and voice assistants. These tools increasingly shape how customers search, shop, and interact with brands.

In 2026, optimizing for AI means:

Structuring content for AI search engines
Using schema markup and FAQs to earn visibility in answers
Creating natural, conversational content that AI can easily cite
Updating your website for Answer Engine Optimization (AEO)

Don’t just rank on Google—be the answer AI chooses to recommend.

�� 2. Build a Mobile-First Experience

Mobile usage will continue to dominate in 2026. If your website or content isn’t mobile-friendly, you’re missing out—plain and simple.

Make sure your business:

Has a fast-loading, responsive website
Uses click-to-call buttons and interactive maps
Features mobile-optimized landing pages and forms
Creates short-form video content for Instagram Reels, TikTok, and YouTube Shorts

�� 3. Prioritize SEO + Local SEO

Search engine algorithms have changed. SEO in 2026 is not just about keywords—it’s about intent, authority, and local presence. At Hometown Beat , we build SEO strategies that help West Georgia businesses dominate Google and AI search results.

Why SEO still matters:

People search locally ("plumber near me," "best restaurant in Carrollton")
AI engines pull from top-ranking SEO content
Local reviews and listings influence visibility
Structured data improves your chances of appearing in rich results and voice search

�� 4. Be Active on the Right Social Channels

Social media is still critical—but not every platform works for every business. In 2026, it’s all about strategic posting and automation. That’s where our social media management services come in.

Key 2026 Social Media Trends:

Short-form video rules (Reels, TikToks, Shorts)
AI tools for scheduling, engagement, and analysis
Authentic content performs better than polished promos
Community-building > vanity metrics

�� 5. Stay Educated, Stay Agile

What worked in 2020 doesn’t work now. And what works now may not work next year. That’s why Hometown Beat focuses on scalable, flexible marketing strategies that grow with your business and the market.

“In marketing, change is the only constant. Stay curious. Stay nimble. Stay local.”

✅ Final Thoughts: Let Hometown Beat Be Your Competitive Edge

Located in Carrollton, GA , Hometown Beat is more than a marketing agency—we’re your hometown growth partner. We offer:

Whether you’re a small shop in Villa Rica or a growing brand in Newnan, our team delivers results-driven marketing for every budget.

�� Ready to future-proof your business?

Get a FREE marketing audit from our team or call us today!

Top 10 Free (or Low‑Cost) Marketing Tools for Small Businesses

Mon, 20 Oct 2025 00:35:20 GMT

Top 10 Free (or Low‑Cost) Marketing Tools for Small Businesses

Running a small business means every marketing dollar counts. That’s why smart, affordable tools matter—tools you don’t need a big budget or a full tech team to master. At Hometown Beat, based in Carrollton, GA and serving the West Georgia area, we help local businesses get the most out of their marketing without overspending. Here are ten free or low‑cost marketing tools you can start using today.

1. Canva (Free & Paid)

Create compelling graphics, social posts, banners, flyers and more with ease. According to Canva, it's “one of the best free tools available” for businesses to produce visuals.

Why it matters: Good visuals drive engagement —and you don’t need an expensive designer to make them.

2. Google Analytics

This free tool tracks your website traffic, user behavior, referral sources and more.
Why it matters: You can base your marketing decisions on data—not guesswork.

3. Buffer (Free plan available)

Buffer lets small businesses schedule social posts across platforms, analyze performance, and stay consistent with very modest expense.
Why it matters: Consistent posting builds brand presence—and time savings make it cost‑effective.

4. Mailchimp (Free tier)

Great for email marketing—Mailchimp’s free plans allow up to thousands of subscribers and help you automate campaigns.
Why it matters: Email remains one of the highest‑ROI channels for reaching customers directly.

5. Google Trends

A free tool to see what people ar e searching for in your region or niche.
Why it matters: Create content your audience is actively looking for—especially helpful for local businesses in Carrollton, GA and beyond.

6. Hotjar (Free‑limited plan)

Allows heatmaps, session recordings, and user behavior tracking to identify conversion barriers.

Why it matters: Understanding how visitors behave on your site means you can fix weak spots and turn browsers into buyers.

7. HubSpot CRM & Free Marketing Tools

HubSpot offers free CRM tools and marketing features that small businesses can use to collect leads, manage relationships and send basic campaigns.
Why it matters: Keeping track of contacts and leads in one place boosts organization—and professional image.

8. Google Alerts

Free monitoring of your brand, competitors, or industry keywords—alerts delivered when your chosen terms appear online.
Why it matters: It helps you stay aware of mentions, opportunities, and manage your reputation—especially locally in West Georgia .

9. Meta (Facebook) Free Business Tools

Meta offers free tools for small businesses using Facebook, Instagram, WhatsApp, etc.—such as audience insights, post scheduling and ad previews.
Why it matters: These platforms matter for local engagement —and these tools help you use them efficiently.

10. Grammarly or Language‑Tool (Free tiers)

Quality writing matters—these tools help you avoid typos, improve clarity, and maintain professional tone in your marketing copy.
Why it matters: Polished copy builds credibility and improves how your brand appears to both users and search / AI systems.

�� Putting It All Together

The right mix of tools lets you design visuals, schedule posts, analyze traffic, engage leads, monitor your brand—and do it all on a small budget. For businesses in Carrollton, GA and across West Georgia, using these tools strategically means you’re doing big‑agency work without the big‑agency cost.

At Hometown Beat , we help local businesses pick the right tools, implement them effectively, and make sure your marketing stack supports growth and ROI—not just activity. Want help getting started? Reach out , and we’ll guide you through the setup, training, and strategy you need.

Maximize Your Business Impact at Fall Festivals with Social Media

Tue, 30 Sep 2025 21:41:07 GMT

Fall in West Georgia means cooler weather, colorful leaves, and some of the most anticipated community events of the year: Fall Festivals. These gatherings are more than just a chance to enjoy funnel cakes, hayrides, and live music—they’re a powerful opportunity for small businesses to connect with hundreds (sometimes thousands) of local families and potential customers.

At Hometown Beat , we know that Fall Festivals aren’t just about showing up—they’re about showing off. Here’s how you can maximize your business impact this season.

1. Don’t Just Attend—Announce Your Presence

Your first job is to let people know you’ll be there. Social media is your loudest megaphone. Create fun, eye-catching posts that highlight what you’ll be offering at your booth. Use festival hashtags, tag the event organizers, and add engaging visuals (like a sneak peek of your setup or a special product).

�� Pro Tip: Start your posts at least a week before the festival, then remind your audience a day or two before with a countdown-style post.

2. Make Engagement Part of the Fun

Fall Festivals are about energy and community spirit. Don’t just sit behind your booth—get interactive! Offer a photo opportunity (like a themed backdrop or props), run a small giveaway, or create a quick “spin the wheel” prize game. Then, blast these moments onto your social channels.

Encourage festival-goers to tag your business when they post their own pictures. Every tag = free visibility to all of their friends and followers.

3. Go Live and Be Real

Social media loves authenticity. While you’re at the festival, take a few minutes to go live on Facebook or Instagram. Show off your booth, interview a happy customer, or simply pan the crowd with a “look at this amazing turnout!” energy.

Even people who can’t attend in person will see your business at the center of community life—making them more likely to visit your shop later.

4. Capture the Afterglow

The festival may last a day, but your impact should stretch far beyond it. After the event, post a thank-you message with photos and highlights. Tag the event organizers again and shout out other local businesses you connected with. This positions your brand as a true community partner—not just a vendor.

Why It Works

By leaning into social media before, during, and after Fall Festivals, you transform a single booth into a brand experience. You’re not just seen—you’re remembered. You’re not just participating—you’re engaging. And when local customers see you having fun, they want to be part of that energy.

�� Bottom Line:
Fall Festivals are one of the most powerful local marketing opportunities of the year. Show up, post often, have fun, and you’ll see more customers stopping by your booth and your business long after the pumpkins are gone.

�� Ready to make your local marketing efforts work year-round? Hometown Beat is here to help . From social media management to event promotion, we’ll make sure your brand is the one everyone’s talking about at the next community gathering.

Thriving in the AI era of search: Realign, measure, collaborate

Fri, 12 Sep 2025 15:00:00 GMT

As I watched Live with Search Engine Land: SEO, AIO, GEO! on Aug. 11, which featured four top SEO experts discussing how to adapt as search shifts in the AI era, I had the strangest feeling I’d seen this movie before.

I remember feeling the same way back in 2011, when social media was shaking the foundations of search.

The industry was being forced to adapt overnight – much like Hollywood pivoting from silent films to talkies in “Singin’ in the Rain.”

The question is clear: who will thrive as AI reshapes search?

The answer lies in adapting your skills, breaking silos, mastering new metrics, and collaborating across teams.

Realign your career path with the new customer journey

Organizations can’t afford to keep cranking out silent movies if the public is flocking to talkies.

Optimizing video content and text is essential if you want to be the star of a talking picture.

Why video?

Video combines sight, sound, and motion, making it more engaging, memorable, and shareable than text.

It’s a powerful way to convey complex ideas and build emotional connections.

Research backs this up. In Think with Google, Celia Salsi tells us why video now plays a central role in how people shop and make decisions.

The challenge for brands isn’t just getting noticed, but being chosen – especially for considered purchases that require deeper trust.

YouTube plays a key role here.

It’s a top destination for shoppers researching and comparing products, with more than 35 billion hours of shopping-related content viewed last year.

Ads shown on connected TVs also drove over 1 billion conversions.

Salsi also reveals:

“There’s no one way to shop online. New research decodes multifacted shopping journeys.”

A study of 2,000 online shoppers in the U.S. identified seven distinct shopping journeys based on different needs and motivations. They are:

Impulse : Shoppers are triggered by something that catches their eye, but these journeys rarely end in a purchase.
Passion pursuit : These shoppers are already experts in a product category and will wait for the right item at the right price.
Vision to reality : Driven by style and self-expression, these shoppers use video to find products that will help them achieve a desired look.
Rookie : New to a category, these shoppers seek ideas, advice, and recommendations to help them decide.
Quest for the best : These shoppers do extensive research to feel confident in their purchase decisions.
Buy and try : Shoppers in this category compare a few options and often buy multiple items with the intention of returning some.
Quick-fire : This journey, which has the highest purchase rate, is for shoppers who need a low-cost replacement or a household staple and know exactly what they want.

Although most online shopping behavior is deliberate, Salsi reveals:

“YouTube’s influence cuts down the average online video shopper’s journey by six days.”

Get out of your comfort zone and organizational silo

You’ll also want to take a close look at “ How to get cited by AI: SEO insights from 8,000 AI citations .”

As Search Engine Land contributor James Allen reported, YouTube was the most cited domain in AI Overviews, followed by blogs like Zapier and news sites such as PCMag and Forbes, which together accounted for more than half of citations.

Websites with detailed, specialized deep content are favored over homepages, and sites known for expertise, particularly in finance and healthcare, tend to be cited, while community sites like Reddit and Quora are cited less often.

It’s time to get out of your comfort zone and organizational silo if you want to connect with people in video marketing and digital PR.

Together, you’ll need to build new teams that can balance multiple disciplines – from content and social to analytics and PR.

Measure outcomes and organic search traffic

To adapt and succeed as search changes in the AI era, you’ll need to measure outcomes alongside organic traffic.

These outcomes fall into two categories that align with video campaign goals:

Brand awareness and consideration.
Website traffic, leads, and sales.

Brand awareness and consideration

In a zero-click world, you may be tempted to use a new marketing metric called share of model.

It measures how effectively a brand is represented by large language models (LLMs) like ChatGPT .

This newfangled metric quantifies how well the LLM understands and recommends a brand, based on its identity, products, and reputation.

However, share of model may turn out to be a vanity metric, just like ranking for low-intent keywords, low-quality backlinks, impressions, gross rating points (GRPs), social media followers, and advertising value equivalency (AVE).

Instead, use a different set of metrics to measure brand awareness and consideration: the brand lift study.

Over half a dozen major social media platforms, including YouTube, Facebook, Instagram, X, TikTok, LinkedIn, and Pinterest, provide brand lift studies to advertisers who meet specific spending requirements.

These studies gauge the impact of ad campaigns on brand awareness, brand association, and consideration by surveying groups of users who were exposed to the campaign versus those who were not.

Brand lift studies measure changes in awareness, consideration, and intent – giving you hard data on how campaigns shape perception over time.

Using these metrics as key performance indicators (KPIs) makes far more sense to senior executives than betting your credibility on experimental metrics like share of model, which may not align with share of search or market share.

Website traffic, leads, and sales

For more than two decades, SEOs have tracked organic search traffic as a primary metric.

But with zero-click searches on the rise, it’s time to re-examine how you read Google Analytics 4 (GA4).

In one recent case, a marketer noticed a 28% year-over-year increase in new users.

But a closer look revealed organic traffic down 32%, while “direct” traffic had spiked nearly 47%, now making up three-quarters of all new users.

The culprit?

When their content appeared in Google’s AI Overviews, clicks weren’t passing referrer data to GA4 – making search-driven traffic look like “direct.”

Here’s how to adapt:

Pay less attention to default channel groups and more to top landing pages within “direct” traffic. If many are recent blog posts, that spike may be AI-driven.
Use campaign tagging strategically: Add parameters with Google’s Campaign URL Builder to links you share on platforms like LinkedIn. That way, you can measure the impact of your organic content and social posts even when GA4 hides the source.

Collaborate to generate successful campaigns

If you lead a marketing department or agency, you can easily subcontract skills like YouTube SEO, Reddit marketing , digital PR , or brand lift surveys.

After a few projects, you may even decide to bring some of those capabilities in-house.

If you’re an SEO manager or marketing professional, the path looks different. You might:

Start by analyzing Tubular Labs leaderboards to see which brands are winning attention across YouTube, Facebook, and Instagram.
Invest in workshops and online courses to expand your own skills – a step I’ve recommended before when talking about upskilling and reskilling .

And why push yourself to do this now?

Because as soon as this fall, senior marketers will be scanning the horizon of a shifting search industry.

Many will call for more creative collaboration, reorganize teams, or review agency relationships to realign with new customer journeys.

The SEOs and marketers who’ve already broken out of their silos – and built connections across video, PR, and social – will be ready to take the lead.

The end of the marketing funnel: Why signals are your new opportunity by Semrush Enterprise

Fri, 12 Sep 2025 11:00:00 GMT

Today’s customer journeys look less like funnels and more like works from Jackson Pollock; splashes of touchpoints across dozens of channels.

Digital transformation has “fractured consumer journeys into unpredictable, nonlinear patterns,” per Boston Consulting Group’s research , and AI is accelerating this complexity at breakneck speed.

Here’s the take: The real opportunity isn’t in controlling the journey. It’s about consistently and authentically showing up where your customers already are.

Customers don’t move in order – they move everywhere

The traditional marketing funnel assumes customers move predictably from awareness to consideration to purchase. It’s an assumption that makes presentations cleaner and attribution models simpler. But it’s also completely removed from reality.

MarTech’s analysis reveals that 73% of retail shoppers now use multiple channels throughout their journey. Not sequentially. Simultaneously.

Customers are researching on TikTok while traveling to your competitor’s store, checking G2 reviews while your salesperson is mid-pitch, and asking ChatGPT for recommendations while your landing page loads.

This behavior taps into a principle called “cognitive offloading.” When faced with complex or stepped decisions, we can fragment our research across multiple sources to minimize the load. Your customers aren’t being difficult; they’re being human.

The outcome? Increasingly diverse touchpoints for discovery and research.

Social media is becoming search

The lines between channels are increasingly blurring. Social media, traditionally a brand channel, is increasingly vital to search.

46% of Gen Z and 35% of Millennials now prefer searching on social media. This is an important deviation from search engines as social search is push-based.

Algorithms surface relevant content before users even know they need it. It’s the difference between going to the library with a specific book in mind versus walking into a store and discovering a book you didn’t know existed.

The psychological principle at play here is “peripheral route processing.” When we’re in a browsing state (like scrolling TikTok), we’re more influenced by peripheral cues like social proof, aesthetics, and emotional appeal rather than systematic evaluation of facts.

Alongside this behavior is the fact that social media channels like LinkedIn or YouTube fuel and shape the AI search visibility of brands .

These reasons together explain why a 15-second TikTok can drive more purchase decisions than a 3,000-word research study. And why we need to rethink our siloed approach to channels.

Every touchpoint builds – or breaks – trust

The average customer might encounter your brand through a Reddit comment, a TikTok video, an AI chatbot response, and a retargeted ad all in one day.

MarTech’s research describes modern channels as “layered” rather than linear. Every layer simultaneously offers an opportunity to deepen connection and a risk of losing it entirely.

The difference between success and failure is ruthless consistency.

This isn’t about having the same logo everywhere (though that helps). It’s about ensuring that whether a customer encounters you through a ChatGPT response or an X thread, the core message, values, and facts remain constant.

Consistency builds trust – and trust is the only currency that matters when you can’t control the conversation.

Consider how Apple maintains consistency across thousands of touchpoints. Whether you’re in an Apple Store, watching an iPhone review on YouTube, or asking Siri about features, the message is remarkably consistent: simplicity, premium quality, seamless integration.

They don’t control every conversation about their products, but they’ve made their narrative so consistent that it’s hard to misrepresent.

5 core signals that decode real customer behavior

While everyone else is trying to patch their broken funnel back together, the smartest brands are abandoning it entirely. They’re replacing linear thinking with something radically different: a unified signal system that treats every customer interaction as intelligence, not just another touchpoint to optimize.

The framework is deceptively simple. Instead of forcing teams to think in stages, you organize around five core signals that actually reflect how customers behave in 2025:

Search signals tell you what customers want before they know your brand exists. This isn’t just SEO data, it’s AI search queries, voice assistant requests, and the questions people ask their favored platform at 2 a.m., whether it happens on Google, Perplexity, or TikTok.
Traffic signals reveal how people actually move through digital spaces. Forget last-click attribution. This is about understanding the full journey – including the 70% of touches that happen on platforms you don’t control. It’s knowing that a Reddit thread can drive as much qualified traffic as your PPC campaign.
Behavior signals show what people do, not what they say. Cart abandonment is a behavior signal. So is spending 12 minutes on your pricing page before moving directly to a competitor. Traditional analytics miss most meaningful behaviors because they only measure what happens on your domain.
Social signals capture the conversations you’re not part of. Every subtweet, every LinkedIn comment, every Discord discussion about your category is data. As Google executive Prabhakar Raghavan put it : “40% of young people, when they’re looking for a place for lunch, they don’t go to Google Maps or Search.” Yet many companies treat social listening as a vanity metric exercise.
Brand signals measure perception velocity, or how quickly sentiment shifts, spreads, and sticks. This includes everything from branded search volume to the tone of AI-generated summaries about your company. When ChatGPT describes your competitor as “innovative” but calls you “established,” that’s a brand signal that influences future market share.

Why this shift matters

Funnel-based thinking creates organizational blindness:

Your SEO team optimizes for search without knowing what your social team sees.
Your brand team launches campaigns without understanding behavior patterns.
Everyone’s optimizing their slice while the whole pie shrinks.

Signal-based organizations see more.

When search signals show rising interest in a competitor’s feature, traffic signals confirm people are researching alternatives, behavior signals reveal they’re comparing prices, social signals indicate frustration with your product, and brand signals show sentiment declining – you don’t need six meetings to figure out you have a problem. The signals tell a unified story that demands immediate action.

The operational impact is profound , directly addressing what Adweek’s 2025 CMO Summit identified as marketing’s top pain point : “managing fragmented customer experiences.”

From chaos to clarity: Your action plan

Ready to rise above the chaos? Here’s your action plan:

Audit your AI presence: Search for your brand on ChatGPT, Perplexity, and other AI tools. What comes up? What citations are being used? Spend an hour this week understanding how AI currently represents you.
Create platform-native content: Stop repurposing. Your TikTok strategy shouldn’t be your Instagram strategy with different dimensions. Each platform has unique discovery mechanisms, audience expectations, and content formats. Respect them.
Build a single source of truth: Create a comprehensive, structured content hub that serves as the definitive source for all product information, FAQs, and brand messaging. This isn’t just for customers. It’s for your own teams and the AI systems that inform customers.
Implement cross-channel analytics: Traditional attribution is over. You need tools that can track influence across fragmented touchpoints. Look into unified marketing intelligence platforms, like Semrush Enterprise that can connect signals across search, traffic, behavior, social, and brand.
Target strategic gaps: Your social media manager needs to have visibility of SEO. Your SEO team needs to understand AI optimization. Your content team needs to be a part of all of it. Break down the silos before they break you.

The promise is straightforward:

Fewer silos because everyone’s watching the same signals.
Clearer actions because signals tell stories, not just statistics.
Faster answers because you’re not waiting for monthly reports.
Earlier risk detection because signals shift before sales do.

The future (and need for signal-based thinking) is here

The funnel assumes customers move in one direction. Signals recognize that customers move in every direction simultaneously.

The brands that will thrive will embrace leveraging each touchpoint with purpose – they won’t just have eye-catching social media feeds or top search rankings.

The strategic imperative is now to consistently appear where customers are, in whatever form they prefer, with messages that resonate in that medium.

AI search optimization? GEO? SEOs don’t agree on a name: Survey

Thu, 11 Sep 2025 20:23:00 GMT

SEOs (and their bosses) are rapidly adopting AI search optimization, even if they’re still figuring out what it all means, according to a new survey from Aleyda Solis.

By the numbers: More than 200 senior SEOs worldwide shared how they’re referring to this emerging discipline.

36% say their clients/managers simply call it “AI search optimization.”
27% stick with SEO (but for AI platforms).
18% call it generative engine optimization (GEO).
Others use terms like AEO (answer engine optimization) or even LLMO.

Yes, and… Nearly 91% said leadership asked about AI search visibility in the past year. Yes, the topic is mainstream in boardrooms.

Who’s in charge? 75% of SEOs said the SEO team runs AI search efforts.

Few reported having a dedicated AI search team or cross-functional ownership.

What’s working. Top tactics SEOs are prioritizing:

Schema and structured data.
Digital PR and citations on sources like Reddit and Wikipedia.
Content chunking and FAQs for retrieval.
Tracking AI mentions/citations across tools like ChatGPT, Perplexity, and Gemini.

What’s not working. Revenue impact is tiny – 62% reported AI search drives less than 5% of revenue.

Measurement is also not working. The top frustrations for SEOs? Lack of attribution and volatile AI answers.

Why we care. AI search barely moves the revenue needle – 62% of SEOs said it drives less than 5% of revenue. However, clients and executives are already demanding visibility reports, strategies, and budgets. This survey indicates that AI search is reshaping priorities faster than it is delivering results.

Bottom line. AI search is messy. SEOs are experimenting with various tactics, and still nobody can agree what to call this emerging marketing discipline – or who owns it.

The survey. The SEOFOMO State of AI Search Optimization Survey – 2025 Edition

Marketers aren’t ready for GEO: Survey

Thu, 11 Sep 2025 16:49:00 GMT

Most digital marketers aren’t investing resources in generative engine optimization (GEO) , even as AI-powered search looms larger. That’s according to new survey data shared exclusively with Search Engine Land by digital marketing firm Centerfield.

By the numbers . According to Centerfield’s survey:

63% of marketers said their company isn’t investing time, budget, or staff in GEO.
9% of marketing resources are allocated to GEO on average, compared with 24% for SEO and 36% for social campaigns.
33% of marketers said they have a good or expert understanding of GEO, versus 72% for SEO.
41% expected their company to dedicate more GEO resources in the next year.

Why we care. As we move toward a world where AI-generated search summaries are the norm, brands risk falling behind if they don’t adapt their strategies to be visible (via mentions/citations) in new answer-driven formats.

Dig deeper. Ranking in Google doesn’t guarantee visibility in ChatGPT: Study

Other findings . Nearly all marketers (93%) said they are facing barriers to GEO implementation:

40% cited budget; 36% said limited expertise or competing priorities.
Only a third feel highly confident in telling whether traffic came from AI search versus traditional search.
Still, 67% believe AI search visibility will be “very or extremely important” within two years.

What’s next . Most teams want education and playbooks. Half say they need basic training on AI search. Others are calling for best practices and tools to measure impact.

About the survey. The Centerfield Gen-AI Search Readiness Survey was an online poll of 878 U.S. marketers conducted in August, with a ±3.3% margin of error at the 95% confidence level. Respondents represented a broad mix of ages, genders, ethnic backgrounds, industries, and company sizes, and all held responsibility for at least one key marketing function.

Optimizing for AI: How search engines power ChatGPT, Gemini and more

Thu, 11 Sep 2025 13:00:00 GMT

How do you optimize to have content featured in AI?

Whether it’s ChatGPT, Google Gemini, Meta’s LLaMa, Grok, Microsoft’s Claude, or Perplexity AI, the challenge is understanding how these systems access, consume, and deliver answers from live documents on the web.

If you follow digital marketing and SEO conversations on social media, you’ll know how quickly AI optimization advice has surged in popularity.

Google Trends shows interest rising steadily over the past couple of years – now spiking sharply.

The term “AI SEO” appears to be winning the naming-convention debate for this niche, with “ generative engine optimization ” also gaining traction.

The rise of AI optimization

When generative AI burst on the scene, it was quickly followed by conjecture about the death of SEO .

But such claims rest on a naive view of how generative AI works, shaped by science fiction portrayals.

In those stories, AI is an intelligent agent that produces answers on its own or flawlessly curates the entirety of human knowledge.

In reality, AI is fed on information, and one of its primary contemporary sources is the web and the websites that compose it.

Simply put, SEO feeds AI.

Google Gemini as a case study

One could argue that optimization unique to AI is unnecessary or redundant, since in some cases – such as Google’s Gemini – AI already pulls content and answers from the top-ranking webpages for a query.

Google co-founder Sergey Brin describes this as the AI retrieving the top thousand search results, then running follow-on searches to refine and analyze them.

This is exactly how part of Google’s AI works.

For instance, if I search in Google AI Mode for something that needs fresh information, such as “what are the current-most promising experimental treatments for melanoma?”, the system follows a multi-step process:

First, a broad search gathers a wide range of information.

Then, it narrows down to the most relevant and reliable sources to construct an answer.

Submitting that phrase to Google Gemini – the AI powering Google’s AI Mode results – triggers a rapid countdown of hundreds of sites identified behind the scenes as relevant to the query.

From broad to refined queries

The initial broad queries were identified for a range of potential treatments and medical studies, including:

“Experimental treatments for melanoma.”
“Novel therapies melanoma.”
“Melanoma clinical trials promising.”
“Future melanoma treatments.”

Does this not seem like the sorts of keyword phrases one would uncover if conducting keyword research around the initial search query about promising experimental treatments for melanoma?

It definitely is – Google’s AI Mode is initially conducting an identification of closely related search phrases.

After it came up with these multiple, broad queries, Google’s AI Mode conducts searches with each of them, just as a human researcher seeking information might do.

AI Mode is using Google Search itself to conduct this query.

The webpages coming up high in the search results for each of these queries are the sources for the information it analyzes before answering the submitted question.

From those broad results, Gemini conducted more refined queries based on lines of inquiry it developed from the first stage of results.

These refinement queries included:

“TIL therapy melanoma clinical trials.”
“Oncolytic virus melanoma treatment.”
“BiTEs melanoma.”
“mRNA vaccine melanoma.”
“Lifileucel melanoma.”
“T-VEC melanoma.”
“Tebentafusp melanoma uveal.”
“V940 melanoma clinical trial.”

It conducts further queries indicated by the research and pulls back webpages based on those as well.

This process, which they refer to as “fanning out,” identifies a tree of related queries to identify all the sources of information it considers in answering queries.

Dig deeper: Chunks, passages and micro-answer engine optimization wins in Google AI Mode

Authority and reliability in AI answers

Of the webpages it reviews, either through the search process or after obtaining all relevant pages, it then identifies reliable and authoritative sources based on E-E-A-T criteria.

For this sort of query, Google will likely consider it in the important category of Your Money or Your Life (YMYL).

It is unsurprising that Gemini used the websites of:

Reputable medical institutions (e.g., universities, cancer centers).
Government health agencies (e.g., NIH, FDA).
Organizations dedicated to melanoma research and advocacy.
Peer-reviewed scientific publications.

Right now, with Google’s AI Mode, you really don’t need to change a thing. Just keep doing what you’d normally do to appear in Google’s search results.

Performing SEO for Google is automatically performing optimization for Google’s AI.

(There are a few caveats to this, which I can explain shortly.)

Where different AI models get their data

But what about the other AI models? Where do they get their web data?

A survey of platforms suggests how one needs to optimize for AIs and where information may need to appear to feed them. It is important to note that there are two different things going on:

Training data for large language models, which typically differs from fresh or current web search results, although there are obviously overlaps. Multiple major AIs were trained on data obtained from Common Crawl , a free source of web data that has been digested and stored in a format that is relatively easy for AIs to access, consume, and use.
Sources of current or “live” web information, which are not necessarily the same as training data. Training sets could be a few years old, while contemporary web data may include information relevant to queries like “what are the current specials at Acme Restaurant?” or “have tickets for the Taylor Swift concert gone on sale?”

A recent revelation is that while some AIs were unable to obtain licenses for Google search results, they appear to be circumventing that by using SerpApi, a company that specializes in scraping Google SERPs.

Both OpenAI and Perplexity are reportedly making limited use of Google results through this indirect access.

Why SEO still matters in the AI era

As you can see, many of the top AI chatbot and search systems are likely to reflect content that already ranks well in Google and Bing.

This supports the hypothesis that getting content surfaced in Google AI Mode, ChatGPT, Meta LLaMa, and Microsoft Copilot still depends on the established SEO methods long used to optimize for those search engines.

Google personnel have confirmed this , emphasizing the same high-level approaches they have advocated in recent years:

Focus on unique, valuable content for people.
Provide a great page experience.
Ensure Googlebot can access your content.
Manage visibility with preview controls (nosnippet and max-snippet).
Make sure structured data matches the visible content.
Go beyond text for multimodal success.
Understand the full value of your visits.
Evolve with your users.

For YMYL queries, your website also needs to convey trust, authority, and experience in the topic matter.

There has also been emphasis on structured data in AI optimization.

Google’s article “ Top ways to ensure your content performs well in Google’s AI experiences on Search ” reiterates that structured data should match visible content, a long-standing best practice.

What is notable is their added stress on validation, with the clear takeaway to use Google’s Rich Results Test and Schema Markup Validator to ensure pages have implemented markup properly.

Dig deeper: Answer engine optimization: 6 AI models you should optimize for

Caveats and complications

There are additional wrinkles depending on what you are trying to optimize.

As the melanoma treatment query example shows, you may need to optimize to appear for many search queries, not just one.

Other AI systems, like Gemini, are also likely to fan out to closely related queries when composing answers, so optimization may require targeting multiple queries rather than a single primary keyword phrase.

This creates a dilemma for publishers, since past Google updates have penalized thin pages built to target many keyword variations.

While I have not thoroughly researched this, there is some cause to think that broad content strategies covering many similar variations could be rewarded by AI while harming organic keyword rankings.

If considering such an approach, one should invest in specific, sophisticated, rich content for each variation, not content produced cheaply through automation or generative AI.

Where online reputation management is concerned, negative material in the top search results can be highlighted and even amplified in AI responses.

This is especially the case when multiple sources reiterate the same claims, which AI may present without important context.

For example:

A news article states, “John Smith was accused of impropriety by Jane Jones, and he later sued her for defamation and prevailed.”
AI might summarize it as, “Some sources have represented that John Smith may be guilty of impropriety.”

For reputation management, this means publishing multiple pieces of content across different domains to crowd out damaging material. It may even be rewarded by AIs that summarize top results.

Emerging gaps and unknowns

While optimization techniques and elements for Google and Bing are well-established, optimizing for some of the other search engines in the above chart is not so well-documented.

PerplexityBot

There are no known guidelines for optimizing for PerplexityBot.

One can assume that having solid technical SEO for a website will facilitate its content being crawled and absorbed by PerplexityBot.

Optimization beyond that is unknown.

Grok WebSearch

There are currently no known methods for optimizing for Grok WebSearch.

However, if you want a new website or webpage indexed for Grok, you should likely promote the URLs on X by posting them there as a signal for their bots to crawl and index the content.

Brave Search

This search engine does not have published optimization methods, but it states that much of its content discovery is through users’ web activities detected via the Brave browser.

Installing Brave would be a way to get the latest content added to the Brave Search engine.

One must opt in to sharing browsing data with Brave in order for one’s website visit activity to be submitted to them for indexation.

This feels pretty limiting and outdated, rather like submitting websites to search engines 20+ years ago.

Practical takeaways for search marketers

There may be more guidance on the horizon from generative AI providers, as companies and site owners continue to ask how they can get their content presented by AI – and why it may not be.

It has been said before, but it is worth reiterating that optimization strategies and content marketing should not be fueled purely by AI-generated content.

Each generative AI model has some vested interest in avoiding recursively created material because of the likelihood of error propagation and quality erosion.

When using AI in marketing and content creation, the long-term guidance is to focus deeply on quality and incorporate AI with a light hand, together with human oversight and QA.

Otherwise, producing large volumes of content cheaply with AI is likely to be a short-term tactic that is not sustainable, as many black-hat SEO strategies have already shown.

SEO is fuel for AI

Reassure yourself that “SEO is not dead.”

Each AI platform still needs to conduct searches among the trillions of webpages and other online content to deliver information.

The tried-and-true SEO techniques will remain necessary for the foreseeable future.

Even prior to the generative AI revolution, Google and other search engines were incorporating machine learning processes into their ranking algorithms.

Machine learning will likely continue to transform keyword search functions.

There is already a strong likelihood that Google and others are using models that create customized ranking methods and factor weightings by topic and query, as I have described in past work on Google’s quality scoring methods .

SEO is the fuel for the AI chatbots.

Each of them needs the content found among the trillions of webpages, with search engines working behind the scenes to help locate and organize it.

For webpages to be findable – and to rank well enough in the search engines feeding AI – SEO remains the essential connection between web content and the information ultimately delivered to users.

Ranking in Google doesn’t guarantee visibility in ChatGPT: Study

Wed, 10 Sep 2025 17:17:00 GMT

Dominating Google’s first page doesn’t mean you’ll be mentioned in ChatGPT. A new study conducted by Chatoptic of 15 brands across five categories found almost no correlation between SEO strength and visibility in AI answers.

Why we care. Many SEOs have argued that SEO success will carry over to generative AI platforms like ChatGPT and that SEO tactics are enough. This study suggests that GEO requires its own playbook. SEO alone isn’t enough to secure mentions in AI answers.

By the numbers. Brands ranking on Google’s first page were mentioned in ChatGPT just 62% of the time.

0.034 correlation: Even when brands appeared in both, their Google rank barely predicted their ChatGPT position.
+1% boost: Turning on browsing in ChatGPT increased alignment with Google by just 1%.
High overlap: Coursera (86–87%) and GoDaddy (83%).
Low overlap: Hostinger (32–34%) and edX (47–48%).
By category: Online courses aligned the most (65%); hotel booking the least (58%).
Intent didn’t matter: Exploratory, feature-based, and brand-seeking queries all hovered at ~61%-63% overlap.

Bottom line. Search and AI aren’t completely interchangeable. Winning in Google doesn’t guarantee visibility in ChatGPT – and that gap is where GEO is taking shape.

The study. SEO ≠ GEO: Only 62% Overlap Between Google Ranking and ChatGPT Visibility (Study)

Measuring GEO: What’s trackable now and what’s still missing

Wed, 10 Sep 2025 12:00:00 GMT

Large language models (LLMs) are transforming how consumers discover brands and find answers to both simple and complex questions.

For marketers, this shift demands new ways of measuring visibility and impact.

Yet unlike Google search, generative engines reveal far less data to guide strategy.

This article outlines the GEO metrics you can track right now and the blind spots that still make optimization a challenge.

GEO metrics you can measure right now

Though the GEO landscape is still evolving, several core metrics already help track performance and guide optimization.

AI mentions and citation rate

This is the most foundational GEO metric.

Unlike traditional SEO , which aims for a high ranking, the goal of GEO is to be cited as a source within a generative response.

Tools and analytics platforms are rapidly emerging to track when a generative engine, such as Google AI Overview, mentions your brand or links to your content.

This metric shows whether your GEO efforts are working and whether the engine is recognizing your content as credible .

A high citation rate is the new equivalent of a Position 1 ranking.

Here is an example of mentions vs. total “presence score.”

The point is that being mentioned is only one factor.

You also need accuracy, positive sentiment, and other key metrics (outlined below) for a well-rounded view of your GEO presence.

Here’s an example from our reporting on different links.

Focusing on where LLMs direct traffic helps reveal where to start building an off-site content strategy.

Referral traffic from generative engines

While generative engines aim to provide “zero-click” answers, they often link to their sources.

Tracking this referral traffic is a critical metric. It shows the direct value – in terms of website visits – your GEO strategy generates.

By segmenting traffic in your analytics platform, you can see which engines drive the most users and double down on the content delivering returns.

We’ve built dashboards to help customers compare these metrics with other inbound sources – especially useful for brands still grasping the impact of LLMs on their business.

Share of voice in AI responses

This metric goes beyond citation count, measuring the frequency and prominence of your brand in AI-generated responses for target queries.

For instance, a hotel brand would want to know how often it appears when users ask, “What are the best hotels in Chicago?”

A high share of voice shows that your content is consistently chosen as a primary source.

This is a clear sign of success in a world where brands must be part of the answer, not just a link in a list.

Content prominence and location in response

Generative engines often structure answers with key points, summaries, or lists.

Where your content appears within this matters. Are you the first source cited, or buried at the bottom?

Tracking position and prominence offers a more nuanced view of success, signaling the engine’s perception of your authority and relevance.

Dig deeper: What’s next for SEO in the generative AI era

The most elusive metric: Search or prompt volume

In traditional SEO, search volume is a cornerstone metric.

Tools like Google Keyword Planner, Semrush, and Ahrefs draw on vast query databases to estimate how many people search specific keywords each month.

This data underpins keyword research and content strategy, letting you prioritize topics by demand.

That model doesn’t translate to generative engines for several reasons:

Closed ecosystems

Generative engines like ChatGPT, Gemini, and Perplexity operate as “black boxes.”

Google still provides keyword data for search, but these platforms don’t offer public APIs that share query volumes.

What users ask remains proprietary and internal.

Conversational queries

Prompts aren’t simple keywords.

Instead of “best pizza New York,” users might ask, “What are the best pizza places in New York that are open late and have outdoor seating near Times Square?”

The variety and length of prompts make them impossible to categorize or count like traditional keywords.

Other ‘missing’ metrics to help understand GEO results

Beyond search/prompt volume, some of the most valuable insights remain out of reach.

Two stand out as especially critical for shaping GEO strategy:

The ‘why’ behind a citation

We can see when a generative engine cites content, but not why.

Was it a specific phrase, a unique data point, or the combination of structured data and overall authority?

Because LLMs are opaque neural networks, their decision-making is hard to reverse-engineer.

Without that visibility, it’s difficult to replicate success.

Unlocking the “why” would enable far more precise optimization.

Attribution in multi-source synthesis

Generative engines often blend information from multiple sources into one answer.

It’s nearly impossible to measure each source’s weight or contribution.

If your statistic is used alongside a competitor’s narrative, who gets credit?

The lack of granular attribution makes it hard to assign value and justify GEO investment, limiting the development of more advanced attribution models.

Dig deeper: 12 new KPIs for the generative AI search era

The next frontier of search optimization

The current state of GEO metrics is a tale of two realities.

We have a solid foundation of measurable signals – citations, referral traffic, share of voice, and content prominence – that confirm our content’s visibility and influence in generative search.

These provide valuable insights into current performance and help inform strategy.

At the same time, deeper layers of insight remain elusive.

We cannot see into generative engines to understand why content is cited, nor can we accurately attribute our contribution when multiple sources are synthesized.

These blind spots make it difficult to replicate success and justify investment.

The next chapter of GEO will belong to strategists who master the metrics available today while recognizing that the true value lies in unlocking the elusive ones that will define the future of optimization.

GEO and SEO: Convergence, divergence or something in between

Mon, 08 Sep 2025 13:00:00 GMT

Over the past two years, AI and LLMs have stepped out of the shadows and into public consciousness.

Not long ago, most people’s understanding of AI was limited to sci-fi references or the occasional nightmare about machines taking over.

Now, everyone seems to be talking about it – and naturally, marketers have jumped right in.

We did it with search engines. We tried it with social media.

And now we’re doing it again with LLMs, using emerging technology and growing public interest to promote our brands.

Conferences are dedicated to finding the silver bullet of AI optimization . LinkedIn is overflowing with think pieces.

We’ve nearly agreed on an acronym. And marketing job postings already list it as a must-have skill.

But what about the future of “ GEO ”?

Will it grow into a critical channel with its own specialists, or will it settle into a niche within SEO like so many other skill sets before it?

Why GEO may become its own marketing discipline

There are reasons why we might expect GEO to develop into its own marketing discipline.

We make it a thing

The core reason GEO could become its own specialization is if we, as an industry, make it so.

There is already significant discussion around GEO within the SEO community, from trying to pick the right acronym to defending its importance among other marketing channels.

One way it may become its own discipline is through sheer force of will.

The more we label it as distinct from SEO, the more it will be seen as a standalone channel.

Dig deeper: Yes, GEO is happening

LLMs evolve beyond Q&A

We may also see a major shift in how LLMs operate.

Right now, GEO feels very similar to SEO at a strategic level, with differences mainly in tactics.

Both focus on optimizing for discovery across platforms.

But if LLMs evolve beyond the current question-and-answer model – or if agentic AI becomes the real focus of GEO – our tactics may need to change significantly, requiring an entirely new strategic approach.

At that point, the platforms themselves would have diverged enough for GEO to be treated as its own discipline.

GEO shifts toward partnerships

Another reason GEO might separate from SEO is if it becomes less about optimizing for algorithms and more about partnerships with LLM providers.

For example, Perplexity AI has formed partnerships with media companies , using their data sets in its knowledge base so customers can interact with participating brands directly through answers.

If this trend grows, optimizing for visibility may look less like SEO and more like affiliate or partnership marketing.

SEO rebrands as GEO

Finally, GEO could surpass SEO in overall value.

If LLMs become the primary source of discovery, GEO skills will be in higher demand.

In that case, many SEO specialists may simply rebrand themselves as GEO specialists.

Rather than being just a niche channel, GEO could become the default search discovery channel.

Dig deeper: SEO vs. GEO: What’s different? What’s the same?

Why GEO might not separate from SEO

While there are many reasons why GEO could become its own specialization, there are also important reasons why it may not.

Skills stay too close to SEO

Right now, the skills needed to optimize for visibility in LLMs are very similar to those of SEO.

The main difference lies in understanding how LLMs function and how people use them.

But these aren’t fundamentally new skills.

SEOs already spend hours:

Studying algorithm changes.
Analyzing how they affect results.
Understanding how audiences engage with search engines.

The platforms may operate differently at a technical level, and their inner workings aren’t as straightforward as some assume.

Still, the expertise required to succeed with LLMs overlaps heavily with SEO.

Niche focus adds little value

Another reason GEO might not split from SEO is if there simply isn’t enough revenue or measurable value to justify a dedicated specialization.

Right now, LLMs drive very little traffic to websites, and the true impact of visibility in these platforms is difficult to track.

Unless GEO begins to replace the value currently generated by SEO, the case for a standalone focus remains weak.

Paid models in LLMs will fold into PPC

LLMs will likely be further monetized, most likely through paid partnerships or pay-per-click ( PPC ) and pay-per-impression models.

If that happens, these opportunities will fit neatly within the existing skills of digital marketers.

Paid GEO would simply become part of established advertising practices, not an entirely new discipline.

Similarly, there’s no real reason to expect a need for specialists in organic LLM optimization.

Dig deeper: Will GEO replace SEO – or become part of it?

What I think is most likely

Honestly, I think the most likely outcome lies somewhere in between.

GEO becomes a sub-specialty

Most plausibly, GEO will end up as a sub-specialty rather than a standalone discipline.

Think of local SEO or YouTube SEO . These aren’t fields that someone practices exclusively.

Instead, they tend to be areas of focus for people who enjoy the nuances or have gained significant experience in them, but they’re rarely the sole focus of a role.

It’s uncommon to see a job posting for a “YouTube optimizer” with no other requirements.

Even roles that specify “local SEO” typically expect strong generalist SEO knowledge alongside it.

Similarly, it’s hard to imagine a company hiring one person to handle SEO and another to handle only GEO.

The overlap is just too great.

GEO and SEO converge into search optimization

More likely, once the current hype around AI optimization fades, search professionals will fold GEO into their existing responsibilities.

Right now, many people are positioning themselves as GEO experts, often trying to stake out something revolutionary (and sometimes deliberately contrary) to gain attention.

But this isn’t new.

Not long ago, the buzz was around TikTok as the “next frontier” of SEO.

LinkedIn was full of think pieces about social media becoming Gen Z’s search platform.

That didn’t materialize in any meaningful way.

When the next shiny thing arrives, GEO and SEO will likely become two sides of the same coin – simply part of a broader practice of search optimization.

Dig deeper: GEO and SEO: How to invest your time and efforts wisely

What AI means for paid media, user behavior, and brand visibility

Mon, 08 Sep 2025 12:00:00 GMT

AI isn’t just tweaking paid media – it’s transforming it.

What was once background automation is now driving entire campaign types and reshaping how users discover brands.

From incremental optimization to transformation

AI has, in some ways, always been part of paid media.

Smart Bidding , responsive search ads , and other machine learning tools quietly optimized bids and creative combinations in the background.

Today, that role has expanded dramatically.

The biggest change, though, isn’t just new campaign types. It’s the pace and depth of impact.

We’re no longer talking about small, behind-the-scenes optimizations. Instead, we’re experiencing a dual transformation:

A rapid expansion of tactical opportunities for advertisers, enabling smarter and faster campaign management.
A fundamental shift in user behavior, where consumers increasingly turn to AI-based tools to inform decisions, changing how and where ads are seen.

This discussion explores three layers:

How AI is reshaping the tactical side of paid media.
How user behavior is shifting.
How these forces together are influencing marketing as a whole.

The goal isn’t a fixed playbook – things are moving too fast – but rather thought-starters on how to adapt with clarity and focus.

A tactical evolution

AI is now deeply embedded in the foundations of paid media.

AI-driven campaign types

AI is no longer just optimizing in the background.

It now powers entire campaign formats across major platforms, including:

Google’s Performance Max and AI Max .
Meta’s Advantage+ .
TikTok ’s Smart Performance Campaign.
LinkedIn ’s dynamic creative optimization.

These formats reduce manual setup while maximizing algorithm-driven testing and asset delivery across placements.

Smarter budgeting and targeting

Bid and budget tools now adjust in real time to countless micro-signals – location, device, browsing intent, and more – that humans simply can’t manage at scale.

Audience targeting has also leapt forward.

Predictive models go beyond demographics and lookalikes.

They identify new user segments that marketers may never have considered, unlocking faster paths to scale.

Automation at scale

AI is increasingly handling repetitive, time-consuming tasks.

AI agents now handle work that once demanded hours each week:

Analyzing search queries.
Detecting CPC trends.
Flagging anomalies within minutes.

The expanding role of the practitioner

This automation doesn’t eliminate the need for tactical expertise, but it shifts the focus.

PPC practitioners who once spent their time adjusting bids or pulling reports can now devote more energy to strategy, positioning, and cross-channel measurement.

To thrive, we must think more like CMOs – balancing channel execution with an understanding of brand, creative, and customer experience.

The edge goes to those who learn to work with AI rather than resist it.

Dig deeper: Top AI tools and tactics you should be using in PPC

The bigger picture: Paid media user behavior is shifting

Platform innovations are significant, but the deeper disruption lies in how users interact with information.

AI Overviews in Google SERPs are taking up more space, pushing paid and organic listings farther down the page. This changes click patterns, reduces visibility, and forces advertisers to adapt to a more competitive landscape.
LLMs like ChatGPT and Gemini are increasingly serving as research tools. Instead of typing a query into Google, users are asking conversational AI for solutions and recommendations – behavior that reduces traditional ad interactions at the top of the funnel.
With fewer ad spots available, competition for inventory is intensifying, and CPCs are rising . Advertisers must balance efficiency with visibility, often broadening channel mix and strengthening brand presence beyond search alone.

This isn’t just a tactical challenge. It reframes how we define visibility in paid media.

To stay competitive, brands must be discoverable across the AI-driven journey, not just within a single channel.

Key shifts to embrace

These shifts aren’t just tactical tweaks.

They require rethinking how campaigns are designed, measured, and connected across the journey.

Test relentlessly: AI-driven formats evolve quickly, and platforms often reward early adopters. Waiting too long can mean higher costs and missed opportunities.
Craft cohesive cross-channel campaigns: No channel exists in isolation. Campaign objectives should ladder up to shared brand goals, with each platform amplifying the others.
Invest in brand: Programmatic and social campaigns build awareness, social proof, and credibility early. When users eventually ask ChatGPT for “the best solution,” your brand is already part of their shortlist.
Rethink attribution: LLM-driven journeys collapse the funnel. A consumer might:
- Ask ChatGPT for recommendations.
- Cross-reference with Google.
- Check social proof on Instagram.
- Convert via a retargeting ad.

Not every touchpoint will be measurable in the way we’re used to – but every touchpoint matters.

Zoom out: The funnel is flattening

The traditional marketing funnel no longer reflects reality – and neither does the flywheel.

Today’s buying journey is fragmented and conversational. A user might start with a broad search query, refine their thinking with a ChatGPT recommendation, validate that suggestion on TikTok or through peer reviews, and only convert later through a retargeting ad.

New research from Sprout Social reflects this reality:

11% of Gen Z reported using chat-based AI as their first stop for gathering information.
41% said social media was their go-to channel.

Search engines still matter, but they’re no longer the single gateway to discovery.

Many of these touchpoints leave no trace in traditional analytics.

That makes attribution fuzzier, but it also highlights the need to design for the full experience, not a linear funnel.

The takeaways are clear:

Build experiences, not rigid journeys.
Anticipate and answer questions before your audience asks them.
Ensure brand visibility across every possible interaction, even those that don’t directly convert.

Going beyond paid media

Perhaps the most important recent shift is the collapse of silos between paid, organic, and content.

Search illustrates this clearly.

AI Overviews may reduce paid search traffic, making SEO and content even more critical.

Brands must proactively monitor how they appear in AI-generated results and adjust strategies accordingly.

Queries surfaced by LLMs should be shared across paid, SEO, and content teams. This enables teams to:

Refine keyword strategies and creative messaging.
Adjust audience targeting.
Shape content strategies that anticipate the questions users are asking before they reach paid channels.

Paid campaigns can amplify educational or thought leadership content, ensuring your brand is visible long before a query is typed or an AI prompt is submitted.

Collaboration can be creative and iterative:

Analyze organic LLM queries to improve paid ad copy and targeting.
Review paid ad performance to identify content (thought leadership pieces, downloads, videos, etc.) with high engagement but low conversion.
Feed these insights to content and SEO teams so they can produce new assets that answer questions blocking conversions.

This type of collaboration builds a holistic, integrated marketing approach.

Channels may blur, but the brand story remains consistent, and insights from one channel reinforce performance across others.

Key takeaways

Craft experiences, not funnels. The buyer journey is conversational and unpredictable – marketing should reflect that.
Invest in cross-channel cohesion. Paid, SEO, and content strategies should reinforce one another and share insights.
Prioritize brand strength. Build awareness and authority before demand exists so your brand is already credible when AI surfaces answers.
Lean into testing and adaptation. Marketers who evolve with AI will thrive; those who resist risk falling behind.

The AI advantage in paid media

AI’s impact on paid media – and marketing more broadly – is deep, multifaceted, and accelerating:

Driving new tactical efficiencies.
Reshaping user behavior.
Redefining what it means for a brand to be visible and credible.

And this evolution will continue in ways we can’t fully predict.

The marketers who thrive in this new era will be those who understand AI as both a tool for streamlining operations and a force for scaling creativity and impact.

By embracing adaptability, integrating insights across channels, and prioritizing brand-first thinking, you can survive and flourish in a landscape where AI is no longer optional – it’s foundational.

Dig deeper: Scaling PPC with AI automation: Scripts, data, and custom tools

SEO in the age of AI: Becoming the trusted answer

Fri, 05 Sep 2025 13:00:00 GMT

For years, I’ve updated my working definition of SEO to capture how the discipline has evolved.

Here’s how those definitions have changed over time:

1998 to 2023
- SEO is “the art and science of persuading search engines such as Google, Bing, and Yahoo to recommend your content as the best solution to a user’s problem.”
By 2023
- As the landscape expanded, SEO became “the art and science of persuading recommendation engines – including Google, Bing, ChatGPT, Perplexity, Siri, Alexa, and Copilot – to present your solution as the best in the market.”
In 2025
- The definition has become simpler – and broader. Call it search engine optimization, generative engine optimization ( GEO ), answer engine optimization, or AI assistive engine optimization. Today, they have all converged into one unified discipline:
  - “The art and science of engineering a brand’s entire digital ecosystem to educate AI assistive engines, ensuring the brand becomes their most trusted, logical, and go-to answer at every stage of the conversational acquisition funnel.”

This redefinition raises an essential question: if SEO has converged into educating AI assistive engines, where should we look to understand how these systems learn?

Why Google still sets the blueprint for the AI era

Although Google may seem to be losing ground in online search and research, it remains the best ecosystem for understanding where AI assistive engines are heading.

Why?

Because it’s the only major player with all three pillars of the algorithmic trinity:

A dynamic web index from traditional search.

A factual knowledge graph .
A large language model (LLM) that can communicate.

Our role is to create clarity.

Think of AI as a child – eager to please, but easily confused.

It learns from your digital footprint and forms its answers, recommendations, and suggestions through three lenses:

What’s current (search results).
What’s factual (the knowledge graph).
What’s conversational (the LLM).

The curriculum to teach is threefold: conversation, knowledge, and up-to-date information.

Dig deeper: Generative AI is changing search, but Google is still where people start: Study

How to educate AI

To succeed in this new paradigm, brands need a structured, repeatable methodology.

Traditional, channel-specific tactics won’t hold up because AI learns from the entire digital ecosystem.

This methodology rests on three sequential pillars:

Understandability : Machines must clearly and unambiguously grasp who the brand is, what it does, and who it serves. This is the foundation.
Credibility : The brand must prove it is the best solution by demonstrating notability, expertise, authority, trustworthiness, and transparency across the ecosystem.
Deliverability : The brand’s message must appear with the right information, in the right format, at the right time, wherever its audience is active.

Together, these three phases build a consistent digital presence that:

Educates the algorithmic trinity.
Fosters long-term algorithmic trust.
Positions the brand at the top of the algorithmic mind.

The conversational acquisition funnel: Your new curriculum

Every day, AI assistive engines like Google AI Mode, ChatGPT, Perplexity, and Copilot have trillions of niche conversations with billions of people who trust them.

These engines now cover every stage of the Conversational Acquisition Funnel – explicit, implicit, and ambient research – and every prospect will engage with their version of your funnel at some point.

Your job is to win at every stage by being top of algorithmic mind, whichever engine your ideal prospect prefers.

Top of the funnel: Clarity makes you the answer

At the awareness stage, how does a new client discover you?

Google’s recent cleanup of ambiguous thing entities shows that AI assistive engines are unlikely to introduce a vaguely defined concept into a specific conversation. They prefer a recognized expert.

To become the clear answer here, you must provide relevant content in the right format and be the trusted entity on a specific topic (topical authority).

When AI is confident in your expertise and you feed it the content it needs, it will prioritize your solution – and advocate for you at the top of the funnel.

Awareness is the deliverability phase.

Middle of the funnel: Clarity makes you the credible choice

At the consideration stage, Google’s focus on unifying person entities is telling.

When a user is evaluating options, AI is asking:

Who is speaking?
Why should I trust them?

An entity with a confused or multi-typed classification is at a disadvantage. It lacks the clear authority to be presented as a trustworthy option.

To earn the recommendation here, AI must have absolute confidence in your credibility, earned through a clearly defined entity and reinforced by undeniable N-E-E-A-T-T signals.

Consideration is the credibility phase.

Bottom of the funnel: Clarity closes the deal

At the decision stage, the stakes are highest – the perfect (money) click.

When a potential client asks, “Who should I choose?” you need to be firmly established as the ultimate choice.

To be the trusted choice at this stage, your brand must be factually and consistently defined across the web.

That clarity allows AI to confidently present you as the solution – reinforced with a trust-building summary and a direct link to the money page.

Decision is the understandability phase.

The new job description for SEOs: Educators

Google’s June 2025 Knowledge Graph update was a call to arms for clarity – and it changes our job description.

Our role is no longer about persuading algorithms with tactics, but about fundamentally educating them.

This is the greatest opportunity since Google won the search engine wars, when we could finally focus on one engine instead of half a dozen.

Today, brands that embrace their role as teachers will build a deep, resilient moat of algorithmic trust.

Those brands will stay top of the algorithmic mind – and the ones AI engines confidently recommend again and again.

Google: Your proving ground for AI success

The role of brands as educators is clear.

But understanding the theory isn’t enough – you need a way to measure whether your digital ecosystem is truly teaching AI what you want it to learn.

This is where Google, even in 2025, remains your proving ground. As mentioned, it is the only ecosystem where you can establish reliable KPIs for each pillar of the algorithmic trinity.

You can see and measure your presence in all three branches:

The traditional search index.
The Knowledge Graph.
The LLM via AI Overviews.

This unique window of insight is closing fast. The traditional search results page is giving way to conversational, AI-driven interfaces.

Now is your only chance to use Google’s ecosystem to measure and strengthen your standing while visibility is still present.

My advice? Intentionally and systematically engineer your digital presence across all three pillars today.

Do that, and you’ll win measurable results now while also positioning yourself to lead when assistive engines become the default interface.

Remember: this is a one-time opportunity.

In a few years, AI platforms will turn research into endless private conversations, erasing any meaningful way to measure performance.

Think of the evolving AI knowledge base as an algorithmic blockchain: the understandability and credibility you establish now are your brand’s genesis block.

The KPIs you lock in at this stage will form a permanent, trusted record – one your competitors will find exponentially harder to displace later.

How generative engines define and rank trustworthy content

Fri, 05 Sep 2025 12:00:00 GMT

Generative AI has quickly shifted from experimental novelty to everyday utility – and with that shift comes growing scrutiny.

One of the most pressing questions is how these systems decide which content to trust and elevate, and which to ignore.

The concern is real: a Columbia University study found that in 200 tests across top AI search engines like ChatGPT, Perplexity, and Gemini, more than 60% of outputs lacked accurate citations.

Meanwhile, the rise of advanced “reasoning” models has only intensified the problem, with reports of AI hallucinations increasing .

As credibility challenges mount, engines are under pressure to prove they can consistently surface reliable information.

For publishers and marketers, that raises a critical question:

What exactly do generative engines consider trustworthy content, and how do they rank it?

This article unpacks:

The signals generative engines use to assess credibility – accuracy, authority, transparency, and freshness.
How those signals shape ranking decisions today and in the future.

What is trustworthy content?

Generative systems reduce a complex idea – trust – to technical criteria.

Observable signals like citation frequency, domain reputation, and content freshness act as proxies for the qualities people typically associate with credible information.

The long-standing SEO framework of E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) still applies.

But now, those traits are being approximated algorithmically as engines decide what qualifies as trustworthy at scale.

In practice, this means engines elevate a familiar set of qualities that have long defined reliable content – the same traits marketers and publishers have focused on for years.

Characteristics of trustworthy content

AI engines today are looking to replicate familiar markers of credibility across four traits:

Accuracy: Content that reflects verifiable facts, supported by evidence or data, and avoids unsubstantiated claims.
Authority: Information that comes from recognized institutions, established publishers, or individuals with demonstrated expertise in the subject.
Transparency: Sources that are clearly identified, with proper attribution and context, that make it possible to trace information back to its origin.
Consistency over time: Reliability that is demonstrated across multiple articles or updates, not just in isolated instances, showing a track record of credibility.

Trust and authority: Opportunities for smaller sites

Authority remains one of the clearest trust signals, which can lead AI engines to favor established publishers and recognized domains.

Articles from major media organizations were cited at least 27% of the time , according to a July study of more than 1 million citations across models like GPT-4o, Gemini Pro, and Claude Sonnet.

For recency-driven prompts – such as “updates on new data privacy regulations in the U.S.” – that share rose to 49%, with outlets like Reuters and Axios frequently referenced.

AI Overviews are three times more likely to link to .gov websites compared to standard SERPs, per Pew Research Center’s analysis.

All of that said, “authority” isn’t defined by brand recognition alone.

Generative engines are increasingly recognizing signals of first-hand expertise – content created by subject-matter experts, original research, or individuals sharing lived experience.

Smaller brands and niche publishers that consistently demonstrate this kind of expertise can surface just as strongly, and sometimes more persuasively, than legacy outlets that merely summarize others’ expertise.

In practice, authority in AI search comes down to demonstrating verifiable expertise and relevance – not just name recognition.

And because engines’ weighting of authority is rooted in their training data, understanding how that data is curated and filtered is the next critical piece.

Dig deeper: How to build and retain brand trust in the age of AI

The role of training data in trust assessment

How generative engines define “trust” starts long before a query is entered.

The foundation is laid in the data they’re trained on, and the way that data is filtered and curated directly shapes which kinds of content are treated as reliable.

Pretraining datasets

Most large language models (LLMs) are exposed to massive corpora of text that typically include:

Books and academic journals : Peer-reviewed, published sources that anchor the model in formal research and scholarship.
Encyclopedias and reference materials : Structured, general knowledge that provides broad factual coverage.
News archives and articles : Especially from well-established outlets, used to capture timeliness and context.
Public domain and open-access repositories : Materials like government publications, technical manuals, and legal documents.

Just as important are the types of sources generally excluded, such as:

Spam sites and link farms.
Low-quality blogs and content mills.
Known misinformation networks or manipulated content.

Data curation and filtering

Raw pretraining data is only the starting point.

Developers use a combination of approaches to filter out low-credibility material, including:

Human reviewers applying quality standards (similar to the role of quality raters in traditional search).
Algorithmic classifiers trained to detect spam, low-quality signals, or disinformation.
Automated filters that down-rank or remove harmful, plagiarized, or manipulated content.

This curation process is critical because it sets the baseline for which signals of trust and authority a model is capable of recognizing once it’s fine-tuned for public use.

How generative engines rank and prioritize trustworthy sources

Once a query is entered, generative engines apply additional layers of ranking logic to decide which sources surface in real time.

These mechanisms are designed to balance credibility with relevance and timeliness.

The signals of content trustworthiness we covered earlier, like accuracy and authority, matter. So do:

Citation frequency and interlinking.
Recency and update frequency.
Contextual weighting.

Citation frequency and interlinking

Engines don’t treat sources in isolation. Content that appears across multiple trusted documents gains added weight, increasing its chances of being cited or summarized. This kind of cross-referencing makes repeated signals of credibility especially valuable.

Google CEO Sundar Pichai recently underscored this dynamic by reminding us that Google doesn’t manually decide which pages are authoritative.

It relies on signals like how often reliable pages link back – a principle dating back to PageRank that continues to shape more complex ranking models today.

While he was speaking about search broadly, the same logic applies to generative systems, which depend on cross-referenced credibility to elevate certain sources.

Recency and update frequency

Content freshness is also critical, especially when trying to appear in Google AI Overviews.

That’s because AI Overviews are built upon Google’s core ranking systems, which include freshness as a ranking component.

Actively maintained or recently updated content is more likely to be surfaced, especially for queries tied to evolving topics like regulations, breaking news, or new research findings.

Contextual weighting

Ranking isn’t one-size-fits-all. Technical questions may favor scholarly or site-specific sources, while news-driven queries rely more on journalistic content.

This adaptability allows engines to adjust trust signals based on user intent, creating a more nuanced weighting system that aligns credibility with context.

Dig deeper: How generative information retrieval is reshaping search

Internal trust metrics and AI reasoning

Even after training and query-time ranking, engines still need a way to decide how confident they are in the answers they generate.

This is where internal trust metrics come in – scoring systems that estimate the likelihood a statement is accurate.

These scores influence which sources are cited and whether a model opts to hedge with qualifiers instead of giving a definitive response.

As noted earlier, authority signals and cross-referencing play a role here. So does:

Confidence scoring: Models assign internal probabilities to the statements they generate. A high score signals the model is “more certain,” while a low score may trigger safeguards, like disclaimers or fallback responses.
Threshold adjustments: Confidence thresholds aren’t static. For queries with sparse or low-quality information, engines may lower their willingness to produce a definitive answer – or shift toward citing external sources more explicitly.
Alignment across sources: Models compare outputs across multiple sources and weight responses more heavily when there is agreement. If signals diverge, the system may hedge or down-rank those claims.

Challenges in determining content trustworthiness

Despite the scoring systems and safeguards built into generative engines, evaluating credibility at scale remains a work in progress.

Challenges to overcome include:

Source imbalance

Authority signals often skew toward large, English-language publishers and Western outlets.

While these domains carry weight, overreliance on them can create blind spots – overlooking local or non-English expertise that may be more accurate – and narrow the range of perspectives surfaced.

Dig deeper: The web is multilingual – so why does search still speak just a few languages?

Evolving knowledge

Truth is not static.

Scientific consensus shifts, regulations change, and new research can quickly overturn prior assumptions.

What qualifies as accurate one year may be outdated the next, which makes algorithmic trust signals less stable than they appear.

Engines need mechanisms to continually refresh and recalibrate credibility markers, or risk surfacing obsolete information.

Opaque systems

Another challenge is transparency. AI companies rarely disclose the full mix of training data or the exact weighting of trust signals.

For users, this opacity makes it difficult to understand why certain sources appear more often than others.

For publishers and marketers, it complicates the task of aligning content strategies with what engines actually prioritize.

The next chapter of trust in generative AI

Looking ahead, engines are under pressure to become more transparent and accountable. Early signs suggest several directions where improvements are already taking shape.

Verifiable sourcing

Expect stronger emphasis on outputs that are directly traceable back to their origins.

Features like linked citations, provenance tracking, and source labeling aim to help users confirm whether a claim comes from a credible document and spot when it does not.

Feedback mechanisms

Engines are also beginning to incorporate user input more systematically.

Corrections, ratings, and flagged errors can feed back into model updates, allowing systems to recalibrate their trust signals over time.

This creates a loop where credibility isn’t just algorithmically determined, but refined through real-world use.

Open-source and transparency initiatives

Finally, open-source projects are pushing for greater visibility into how trust signals are applied.

By exposing training data practices or weighting systems, these initiatives give researchers and the public a clearer picture of why certain sources are elevated.

That transparency can help build accountability across the industry.

Dig deeper: How to get cited by AI: SEO insights from 8,000 AI citations

Turning trust signals into strategy

Trust in generative AI isn’t determined by a single factor.

It emerges from the interplay of curated training data, real-time ranking logic, and internal confidence metrics – all filtered through opaque systems that continue to evolve.

For brands and publishers, the key is to align with the signals engines already recognize and reward:

Prioritize transparency: Cite sources clearly, attribute expertise, and make it easy to trace claims back to their origin.
Showcase expertise: Highlight content created by true subject-matter experts or first-hand practitioners, not just summaries of others’ work.
Keep content fresh: Regularly update pages to reflect the latest developments, especially on time-sensitive topics.
Build credibility signals: Earn citations and interlinks from other trusted domains to reinforce authority.
Engage with feedback loops: Monitor how your content surfaces in AI platforms, and adapt based on errors, gaps, or new opportunities.

The path forward is clear: focus on content that is transparent, expert-driven, and reliably maintained.

By learning how AI defines trust, brands can sharpen their strategies, build credibility, and improve their odds of being the source that generative engines turn to first.

Gating in an AI world: What to hide, what to show, and why

Thu, 04 Sep 2025 13:00:00 GMT

For as long as marketers have been chasing leads online, the debate over gated versus ungated content has raged.

Entire conference sessions, whitepapers, and LinkedIn flame wars have been dedicated to the question:

Should you hide your best stuff behind a form fill, or give it away for free to maximize search rankings and reach?

The problem is that most of this debate hasn’t caught up with the new realities of AI-driven search.

In a world where visibility in Google’s AI Overviews, Microsoft Copilot, ChatGPT, and Perplexity directly shapes brand authority, hiding the wrong content behind a gate doesn’t just cost you some top-of-funnel visibility.

It makes you invisible in the layer of search that now matters most: the AI answer layer.

AI can’t and won’t fill out a form or subscribe to your paywall.

If your content is gated, the models can’t see it, can’t cite it, and can’t use it to represent your brand in synthesized answers.

This article aims to reframe the gating debate for 2025 and beyond.

Instead of a binary yes/no, I’ll offer a decision framework for modern gating:

Always ungated: The materials AI and humans alike rely on to understand your value proposition.
Conditionally gated: Feeper research, templates, and assets – but only after exposing enough to earn citations and trust.
Never gated: The basics that establish credibility, authority, and discoverability.

By mapping each type of content to lead quality, brand visibility, and AI presence, you’ll have a clear rubric for what to hide, what to show, and why.

Why AI changes the gating conversation

Traditionally, the gating decision was framed as a trade-off between visibility and lead quality.

Ungated : More eyeballs, less lead capture.
Gated : Fewer visitors, but more “serious” form fills.

AI-driven search has come along and moved the goalposts.

These systems no longer index the whole page and show a URL.

Instead, they retrieve and synthesize content sentence by sentence based on relevance and clarity.

That means if the only version of your report lives behind a form or your insights sit behind a paywall, they effectively don’t exist in the new search ecosystem.

Even worse, if your competitors ungate their abstracts, summaries, and key findings, their content becomes the default citation source for AI Overviews and Copilot answers.

They become the recognized authority, while your gated masterpiece stays invisible.

AI doesn’t reward the best-hidden asset. It rewards the most visible, extractable, and trustworthy one.

Dig deeper: Driving traffic to gated content and paywalled sites: SEO tips + examples

Always ungated: Your brand’s ‘understand me’ layer

Some content should never be hidden. Not from users, and not from machines. This is the content that establishes who you are, what you do, and why you’re credible.

Examples include:

Summaries and abstracts : AI pulls these directly into answers. If your executive summary is locked up, you won’t be cited.
FAQs and definitions : Frequently asked questions and concise definitions are prime AI fodder.
Pricing and product basics : If you hide this, AI will default to third-party sources – which might not be accurate.
Author bios and credentials : Ungating author information is a credibility multiplier. E-E-A-T /QC systems look for clear expertise.

These assets act like your brand’s knowledge graph in miniature.

Gating them is like pulling your business card out of circulation and then wondering why no one calls.

Ungated basics ensure that both AI and humans can understand, trust, and represent you correctly.

Conditionally gated: The ‘earn the right’ layer

This is where nuance comes in.

There are absolutely assets you may want to gate – but gating should come after you’ve earned visibility and trust.

Think:

Research reports.
Templates and calculators.
In-depth guides.
Case studies.

The trick is not to slam the gate at the headline.

Instead, provide enough public-facing content to establish credibility and allow AI to cite you.

For example:

Ungate the abstract, methodology, and key findings of a research report. Gate the full dataset and deep analysis.
Ungate a screenshot and explanation of a template. Gate the full downloadable file.
Ungate high-level insights from a case study. Gate the step-by-step breakdown or full deck.

This “teaser ungating” approach achieves two things:

AI inclusion : Models can see, parse, and cite your key takeaways.
Lead quality : Serious prospects will still exchange information for the full version.

It’s a balance, but err on the side of ungating enough to establish authority.

If you don’t, someone else’s partially visible research will be the one cited instead.

What about paywalls?

Yes – paywalls count as gating.

From the perspective of both humans and AI, if the content isn’t visible without logging in or paying, it’s gated.

There are two major consequences:

For most brands: A hard paywall means your content won’t be included in AI Overviews or Copilot, because the models can’t access it. Unless you negotiate a licensing deal with OpenAI, Google, or Microsoft (as a few elite publishers have done), your work is invisible.
For media companies: Some can get away with it because their authority is so strong that snippets, summaries, and syndicated content exist elsewhere. But for everyone else, paywalls without visible abstracts or teasers are a recipe for disappearance.

If you must use a paywall, pair it with ungated summaries, abstracts, and key data points.

That way, AI systems (and human searchers) still see enough to recognize your authority and cite you.

Beware of soft gates and accidental gating

Not all gates are intentional. Sometimes, brands inadvertently hide their most important content behind what I call soft gates:

PDFs that require clicking through a modal or JavaScript event.
“Read more” toggles that collapse key details.
Accordions or tabbed content where the default state hides the text.
Inline lead-gen overlays that must be dismissed before accessing the content.

From a human perspective, these seem minor – just one extra click.

However, from the perspective of AI systems, they’re effectively gates.

Large language models:

Don’t mimic human behavior at inference time.
Don’t open toggles, expand tabs, or click “download” buttons.
Retrieve and parse only what is visible in the rendered HTML at page load.

That means your “get to know me” content – the very material that establishes credibility and authority – may be invisible if it’s hidden behind a collapsed section or accessible only through a PDF download.

The fix is simple but critical:

Surface summaries inline before linking to full PDFs.
Keep key takeaways visible by default.
Avoid making trust signals (like bios or pricing) conditional on interaction.

If the AI can’t see it without “acting like a user,” it won’t use it, and in the current landscape, invisibility is the same as irrelevance.

Never gated: The ‘credibility’ layer

Some information should never be behind a wall.

Gating it frustrates users and undermines your authority signals with search engines and AI models.

Pricing : If buyers can’t see your pricing, they’ll turn to competitor pages, aggregators, or (worse) AI-generated guesses.
Author and company credentials : Gating this is like telling AI, “We’re not sure we want you to know who we are.” It’s a bad idea.
Basic product specs or service descriptions : Essential for visibility in product-related AI queries.

Hiding this type of content actively damages your E-E-A-T footprint.

If AI can’t verify who you are, what you sell, or why you’re credible, you’re far less likely to be surfaced.

Think of this as the table stakes of trust.

You don’t win by hiding them – you lose.

Mapping gating to outcomes

Here’s a simple way to visualize the impact of gating choices:

When in doubt, ask:

Does gating this improve lead quality or revenue enough to offset the loss of AI visibility?

If the answer is no, ungate it.

A practical checklist for deciding what to gate

Before slapping a form fill, paywall, or modal on your next asset, walk through this checklist:

Will this content build trust if visible?
- If yes, ungate it. Trust-building content is too valuable to hide.
Does AI need to “see” this to recognize us as authoritative?
- If yes, ungate it – at least partially.
Can I provide a teaser version that earns citations without giving everything away?
- If yes, use conditional gating.
Would gating this undermine our E-E-A-T footprint?
- If yes, don’t gate. You can’t afford to weaken your credibility signals.
Is there enough ungated content elsewhere to establish authority?
- If your entire site is walled off, you’ll vanish. Balance is key.

Bringing it all together

The old gating debate framed it as a binary: hide everything or give everything away.

But in the AI-driven search era, the choice isn’t between free vs. lead-gen. It’s between visible vs. invisible.

AI Overviews, Copilot, and Perplexity are shaping how users discover and trust brands.

If your best content is locked away – behind a form, a paywall, or even a toggle – AI can’t cite you.

And if AI can’t cite you, you’re absent from the very narratives shaping search results.

The modern strategy is layered:

Ungate the “understand me” content (summaries, FAQs, bios, pricing).
Tease the “earn the right” content (research, templates, guides) so both AI and humans can see enough to trust you.
Never gate credibility basics (pricing, credentials, specs).
Be strategic with paywalls : They can generate subscription revenue, but only if they are paired with visible abstracts and context.
Eliminate soft gates : Don’t let JavaScript, toggles, or PDF-only assets hide the very signals that make you worth citing.

In short: don’t lock away the very signals that make your brand worth citing.

Dig deeper: AI visibility: An execution problem in the making

Visibility is the new currency

For years, marketers justified gating with the phrase: “If they want it badly enough, they’ll fill out the form.”

The problem is: AI-driven search doesn’t want it badly enough.

It will not fill out a form, it will not subscribe to your paywall, and it won’t click “expand more” to read the details.

That doesn’t mean lead-gen and subscriptions are dead. It means the path to leads and revenue now runs through visibility first.

Build trust, earn citations, and show up in AI answers. Then invite users deeper with gated extras once your authority is established.

In 2025 and beyond, the brands that survive and thrive will be the ones that master this balance.

Not by just hiding, but by knowing exactly what to hide, what to show, and why.

Apple to launch AI search for Siri in 2026: Report

Wed, 03 Sep 2025 20:53:00 GMT

Apple is preparing its own AI-powered search engine. Known internally as World Knowledge Answers, it will debut next spring as part of a long-awaited Siri overhaul, Bloomberg reported.

The goal: Transform Siri into an “answer engine,” pulling information from across the web in a style similar to Google’s AI Overviews, ChatGPT, and Perplexity.

The upgrade. It will go well beyond Siri’s current fact-checking. Apple’s new system will generate summaries that blend text, images, video and local results.

The company plans to expand it to Safari and Spotlight, giving Apple multiple footholds in everyday search.

Behind the scenes. Apple will rely partly on Google’s Gemini AI model for its new search experience.

Siri’s overhaul, built around large language models (LLMs), also includes a new planner and summarizer to make responses more conversational and accurate.
Apple considered adding a standalone chatbot-style app, but for now is weaving the search into Siri and core iOS features.

Why we care . Apple’s push into AI search could reshape how billions of queries are handled on iPhones. Visibility for brands and businesses won’t just depend on Google rankings – it will depend on whether and how Apple’s AI systems surface and summarize your content in voice and web answers.

The report. Apple Plans AI-Powered Web Search Tool for Siri to Rival OpenAI, Perplexity (subscription required)

Yes, GEO is happening

Wed, 03 Sep 2025 18:24:00 GMT

Carolyn Shelby published an article on SEJ the other day with the headline Stop Trying To Make GEO Happen .

“Stop trying to make GEO happen. It’s not going to happen,” she wrote.

With all due respect to Shelby, whom I know and have tons of respect for (she’s a contributor at Search Engine Land as well), GEO is already happening.

The arguments I’ve seen dismissing GEO on LinkedIn and elsewhere lately (often by “it’s just SEO” people) seem to ignore history and reality.

GEO is already real

“Generative Engine Optimization” isn’t just a buzzword someone cooked up on LinkedIn.

The GEO term was formally introduced in a research paper in December 2023.

Since then, it has appeared in conference talks, industry analyses, and practitioner discussions.

Marketers and researchers alike are already using it.

This isn’t wishful thinking. It’s early adoption.

Language evolves

Look no further than a certain American multinational corporation and technology company – nobody assumes “Apple” only means fruit.

Tech is built on redefined words.

“Cloud” doesn’t just mean a white thing in the sky.
“Cookies” aren’t just baked goods.
“Spam” isn’t just canned meat.

We didn’t reject those terms because of any “linguistic baggage.”

People embraced them and adopted them.

GEO is no different.

Pronunciation isn’t a dealbreaker

The argument that nobody will say “G-E-O” just doesn’t hold.

I’ve been to conferences and heard it mentioned. I’ve heard it in meetings.

Nobody has a problem saying the letter G-E-O in succession. Just like they can easily say S-E-O.

C’mon. It’s a one-letter difference. If I showed my 4-year-old daughter those three letters, she could say them.

We have plenty of initialisms and acronyms that aren’t “perfect.” CTR. CRO. ASO. CWV (you know, LCP, INP, and CLS). GSC.

GEO is, if nothing else, more memorable than many of those.

GEO can be said as “gee-oh” or “G-E-O.” Both work. Neither is a barrier to adoption.

Search engines catch up to language

The idea that GEO can’t compete with entrenched meanings in Google or LLM training data is shortsighted.

Comparing GEO to “FBI = For Better Indexing” is a false equivalence. The FBI is one of the most globally recognized initialism in existence. That’s not GEO’s situation.

Think about content marketing. In the early 2010s, hardly anyone was using the term.

By the mid-2010s, it wasn’t just mainstream – it had conferences, software, dedicated teams, and entire agencies built around it.

Search intent shifted. Authority shifted. Google caught up to the way the community was using the language.

That’s exactly how new terms gain traction.

Authority and intent aren’t fixed permanently; they evolve as people adopt and reinforce new concepts.

GEO is following that same path.

Search engines and LLMs will follow the users if they adopt GEO.

Let’s stop pretending SEO is a perfect name

Everyone seems to forget this: Search Engine Optimization is itself a pretty dumb name.

We’re not actually “optimizing search engines.” We’re optimizing content for search engines.

SEO easily could have been “Content Optimization for Search Engines” (COSE?) or something equally clunky.

Oh, and by the way, SEO isn’t even an acronym. It’s an initialism .

You don’t say “see-oh” — you say the letters: S-E-O. Same will be true for G-E-O.

So why hold GEO to some linguistic purity standard?

GEO is just as “bad” or just as “good” as SEO.

And like SEO, GEO will stick if it’s useful.

GEO is a chance to reframe SEO’s reputation

Let’s be honest: SEO as a discipline has always been undervalued and misunderstood.

Externally, it’s often reduced to clichés about “gaming Google” or “keyword stuffing,” while those of us inside the industry know the reality – that SEO drives visibility, revenue, and long-term growth for brands and businesses of all sizes.

That’s why GEO matters. It’s not just about adapting to AI-driven search. It’s a chance to rebrand our field for the next era, to shed some of the baggage SEO has carried for two decades.

GEO signals that optimization isn’t static. It evolves with technology – and so does the value of the people doing it.

In other words, GEO isn’t just a new initialism. It’s a fresh opportunity to get the recognition this discipline deserves.

GEO isn’t going away

Generative Engine Optimization is an attempt to describe the reality of how search is changing.

Is it a perfect name? No. But again, neither was SEO.

What GEO tries to do is define the real shift in optimization practice as AI-driven search reshapes discovery.

The initialism is already in circulation in academia and the marketing industry. Communities are adopting it. Again, the language will follow the people.

The choice isn’t whether GEO will “happen.” It already has.

The only choice is whether you want to be ahead of the curve or play catch-up.

First, they ignored GEO. Then they laughed at GEO. Now they’re fighting GEO. Maybe this means GEO will indeed win?

Dig deeper. What’s next for SEO in the generative AI era

Why SEO and paid media need each other in the AI search era

Wed, 03 Sep 2025 13:00:00 GMT

AI search is rewriting the rules of visibility.

Google’s AI Overviews aren’t just another rich result or algorithm update.

They are cutting into clicks, pushing ads into unpredictable spots, and leaving both SEO and PPC teams with incomplete data.

Success now depends on breaking down silos:

Sharing insights.
Aligning strategies.
Moving together when the old playbooks no longer work.

The black box of AI Overview performance

Many brands are already seeing drops in paid CTRs , Quality Scores, and clicks across terms that once performed well.

Even though ads are now showing in AI Overviews, Google Ads doesn’t provide a clear way to track them.

Just as SEOs can’t see performance for these results in Google Search Console, PPC marketers are also blind to placement data.

Here’s how the gaps show up for paid search teams today:

Ad placement is a black box: Ads can show above, inside, or below the summaries – but Google offers no breakdown of impressions, clicks, or conversions by placement.
Same auction rules, new environment: Ads in AI-generated summaries still run through the standard auction, so bids, Quality Score, and relevance all matter – but the context is shifting.
All the data gets lumped together: Just like Search Console, Google Ads aggregates everything. If your ad appears in an overview, the clicks and impressions are reported under the general “Top Ads” bucket, with no segmentation and no clarity.

With visibility this limited, SEOs have an opportunity to step in and add value.

By sharing what we’re seeing in the SERPs, we can help paid media teams make smarter decisions, faster.

When AI search redraws the funnel, SEO and paid media must align

AI Overviews are hurting clicks , especially from top-of-funnel queries.

Featured snippets and zero-click results were the warm-up.

Now, we’ve entered a new reality where sometimes it feels like the entire first page of Google is condensed into AI-generated summaries.

You’ll even spot People also ask answers generated as AI Overviews.

It’s not just SEO that’s evolving.

AI Overviews are changing paid search too, and giving SEOs a bigger role in supporting and amplifying campaign performance.

“Great SEOs have been aligned with paid media teams for a while now. I think that the difference now is that the search journey has shifted so that there is less top of funnel traffic, meaning that more ‘SEO’ happens on bottom of the funnel pages,” explains Crystal Carter , head of SEO communications at Wix.
“Since those pages need to work well for paid and organic search users, there are a lot of opportunities for collaboration between teams. As there should be.”

We’re seeing this shift echoed across the industry, too.

“AI Overviews are reshaping the funnel, handling a lot of top and mid-funnel education, then accelerating buyers straight to purchase. That means SEO and paid can’t just run in parallel anymore; they have to collaborate around intent. SEO feeds the LLMs and builds brand trust across the journey, while paid captures the transaction when it matters most,” shares Amanda Valle , global director of organic search at Liquid Web.
“We’re testing, learning, and reallocating together, building a playbook that aligns content and spend to our ideal customer profiles so we show up wherever the buyer decides.”

This kind of collaboration isn’t a nice-to-have anymore.

It’s table stakes for success in our new AI-dominated landscape.

Smarter SEO and paid media collaboration in the age of AI search

AI Overviews are blurring the lines between organic, paid, and AI search results.

The best SEOs are already helping PPC teams:

Understand what’s causing changes in keyword performance.
Craft ad copy that mirrors how users are engaging with AI results.
Determine how they can shift spend toward higher-impact keywords.
Spot new competitors before they show up in your CPC data.

1. Track AI Overviews at the paid keyword level

Not all keywords trigger AI Overviews yet. But for the ones that do, your paid team needs to know.

Offer to set up a tagged keyword list in your keyword tracking tool of choice (e.g., Semrush, Ubersuggest, Ahrefs) where you track the most important keywords in your paid campaigns.

The sooner you set this up, the better, as it can be tedious to backfill AI Overviews visibility for untracked/untagged keywords.

Start flagging which terms are generating AI overviews, and what the brand’s visibility looks like against competitors.

Sure, tracking all of your paid keywords might mean a small added cost, but these insights can help paid media teams:

Reallocate budget away from low-performing AI-dominated SERPs.
Adjust bids for terms with dropping CTRs.
Explain Quality Score shifts with data-backed context.
Determine the best strategy for high-intent keywords.

To maximize the impact of earned and paid in AI search, Cassandra Gucwa , founder and CEO of Menerva Digital, explains:

“We focus on purchase-intent prompts where competitors appear, but our client doesn’t. Then we evaluate whether it’s more efficient to capture visibility through earned strategies or paid placements.”

2. Use AI copy patterns to improve ad relevance

More than quick answers, AI Overviews are curated summaries built to feel useful, and users are starting to trust how they read. That’s a signal.

It tells you exactly how to write ad copy that feels relevant and helpful in the same moment.

Think of AI Overviews as free market research, or a live feed of what Google thinks users want to see. Watch for:

Recurring phrases : What language keeps coming up?
Tone : Is it formal, casual, or expert level?
Brand mentions : Are competitors being cited?

Now, use those insights and start testing ad copy that mirrors what’s already winning Google shelf space.

If AI Overviews surface phrases like “fast acting,” “top rated,” or “budget friendly,” your ads may perform better if you echo that tone.

3. Flag zero and low-click queries early

Google is making it easier than ever for users to get answers without clicking, and that trend is not going away anytime soon.

SEO teams have access to a wealth of helpful data from tools like Google Search Console and keyword tracking tools.

We should:

Flag which queries are consistently being answered directly in the SERP through AI Overviews.
Highlight those dominated by other click-reducing features like featured snippets and People Also Ask .
Share helpful data from Google Search Console, such as which high-intent queries show the lowest and highest CTRs.

We should also flag keywords that aren’t consistently triggering AI Overviews, as that can help paid media teams focus on those potentially higher-impact keyword opportunities.

With all of these organic insights, paid media teams can consider if they want to test:

Pausing low-performing keywords that no longer justify the spend.
Focusing on higher intent queries with more click potential.
Shifting messaging to go beyond what’s commonly in AI answers.

This kind of data sharing fuels better testing and a smarter search strategy on both paid and organic.

“AI search has forced our clients to rethink their budgets. Clients are dedicating more budget to optimize for AI search from an earned perspective while setting aside additional testing budgets for paid media,” Gucwa shared.

4. Monitor competitor mentions in AI results

One of the biggest missed opportunities in SEO and paid media collaboration is competitive intelligence.

SEO teams are already monitoring the SERPs, sometimes daily, and can help paid media teams spot brand mentions, shifts in content types, and new players faster.

Watch for patterns with top competitors and new brands that weren’t on your radar before, as well as the different types of content and user intents being met.

Use this data to:

Prioritize conquest campaigns against highly cited competitors.
Adjust positioning to stand out against AI answers.
Spot new players emerging before they appear in Auction Insights.

It’s also worth noting that this type of competitive intelligence isn’t just helpful for AI Overviews.

These days, SEO teams are monitoring AI visibility by topic across platforms like ChatGPT, Perplexity, and more.

I’ve seen AI visibility insights introduce brand new competitors to teams, and give them a totally new way to think about their competitive disadvantages.

Collaboration is the competitive edge in AI search

AI search is shaking up our paid and organic playbooks and giving us a new, exciting edge.

SEOs have access to real-time SERP shifts, AI copy trends, and competitive visibility insights. Paid media teams have the agility and data to act fast and capitalize on those trends.

Together, SEO and paid media teams can build smarter strategies that thrive in an AI-dominated landscape.

Scaling PPC with AI automation: Scripts, data, and custom tools

Wed, 03 Sep 2025 12:00:00 GMT

PPC advertising has never stood still.

Efficiency, scalability, and smart automation are now essential for success, but that wasn’t always the case.

When I started more than 15 years ago, campaign management meant adjusting CPCs by a cent or two at the keyword level – manually.

Since then, the role of a PPC specialist has changed dramatically.

The biggest shift has been the rise of automation and AI.

This article covers how AI can take over repetitive tasks and open new possibilities.

It also takes a closer look at Cursor, a platform that combines automation, AI, and scripting to help advertisers work faster without losing control or flexibility.

You don’t need to be a developer to get started – though a little technical knowledge still helps in understanding what AI is building for you.

New possibilities for PPC automation with Cursor

Most PPC tools are built for a narrow purpose, but Cursor takes a different approach.

It isn’t a dedicated PPC platform like Optmyzr or TrueClicks.

It’s an AI-powered coding environment with a user-friendly interface that makes advanced scripting accessible to anyone.

That flexibility is what makes it so valuable for PPC professionals.

By combining AI integration, scripting, data analysis, and reporting in one interface, Cursor gives you the freedom to create solutions tailored to their needs.

Let’s examine how these capabilities work in practice – automating scripts, analyzing data, and building custom tools.

1. Automate your account with scripts

One of Cursor’s biggest advantages is the ability to write automated scripts without requiring extensive technical knowledge.

You can easily set up scripts for common PPC tasks, such as:

Collecting Google Ads data.
Optimizing bidding strategies.
Adding negative keywords.

Even though scripting is not new, the way Cursor does it is impressive.

Where other AI tools like ChatGPT used to produce many errors while generating scripts, Cursor makes almost no mistakes.

It’s also significantly faster than ChatGPT.

Additionally, it’s very easy to connect Cursor to your GitHub account or specific APIs (such as the Google Ads API) to execute your scripts correctly within the Google Ads interface.

It’s worth noting that GPT-5 also makes script generation in ChatGPT more effective.

While countless examples of PPC scripts already exist online, the possibilities with Cursor remain virtually limitless.

Whether it’s adding automations (i.e., automatically adjusting campaign settings based on data loaded into a spreadsheet at a fixed time) or reporting (i.e., automatically exporting campaign data to a spreadsheet with feedback from an LLM system), the potential applications are wide-ranging.

Dig deeper: How to vibe code for PPC: Building a seasonality analysis tool

2. Integrate external data sources for analysis

One of Cursor’s most valuable features is its ability to analyze data for you.

PPC campaigns require continuous optimization, and gaining insight into the performance of keywords, ads, and budgets is essential.

You can easily upload or connect data in your Cursor account and analyze it alongside other external data sources.

For example, you could connect weather data online – using the right API credentials – to your own datasets or scripts.

Cursor performs the analysis, presents the outcomes, and offers optimization suggestions.

Here’s an example from my experience.

For a herbs-and-spices website that functions both as a webshop and a recipe resource, we used Cursor to combine historical client data with the recipes database.

We asked the system to generate a spreadsheet showing which recipes could be made from the products customers had purchased.

Using that information, we created a dynamic email campaign.

Each recipient received a message such as:

“Hi, you’ve bought product X – did you know you could make recipe Y with it?”

This was paired with a discount code.

Dig deeper: Top AI tools and tactics you should be using in PPC

3. Build your own tools

Cursor also enables the creation of custom tools, websites, or web apps.

Its strength lies in excellent coding capabilities, partly thanks to integration with virtually all relevant LLM models, including Gemini, GPT-5, and Claude.

As a PPC specialist, there are several possibilities. For example:

Geert Groot built his own MCC dashboard tool, allowing all his clients to view data in real time – or, by entering keywords, instantly receive ad suggestions that can be uploaded to their accounts with a single click via the Google Ads API.
Alfred Simon developed a tool to improve images in his shopping feed. These examples illustrate the range of applications
Frederick Vallaeys has shared additional use cases as well.

With these capabilities in hand, the next step is understanding how adopting AI tools like Cursor can transform your PPC workflow.

Dig deeper: AI agents in PPC: What to know and build today

Embracing AI-powered automation tools like Cursor

The pace of change in PPC is accelerating, and the landscape will only grow more complex in the coming years.

Now is the time to embrace AI and automation to remain effective and relevant as a PPC specialist.

PPC specialists who adopt these technologies can achieve better results while working more efficiently and saving time.

Beyond staying current with new developments, AI frees you to focus on higher-value tasks – like account strategy – rather than repetitive, time-consuming work that often drains energy.

This applies not only to Cursor but to AI tools in general.

What you shouldn’t expect

AI is still AI. A human touch remains essential. Guide AI as effectively as possible.

For example, when creating Google Ads scripts with Cursor, be sure to tell the system where to find the script documentation.

This ensures the tool uses the official Google Developers documentation as a reference, significantly reducing the risk of errors.

How to get started

Cursor can feel overwhelming at first. Make sure you familiarize yourself with the interface before diving in.

Before building scripts or web apps, clearly define what you want to create. Don’t start coding from scratch without a plan.

As Mike Rhodes advises, don’t try to jump straight from step 1 to step 100.

Take it step by step – the chance of success is much higher this way.

Members of the community are already building software with tools like Cursor or Loveable and, in some cases, selling it to vendors.

This approach can save money while providing a highly personalized tool or script.

If you aren’t exploring tools like Cursor, you may be missing out.

Set aside time in your schedule and start (vibe)coding!

GEO and SEO: How to invest your time and efforts wisely

Tue, 02 Sep 2025 13:00:00 GMT

Generative engine optimization (GEO) is the new kid on the block, the hot topic.

SEO professionals and stakeholders want to know: How much should I invest in it in a world where the people writing the checks are a bit skeptical?

In the world of large language models (LLMs) in 2025, that’s a complicated question.

This article breaks down why by covering:

How LLMs like OpenAI and Gemini currently use search engines.
What search marketers should assume about where AI is heading.
The types of executional work that align with GEO.
What all of this means for prioritization and investment.

How LLMs stay current: Grounding and RAG

One of the fundamental challenges for the creators of LLMs (LLMs) like OpenAI or Claude is timeliness.

Their training data is static, locked to a specific cutoff date.

For example, the GPT-5 model’s knowledge cutoff is Sept. 30, 2024.

It’s more recent than GPT-4o’s cutoff of Oct. 1, 2023, but still not up to the present day.

Updating that training data is extremely costly, and it’s increasingly under public scrutiny – both for the resource-intensive nature of the process and the potential copyright issues it raises.

In my view, these large-scale training updates are becoming less and less likely over time.

So how do OpenAI, Claude, or Gemini keep their answers current?

They use retrieval-augmented generation (RAG), where the model enriches its responses by effectively “browsing” the web. ChatGPT relies on Bing, while Gemini draws from Google.

(There are signs Gemini doesn’t always use live results, but rather cached ones – that’s a whole other article, and one Dan Petrovic has already written smartly about.)

Grounding is a similar concept here, so for this article, we’ll treat it as the same “timely” method, even though there are important nuances in implementation.

What does this mean for SEOs and digital marketers deciding how much to invest in GEO?

Quite simply: we still need to prioritize traditional SEO first. RAG is a limited resource, and research shows:

Of the 80 million ChatGPT queries analyzed, about 46% triggered ChatGPT search .
Nearly 90% of ChatGPT search citations matched Bing’s Top 20 results.

It’s also important to note: when ChatGPT cites your brand, it doesn’t just pull from your website. It pulls from sources across the web.

The bottom line: you still need to master traditional SEO fundamentals to rank in LLM-driven search.

If you don’t have the authority to break into the Top 20 results, plus a diversified outreach strategy for press mentions and brand visibility, it will be much harder to surface in generative search.

Dig deeper: SEO vs. GEO: What’s different? What’s the same?

Thinking long term

As a low-risk, forward-looking, brand-focused SEO, you must plan for a future where generative search dominates, driving most traffic and revenue.

At that point, we must assume our websites and digital properties function primarily as enriched data feeds for LLMs.

It’s also critical to clearly define our brand for both Google and Bing, as strong, unambiguous entity signals will only grow in importance.

Optimizing your data infrastructure and strengthening brand signals – through consistent press mentions, directory listings, and owned media – are essential but resource-intensive tasks.

They demand coordination across departments that rarely collaborate and often require dismantling entrenched processes.

Because many businesses hesitate to make these foundational changes, you’ll need to account for the time required to execute the work and the time required to gain stakeholder alignment.

Execution: Technical and brand

The work required to make your website as LLM-friendly as possible falls into two main buckets: technical and brand.

Technical tasks

Implementing thorough schema markup

This is a contentious topic.

LLMs don’t directly use schema markup in their training data (it’s stripped), and in their RAG process, everything is tokenized and likely broken into n-grams.

I’m not suggesting schema markup is a direct way to influence visibility in LLMs.

It’s a vehicle for helping Google and Bing understand:

Your website.
Its relationships.
Its products.

This builds your brand and search engines’ recognition of it, which should improve your visibility in results.

Technical copywriting

On navigational pages – like product collection pages or company listing pages if you’re a marketplace – create technical copy (done via AI with smart prompting if you’re working at scale) that summarizes the available resources.

For example:

“Our stationery includes 5 A5 dotted journals, 2 N1 blank journals, 25 stickers featuring animals, 4 stickers with curse words (all vinyl for weatherproofing and waterproofing), and 1 lapel pin.”

Notice how direct and technical this is. The clear formatting ties back to dependency hops in natural language processing.

XML sitemaps

A spring cleaning task.

Your XML feeds should be 100% to spec :

No 404s.
No 301s.
No more than 5,000 URLs per sitemap.
All recommended fields in place.

I’m calling it out specifically because it’s one of the most direct ways for search engines and other bots to see and navigate the full scope of your website.

JavaScript fallbacks

This has always been important but has fallen by the wayside in recent years.

Training data for LLMs is static HTML. For the most part, they don’t render JavaScript.

Make sure to have functional JavaScript fallbacks.

Address technical debt

This will depend on your organization. It could mean:

Having a clear product sunsetting process.
Updating the codebase.
Removing the ghost codebase still sitting on your site from eight years ago that everyone built on top of rather than deleting.
Migrating from an SPA to a more search-friendly framework.
Removing deprecated scripts.
Auditing third-party tags to ensure they’re up to date and still in use.

All of these impact performance.

The technical strength and response time of your website will only grow more important.

Every piece of tech debt is an opportunity to improve.

Dig deeper: A technical SEO blueprint for GEO: Optimize for AI-powered search

Brand tasks

Brand tasks would include things like:

Creating consistent brand descriptions and implementing them across all platforms.
Updating your About page to have as much relevant information as possible, such as:
- Founding timeline.
- Founders and leadership profiles.
- Corporate social responsibility initiatives.
- Partners.
- Supply chain.
- Your unique selling proposition.
- Press mentions.
- Awards and other social proof.
- Testimonials.
- A contact form.
Claiming your Google Knowledge Panel (or monitoring it until it becomes available, then claiming it).
Planning regular press mentions , doing outreach yourself, or working with a PR company to make it happen.
Claiming and optimizing your Google Business Profile if you’re a local business.
Submitting your company for a Wikipedia page once you’ve built enough notability.

Dig deeper: In GEO, brand mentions do what links alone can’t

Making smart investments in SEO and GEO

If there’s only one takeaway, it’s this: keep investing the majority of your time and budget in traditional SEO, while dedicating a smaller portion to technical and brand tasks like those outlined above.

Look closely at the 1-5% improvements you’ve been putting off – things like:

Correcting the HTML heading hierarchy to match the site’s visual hierarchy.
Fixing internal links so they point directly to final URLs instead of redirect chains.
Cleaning up your XML sitemap.
Removing deprecated libraries and unused WOFF files.

This “spring cleaning” and tech debt cleanup should be a priority.

Add in the brand work as well, since it strengthens traditional search today and also lays the groundwork for an LLM-led search future.

If you don’t already have regular reporting in place for stakeholders and leadership, create it now.

There’s a perception that large language models are evolving rapidly and changing everything at once.

That isn’t entirely true – but we do need to plan.

Establishing a cadence of reporting and education means that when real shifts do happen, your stakeholders will already be aligned and ready to support the work.

Finally, treat GEO/AI optimization as roughly 20% work .

This means building systemic schema layers across your organization and creating structured connections in the machine’s native language – code.

Start with:

Conversations.
Proofs of concept.
Pilot implementations.

Done properly, this work should have no negative impact on your business metrics, and it builds support for more holistic optimization over time.

Going all in on LLM-specific tactics isn’t the best use of your resources today.

Instead, treat it as complementary work – something that strengthens your technical and brand foundation while preparing you for a future where generative search plays a central role.

Looking beyond AI: 9 marketing principles that will always matter

Tue, 02 Sep 2025 12:00:00 GMT

AI has fundamentally changed how people search and engage with information online.

Features like AI Overviews may boost visibility, but they’ve also reduced clicks , leaving many websites with less traffic despite stronger rankings.

Discovery no longer happens in just one place.

It’s fragmented across search engines, social platforms, paid ads, and AI tools, creating a complex user journey that’s harder than ever to track.

As behavior shifts with these new technologies, search marketing is evolving in response.

Yet while the platforms, tools, and touchpoints keep changing, the core principles of effective marketing remain the same.

Marketers who stay grounded in these fundamentals will be best equipped to adapt and grow.

Here are nine timeless marketing principles that will hold steady – no matter how search evolves.

1. Focusing on search intent: Why people search

Where people search and find information will continue to change over time as preferences for LLMs, social media, or video content shape where people go for answers.

However, what remains constant is search intent .

Users are always looking to:

Learn about something.
Navigate to brands they know.
Compare and evaluate different options.
Purchase or convert.

Focus on the why behind a search – the intent driving it.

The key question is whether your content aligns with that intent.

If it doesn’t, you’re overlooking a critical driver of user behavior.

When content matches search intent, users immediately recognize its value and engage, which is why intent should remain central to your marketing strategy.

2. The lasting value of brand recognition and loyalty

Even as AI continues to drive change in how companies reach their audiences, brand recognition and loyalty remain important pillars of long-term engagement and growth.

Discovery channels are shifting as people find brands through social media, search engines, paid ads, email, and more.

That’s why it’s important to continually reassess the customer journey and understand where your audience is finding you.

After discovery, your job is to highlight your unique value – what sets you apart from competitors and how you provide real value to your audience.

Keep asking yourself:

Why should someone choose my brand?
What makes us stand out?

The clearer and more consistently you communicate this in the spaces that matter, the more you’ll earn trust, recognition, and reliability – all of which shape how people respond to your brand.

Brand loyalty isn’t automatic. It’s something you earn by building real relationships with your customers and consistently providing value.

Loyalty creates long-term stability and growth, even as platforms and algorithms continue to shift.

While search intent and brand recognition can attract new visitors, loyalty turns impressions into conversions and builds lasting customer lifetime value.

3. Knowing and understanding your audience

Beyond search intent and branding, truly knowing your audience is essential for long-term marketing success.

Without that insight, you risk falling into “spray and pray” campaigns that waste resources and fail to connect.

Building clear audience personas helps you decide not just what campaigns and content to create, but how to present them in ways that resonate.

That means understanding who your audience is, what motivates them, their pain points, their values, and where they spend their time.

These insights form the foundation of a strategy built to genuinely connect with your audience.

Dig deeper: SEO personas for AI search: How to go beyond static profiles

4. Trustworthiness is currency

Even if your content matches search intent and your brand is well recognized, audiences won’t engage without credibility.

This matters even more today, as AI tools summarize information and highlight only the most trustworthy sources.

Both search engines and AI prioritize trust signals. But for users, those signals are everything.

Expertise, consistency, transparency, and reliability are what build that trust.

People want to feel confident that your brand will deliver on its promises and provide lasting value.

When they believe you’re dependable, they’re more likely to engage, return, and recommend your brand to others.

5. Customer service and experience drive perception

Today, customer service is inseparable from brand experience.

Every interaction – whether answering a support ticket or replying to a social media comment – shapes how people perceive your credibility and value.

Testimonials and reviews create a powerful feedback loop: one story sparks another, influencing how others view your brand.

Campaigns can drive visibility, but audiences still turn to peer reviews on platforms like Reddit to validate those impressions and decide whether to trust you.

Audience sentiment has become its own form of publicity.

With user-generated content (UGC) shaping perception and AI systems relying on reviews and sentiment signals to recommend brands, customer experience is now a direct driver of both reputation and visibility.

Dig deeper: How to use SEO and CX for better organic performance

6. Good user experience supports conversions

A core principle that hasn’t changed is the need for an optimized user experience.

When someone lands on your site, the page should minimize friction in the buying journey.

Whether visitors arrive through ads or organic search, they need clear conversion paths that guide them smoothly forward.

Audiences expect ease and clarity when looking for information or taking action.

Slow load times, unnecessary clicks, or confusing layouts increase drop-offs, abandoned forms, and carts – leaving users frustrated.

A good user experience makes the journey to conversion as effortless as possible.

Done well, it not only boosts conversions but also builds satisfaction and trust.

7. Mobile-first experiences: Meeting users where they are

AI may be transforming how people search, but mobile devices remain the primary way users access and engage with brands.

For many, the first interaction with your brand happens on a phone.

That’s why user experience must extend beyond conversion paths.

It also has to be fully optimized for mobile. Otherwise, you risk frustration, lost trust, and missed conversions.

Mobile users abandon sites that load slowly, require pinching and zooming, use hard-to-tap buttons, or rely on clunky forms.

Even a few seconds of delay or disruptive layout shifts can cause drop-offs.

And because search engines prioritize mobile-friendliness, optimizing for mobile isn’t just about usability. It also directly impacts rankings and visibility.

8. Accessibility is essential

Accessibility is a core part of creating inclusive experiences for your entire audience.

In the U.S., it’s also a legal responsibility.

Making your site accessible means adding features like:

Screen reader compatibility.
Alt text for images.
Strong color contrast.
Keyboard navigation.

If accessibility is overlooked, you risk excluding parts of your audience and facing ADA lawsuits.

But when you design with accessibility in mind, you reach more people, strengthen trust, and ensure everyone can engage with your brand.

9. Quality content and authority still define success

No matter how search evolves, quality content and authority remain the foundation of visibility and trust.

Algorithms may shift and discovery channels may change, but users will always value content that is accurate, relevant, and genuinely helpful.

Authority is earned over time by:

Consistently publishing original, reliable content.
Being cited by other trusted sources.

The more credibility your brand builds, the more likely users (and search engines) are to consider you a worthwhile recommendation.

Dig deeper: Mastering content quality: The ultimate guide

Marketing that lasts beyond AI

AI is transforming how people search and how brands reach them, but the fundamentals of marketing haven’t changed.

What still matters is:

Understanding intent.
Knowing your audience.
Meeting them where they are.
Building trust and loyalty.
Delivering real value.

Technology and platforms will keep evolving.

But the brands that stay grounded in these timeless principles will be the ones that adapt, grow, and thrive in the future.

Top 7 Ways SEO Helps Your Website Rank Better on Google (And Why That Matters!)

Fri, 29 Aug 2025 12:04:06 GMT

In today’s digital-first world, your website is your storefront—and if it’s not ranking well on Google, it’s like having a shop with no sign on the door. At Hometown Beat , we provide expert SEO services in Carrollton, GA , Newnan, GA , and Villa Rica, GA , helping local businesses improve visibility, increase traffic, and grow with confidence. Whether you're a boutique, contractor, restaurant, or nonprofit, these seven SEO benefits can make or break your online success.

1. Higher Visibility in Google Search Results

Most people never scroll past the first page of search results. SEO helps your website climb to the top so that potential customers can find you quickly when searching for businesses in Carrollton , Newnan , or Villa Rica . More visibility means more clicks—and more business.

2. Increased Website Traffic Without Paying for Ads

Unlike paid advertising, SEO brings organic traffic —people who find you through search engines without clicking on an ad. Over time, SEO becomes more cost-effective and sustainable than pay-per-click campaigns. Your website keeps working for you 24/7.

3. Attracting High-Intent Local Customers

Local SEO targets searchers who are actively looking for services near them. When someone searches “ website design in Carrollton, GA ” or “s ocial media management near Newnan ,” your optimized content ensures you’re front and center.

4. Building Trust and Credibility

A top-ranking website with quality content and a clean structure builds trust. Google rewards well-structured websites, and your audience is more likely to engage with a business that looks credible. With Hometown Beat’s SEO services , we help your site earn that trust.

5. Improving User Experience (UX)

Google loves websites that are mobile-friendly, fast, and easy to navigate. SEO isn't just about keywords—it’s also about creating a better experience for your users, which leads to longer visits, more engagement, and higher rankings.

6. Optimizing for AI Search Assistants

As AI search tools like Google’s SGE and ChatGPT become more mainstream, SEO strategies must evolve. We now focus on answer-focused content , clear FAQ sections, schema markup, and conversational keywords—so your business shows up in both traditional search and AI-powered queries.

7. Outranking the Competition

SEO gives small businesses a powerful edge—even against national brands. By targeting geo-specific keywords like “ SEO services in Villa Rica, GA ” and building local backlinks, you can outrank competitors and dominate your niche in the West Georgia area.

�� Final Thoughts

SEO isn’t optional—it’s essential. A properly optimized website does more than show up; it converts visitors into loyal customers. At Hometown Beat , we specialize in SEO, website design , and digital marketing solutions in Carrollton, GA, Newnan, GA, and Villa Rica, GA that deliver measurable results.

�� Ready to Boost Your Rankings?

Let us audit your website and show you what’s working—and what’s not. Contact Hometown Beat today for a free local SEO evaluation and let’s put your business on the map!

SEO personas for AI search: How to go beyond static profiles

Fri, 29 Aug 2025 12:00:00 GMT

Simplified personas built as fictional characters with broad pain points are outdated in the future of search.

Think “Curious Cathy,” who just wants to learn more about your product, or “Technical Tom,” who has years of experience in his field and can handle more detailed breakdowns.

Static personas once helped, but they no longer reveal enough about real people to stay competitive.

AI search demands smarter personas

AI is getting better at understanding people – their needs, context, and intent.

As search becomes more personalized, so should your personas.

By layering in real-world data – like location, industry trends, or other environmental factors – you can create personas that reflect the actual people behind the queries.

This richer context makes your content more relevant and increases the chances that AI will surface it when your audience is searching.

The current state of SEO personas

Personas for SEO have always been how we attempt to define user intent. They cover:

Basic demographics.
Motivations.
Wants.
Likes.
Dislikes.
Emotions.
Questions.

But some personas are created only through the lens of a specific product, limiting our understanding of the people behind the queries.

Take a look at this hypothetical, simple persona of a small business owner.

Look familiar?

This persona structure is still helpful, especially for user journey mapping.

It gives you clues on tone, style, and even which questions to answer for Small Business Sara.

That’s a strong start. But in the AI-powered search landscape, tone and topics aren’t enough.

High-level and simplified persona models miss that your target customer is more than just your target customer.

They are living, breathing people, all influenced by outside factors that need to be considered when tailoring content to their needs.

So, what’s missing?

Data on Sara’s environment and what makes her a real human is missing.

You don’t know what’s going on in Sara’s world that could be influencing her intent, needs, and decision-making process when she types in a query.

When crafting our personas, ideally, we would:

Conduct user interviews and testing to help dig into Sara’s life a bit more.
Find a group of “Saras” who will provide similar answers and tell us about “her” life.

Most of us lack the time, budget, or resources to do this, though, leaving us with a knowledge gap we still need to fill.

If you gathered small business owners from similar industries and asked them to list the steps they took to start, you’d probably get similar answers.

If you think about it from a contextual perspective only, sure.

But if you listen more closely, you’ll likely notice a difference in the order they did things and what answers they found valuable at each stage.

And this order, likely influenced by their environment, impacts what they do and don’t know when they land on the content you assumed would fit their journey.

How environmental factors influence search intent

Environmental factors are going to be complex and difficult to capture in their entirety.

Let’s explore just how one piece of additional data can provide a little more insight that you might be able to leverage.

Someone in Florida, which has the highest per-capita rate of business owners , will have different needs behind the query “how to start a business” than someone in West Virginia, where that rate is the lowest.

A person in Florida is statistically more likely to know someone who’s started a business, giving them easier access to information and inspiration.

In 2024, there were just over three business applications per 100 adult residents, according to Census data.

In West Virginia, where there was only one new business application per 100 adults, the story is different.

A business owner there may have some personal connections, but starting a business is a more novel, less familiar path.

They’re more likely to need guidance on the fundamentals, like business plans and logistics.

While both Saras may search “how to start a business,” what they need from that query isn’t the same.

The Floridian Sara is probably ready for logistical steps – forming an LLC, building a website – thanks to greater exposure and access to resources.
West Virginia Sara, on the other hand, needs foundational information first: what a business plan is, how legal structures differ, or where to find funding.

Same question, different needs.

You can’t capture every detail shaping a person’s intent.

Still, even a simple environmental context like this can help you interpret it more accurately – and build personas that feel more human.

Here’s how the intent behind the keyword shifts based on this environmental knowledge:

You can argue that you have the content that ultimately fits both of these intents.

But is that content ranking for “how to start a business”? Or is it featured in LLM responses or AI Overviews?

I know my related content isn’t.

It’s ranking for “how to write a business plan” and “steps to getting an LLC,” because SEO until now has been about mastering a query, not tailoring your content to a user’s environment.

In my “deprecated” SEO strategy, I have a pillar page for “how to start a business” that covers everything from business conception to making your first sale.

I’ve got dozens of internal links with beautiful anchor text to every supplemental guide Sara needs. Google loves it.

This is where my traditional approach to content needs improvement.

While it’s written for what I consider to be the user, it’s still focused on being thorough enough that Google puts me at the top, and not considering other outside factors that might influence search needs.

How AI factors context into search responses

If you doubt that location changes the needs behind ‘how to start a business,’ compare prompts with and without location in incognito chat.

Now, take a look at how it changes when I say I live in Florida.

It’s a subtle difference, but when you say you live in West Virginia, ChatGPT suggested clarifying your business idea and proper planning as the first step.

In Florida, it dives right into business structure.

While AI doesn’t already have everyone’s environmental context in memory, when the user includes context in prompts, ChatGPT attempts to adjust its response.

Assuming everyone searching for “how to start a business” is looking for the same info might let you rank in a traditional blue-link search result.

However, this example shows that this shift to context-driven prompts influences how AI generates a response.

This is just one data point. A full strategy needs deeper environmental context than business applications or economic stats alone.

What this means for content strategies

AI search – whether through AI Overviews or LLMs – is built to reduce the cognitive overload of digging through multiple articles, clicks, and follow-up searches.

It leverages user context to deliver a succinct, relevant answer.

Take the earlier example: you could create 51 articles for every state and Washington, D.C.

In traditional search, that might help you rank for “how to start a business in (state).”

I’ve done this – customizing each article with state-specific agencies and forms – but without weaving in the deeper context of what’s happening in those states.

Traditional SEO still depends on comprehensive coverage, query mastery, and strong site structure to win blue-link rankings.
But AI retrieval and AI Overviews weigh context, clarity, and modular chunks of information that can be pulled into responses.

Both matter – they just reward different optimizations.

The goal isn’t to overhaul your entire strategy overnight.

Start testing ways to incorporate more context over time. Revisit your core content and weave in focused examples or localized insights.

Not every topic needs a “personalized” version.

Instead, find places where you can speak to your audience more directly, whether through localized callouts, customer-focused CTAs, or targeted subsections within existing content.

With clicks declining across the board, optimizing for conversion when you do get traffic is critical to proving the value of search.

Even if you’re focused on just one “Sara,” you still need to understand which end of the spectrum you’re targeting – and adjust your content to meet that audience where they are in the moment.

Digging deeper into environmental data helps you prioritize information gain – the key to becoming the most valuable answer a platform can surface.

The more clearly you understand who you’re creating for, the better positioned you are to deliver that value.

Environmental data isn’t the only input that matters. It’s one piece of a holistic persona-building approach.

The real advantage comes from layering these insights with conversion data, click and impression metrics, rankings, LLM visibility, and referrals.

Environmental context is simply one more tool in a stronger, more adaptive strategy.

Start by enhancing the data you already have

Hopefully, your personas are already rooted in data.

But what kind of SEO would I be if I didn’t ask for more data?

Adding environmental data from third-party sources helps you stand out, prioritize opportunities, and reach the right people at the right time.

It’s most powerful when combined with multiple data sources, environmental factors, and qualitative insights.

Scaling your approach

Digging into every persona or segment at once can be overwhelming.

Start small: choose one persona and one or two environmental factors to explore.
Use that real-world context to adjust a few pieces of content, then test and measure the impact.

Focus on small, incremental adjustments for long-term gains, not a full content overhaul.

The suggestions below are meant to show you where to start – not to imply you need to do it all at once.

Data exploration options

The data you need to understand environmental context rarely lives in-house.

Your BI team can share valuable insights on where you’re winning or growing, but external sources will help you transform personas into richer, more actionable profiles.

First, make sure your base persona includes core demographics such as:

Age.
Gender.
Race.
Location.
Education level.
Income.

If you’re missing these basics, start with publicly available data like the U.S. Census, Bureau of Labor Statistics (BLS), or Statista.

For example, in the business-owner scenario, I used Business Formation Statistics from the Census and population estimates .

The Census Annual Business Survey program also provides insights like:

Business owners’ age.
Education.
Country of birth.

The BLS can help you see which states are experiencing growth in new businesses , while Census tables provide demographic breakdowns for those states.

And if you know your target industry, Labor Force statistics can help you understand the age ranges most active in that space.

While this example focused on location as an environmental factor, age alone is another strong starting point.

There’s ample research on what each generation values, fears, and prioritizes at different stages of their careers.

Here’s a list of environmental questions you can aim to answer and how that might inform your strategy:

If you’re already a power creator – or have the budget – paid tools like Semrush, SimilarWeb, or Kantar can deliver deep audience insights.

If not, free sources like Census Data Tools, BLS tables, and some old-fashioned desktop research are still excellent for uncovering the data you need to better understand your audience.

Enriching personas with environmental data

Combine internal data with public sources to identify your top demographics and refine user journey mapping.

Environmental research helps you tailor content to real needs instead of covering everything broadly.

When layered with behavioral signals, technical SEO, and testing, these insights multiply visibility and conversions.

Marketers may not master the human mind, but we excel at interpreting data.

Apply that skill to strategy: focus on context, clarity, and information gain so your content becomes the answer AI and users trust.

Strong technical fundamentals still drive rankings, but environmental context boosts your chances of surfacing in AI responses.

Amid all the AI hype, the cornerstone of search is still the person behind the persona.

How to create content that works for search and generative engines

Thu, 28 Aug 2025 12:00:00 GMT

For years, optimizing content meant focusing almost entirely on Google and other traditional engines.

But with the rapid rise of generative AI chatbots – tools that don’t just link but summarize – content strategies need to adapt.

It’s no longer enough to rank in search; you now need to be referenced, cited, and surfaced by AI systems as well.

This shift raises a critical question: how do we write content that satisfies both worlds – Google’s algorithmic complexity and AI’s citation-driven simplicity?

Where users are going: Search vs. generative AI

Statcounter’s Search Engine Market Share data suggests that Google is still firmly on top, but those numbers only measure traditional search engines.

They don’t reflect the growing number of users turning to AI chatbots for answers, which is why separate measures – like AI Chatbot Market Share – are starting to emerge.

For now, the two data sets can’t be directly compared.

AI chatbots generated 34 times fewer visits than search engines, per One Little Web’s April 2025 study. Even so, chatbot traffic grew 80.9% between April 2024 and March 2025.

They may still be the underdog, but the growth trajectory is hard to ignore.

By June 2025, Chillibyte found that ChatGPT alone had attracted 55.2 billion visits in that same 12-month period – an 80% year-over-year increase .

Chillibyte noted that chatbots seem to be supplementing search engines rather than replacing them.

Still, divided usage between search engines and AI will inevitably chip away at Google’s overall share.

That’s why content strategies now need to account for both search engines and generative AI.

Dig deeper: How to optimize your 2025 content strategy for AI-powered SERPs and LLMs

How search rankings differ from AI citations

Creating content for both search and AI starts with understanding how each cites or ranks information.

Search engines: Simple inputs, complex algorithms

Search engines typically receive simpler input and deliver simple output.

For example, the keywords you type into Google are usually shorter and less complex than an AI prompt.

Google’s output is also simpler. (This is changing with AI Mode, though. We’ll focus here on traditional output rather than modern AI-driven results.)

Typical search engine input and output:

A simple query usually produces a simple list of URLs.

But does that mean search engines are less complex than their generative AI counterparts?

Not at all.

While input and output may look simple, search engines are algorithmically complex in determining what that output should be.

Once a query is processed, layered algorithms define the results. Users can’t see this layer, but it’s always at work. A search engine may consider:

Content relevance, including metadata, headings, and keyword use.
Content quality, trustworthiness, and authority (e.g., Google’s E-E-A-T ).
Popularity signals such as inbound links.
Link quality, with filters against spammy practices.
Pricing for product results.
Local signals.
Schema and structured data.
Reviews and testimonials.

Over the years, Google has evolved to disqualify pages that rely on manipulative techniques.

Algorithm updates like Penguin (targeting spammy link practices), Caffeine, Panda, Mayday, and Farmer were eventually folded into Google’s core algorithms.

The point is clear: Google remains algorithmically complex.

Generative engines: Complex prompts, simpler filters

In contrast, AI chatbots handle richer input and produce more complex output.

Their defensive algorithmic layer, however, is far less developed than that of leading search engines.

Simply put, generative engines have had less time to build the protective algorithms that surround vectorized, LLM-based response systems.

Here’s an example of typical generative engine input and output:

Both the input and the output are clearly more complex.

That said, generative engines are usually algorithmically simpler.

Yes, they can crawl the web for new information when their LLM lacks the required data.

But they’re far less sophisticated in determining which information to surface as authoritative or trustworthy.

A study I published here on Search Engine Land shows that AI RAG agents rely on far fewer sources than search engines.

Generative engines also place greater value on co-citations – mentions of a term alongside a brand, even without a link – than on traditional backlinks.

This presents new opportunities for content creation and placement.

Many tactics that Google’s algorithmic complexity has pushed aside could be adapted and repurposed to earn AI citations and traffic.

In that sense, AI offers a fresh marketing opportunity.

AI may not yet command the lion’s share of traffic – but the traffic it does generate is, in many ways, more accessible.

That’s why it’s essential to include AI in your marketing mix and consider how to produce content that satisfies both search engines and generative engines.

Dig deeper: Why your best content is invisible to AI search engines (and how to fix it in 30 minutes)

Producing content that serves search and generative engines

Fortunately, much of what search engines prefer also works well for generative AI. For native content:

Create well-structured pages with clear headings, supporting statistics, and rich media.
Ensure content is accessible, even with JavaScript disabled. (Less of a problem for Google now, but AI crawlers are less sophisticated.)
Build strong E-E-A-T signals for your content, authors, and brand.
Address code elements such as alt text and metadata so they properly support your content.
Use accessible, indexable URLs with unique, descriptive slugs.
Cover topics in depth, including related keywords and common questions. Repeating exact-match keywords is no longer effective for SEO and even less so for AI. Instead, use semantic richness and engage with broader search entities.
Include Q&A or answer-style formatting, since AI prompts are often phrased as questions.
Don’t hide primary content (e.g., in linked PDFs).
Provide summaries and key takeaways.

Because AI interrogates vectorized information, additional techniques apply:

Co-citations are often more valuable than hyperlinks. Being cited alongside key terms on a high-authority site can be more effective for AI than earning a traditional link.
External placement may help. Even without a link, branded citations can improve the likelihood of AI recognition.
Consistency matters. AI blends vectorized information, so once citations enter the LLM, they may no longer be treated as separate documents. Consistent details (addresses, phone numbers, etc.) remain essential – don’t let local SEO slip.
Disambiguation works differently for AI. In SEO, trying to clarify that you don’t provide a service can ironically increase rankings for that service. AI, however, is better at separating negatives from positives.
Structured summaries are especially valuable. While they also help search engines, generative AI relies more heavily on them when interpreting main content.
Keep critical information concise. Short, precise statements of facts or data vectorize more effectively.
Shorter search terms still matter. RAG agents often distill compact terms from complex prompts, making FAQ-style content important for both SEO and AI.

Dig deeper: The search visibility framework: Dominating every corner of the SERP in 2026

How generative AI is quietly distorting your brand message by Semrush Enterprise

Thu, 28 Aug 2025 11:00:00 GMT

Your brand message is no longer entirely yours to control.

AI systems have become storytellers, shaping how consumers discover and understand your brand. Every customer review, social media post, news mention, and errant leaked internal document can feed AI models that generate responses about your company.

When these AI-generated narratives drift from your intended brand message, a phenomenon we can define as AI brand drift , the results can be devastating.

Your official brand voice, customer complaints, and leaked memos are LLM fuel. AI synthesizes everything into responses that millions of consumers encounter daily.

Your brand messaging competes with unfiltered customer sentiment and information that was never meant for public consumption. AI-driven misrepresentations can instantly reach global audiences through search results, chatbot interactions, and AI-powered recommendations. Mixed brand signals can reshape how AI systems describe your company for years to come.

This guide will show you how to identify AI brand drift before it damages your market position and provide actionable strategies for regaining control.

The complete brand spectrum: 4 layers you can’t afford to ignore

Large language models aggregate every available signal about your brand, turn around, and synthesize authoritative-sounding responses that consumers accept as fact. Companies confirm that phantom features proposed by ChatGPT cause support tickets, but are also considered part of the product roadmap.

This is the case for the company Streamer.bot:

“We often have users joining our Discord and say ChatGPT told said xyz. Yes the tool can,however their instructions are wrong 90% of the time. We end up correcting their attempts to get it working how they want, still creates support tickets.”

Brand stewardship now requires managing four distinct but interconnected layers. Each layer feeds AI training data differently. Each carries different risk profiles. Ignore any layer, and AI systems will construct your brand narrative without your input.

The Brand Control Quadrant frames these layers:

Key insight: AI reconstructs your brand from all accessible layers. AI co-authors brand narratives.

Here’s a concrete example: BNP Parisbas’ logo is contextualized by Perplexity.ai using a “Bird Logos Collection Vol.01” Pinterest board.

From technical flaw to brand crisis

“Semantic drift describes the phenomenon wherein generated text diverges from the subject matter designated by the prompt, resulting in a growing deterioration in relevance, coherence, or truthfulness.” – A., Hambro, E., Voita, E., & Cancedda, N. (2024). Know When To Stop: A Study of Semantic Drift in Text Generation .

When AI-generated content gradually strays from your brand’s intended message, meaning, or facts as it unfolds, you know you are dealing with a brand drift crisis . This can take several forms:

Factual drift: The model starts out as factual but introduces inaccuracies as the conversation progresses.
Intent drift: Facts are retained, but the underlying intent or nuance is lost, leading to brand misrepresentation or confusion with competitors.
Shadow brand drift: AI-powered search may surface outdated product specs, misquote leadership, or reveal elements meant for internal communication only.

Key insight: Even well-trained AI can quickly undermine brand clarity, consistency, and trust if not closely managed.

This can also create cybersecurity issues. Netcraft published a study concluding that 1 in 3 AI-generated login URLs could lead to phishing traps. Between fake features and dodgy login pages, monitoring is key!

How AI brand drift unfolds

LLMs generate text sequentially, with each new word based on the prior context. There’s no “master plan” for the entire output, so drift is inherent.

Most factual or intent drift occurs early in the output, according to a 2024 study of semantic drift in text generation. Errors are compounded in multi-turn conversations : initial misunderstandings are amplified and rarely corrected without a context reset (starting a new conversation for example).

Marketers must be aware that they face critical vulnerabilities, identified by leading experts at Meta and Anthropic:

Loss of coherence: This manifests as diminished clarity, disrupted logical progression, and a breakdown in self-consistency within the narrative.
Loss of relevance: This occurs when content becomes saturated with irrelevant or repetitive information, diluting the intended message.
Loss of truthfulness : This is characterized by the emergence of fabricated details or statements that diverge from established facts and world knowledge.
Narrative collapse : When AI outputs are used as new training data, the original intent can morph entirely.
Zero-click risk : With Google AI Overviews becoming the default in search, users may never see your official content. They would rely only on the AI’s synthesized, potentially drifted version.

AI-generated content sounds plausible and on-brand but could subtly distort your message, values, or positioning. This drift can erode brand equity, undermine consumer trust, and potentially introduce compliance risks.

The hidden driver of drift

The shadow brand is the sum of internal, proprietary, or outdated digital assets your organization has created but not intentionally exposed:

Onboarding documents.
Internal wikis.
Old presentations.
Partner enablement files.
Recruitment PDFs.
And any other information that is not meant for public consumption.

If these are accessible online (even buried), they are “trainable” by LLMs. If it’s online, it’s fair game for LLMs (even if you never meant it to be public).

Shadow assets are often off-message. Outdated or inconsistent materials can actively shape AI-generated answers, introducing narrative drift. Most teams do not track their shadow brand, leaving a major gap in their narrative defense.

From drift to distortion: The brand risk matrix

Regaining brand narrative control

You should audit and map all four brand layers:

Known Brand: Ensure all official assets are up-to-date, accessible, and semantically clear. Create a “brand canon,” a centralized, authoritative source of facts, messaging, and positioning, optimized for AI consumption.
Latent Brand: Monitor UGC, community forums, and cultural signals; use social listening to spot emerging themes.
Shadow Brand: Conduct regular audits to identify and secure or update internal docs, old presentations, and semi-public files.
AI-Narrated Brand: Track how AI platforms summarize and present your brand across search, chat, and discovery. Implement LLM observability along with methods to detect when AI-generated content diverges from brand intent.

Lead the AI brand narrative

Brand is no longer just what you say, it’s what AI (and your customers) says about you. In the generative search era, narrative control is a continuous, cross-functional discipline.

Marketing teams must actively manage all four layers, own the shadow brand, and measure semantic drift. Track how meaning and intent evolve in AI outputs in order to establish rapid responses to correct drifted narratives, both in AI and in the wild.

As Philip J. Armstrong, GTM Head of Insights & Analytics at Semrush, puts it, “Keeping an eye on brand drift protects your hard-earned brand reputation as consumers move to AI to evaluate products and services.”

The fractured future of search: New rules for SEO in the AI age

Wed, 27 Aug 2025 15:33:00 GMT

Search isn’t dying or being replaced . It’s fracturing.

Consumers aren’t quitting search ; they’re just seeing other people.

Google still dominates, but it’s like that long-term relationship that’s gotten, well, complicated. Too many ads, too much clutter, a few questionable AI answers – and suddenly users are sneaking off to hang out with ChatGPT and Claude.

This doesn’t mean extinction for SEO. It means the honeymoon’s over – and you need to learn some new moves.

The job now? It’s to play offense in a fractured search landscape.

The following insights come from Kelsey Libert , co-founder of Fractl, who shared them during her webinar and SMX Advanced presentation, How AI is Reshaping SEO: Challenges, Opportunities, and Brand Strategies for 2025 .

New rule: The market won’t wait for marketers

Fractl’s survey of 2,000+ people shows how quickly things are shifting:

Two-thirds of consumers believe AI will replace traditional search within five years.
82% say AI-powered search is more helpful than Google’s SERPs.
Only 8% hadn’t heard of ChatGPT. Nearly half said they explore AI tools as soon as they launch.
74% feel confident using AI, and more than half say it makes them more creative, productive, and improves their quality of life.

Translation: This isn’t just hype. We’re watching a massive shift, and consumers are already redecorating their digital lives with AI.

New rule: Don’t fear AI content. Fear mediocre content.

Let’s kill this myth that AI content is automatically untrustworthy.

Because here’s the thing: 80% of consumers are neutral or positive toward brands using AI-generated content.

What people don’t trust? Garbage content. You know, AI slop.

What people trust is useful, accurate, authoritative content – whether a human, a robot, or a robot supervised by a human (or even a human supervised by a robot) wrote it.

The winning formula?

Feed AI your proprietary data.
Add your subject matter expertise.
Edit like you mean it.

This combination can produce better content than what an average writer can crank out alone.

The bar isn’t “is this AI?”

The bar is “is this good?”

New rule: Don’t just use AI to cram more work into the week

One in four executives are saving about seven hours a week with AI. That’s almost a full workday.

But here’s the catch: if your brilliant plan is to take that time and just stuff more crap on your employees’ plates? Congratulations! You’ve turned AI into a burnout machine.

The smarter play? Use AI to ease pressure, not increase it.

Because nothing’s more expensive than replacing burned-out talent. Leaders who get this will keep their best people, their best ideas, and their sanity. That’s a competitive edge no tool can replicate.

New rule: Treat AI like the gold rush it is

AI is like the early days of SEO. It’s a gold rush.

The playing field is wide open. The rewards for moving fast have never been higher.

Yes, the incumbents have resources. But smaller, scrappier brands can move faster than the giants.

The brands that experiment now, take risks, and learn how to win attention in fractured search will walk away with the treasure.

New rule: Play offense, not defense

AI isn’t killing SEO. It’s:

Reshaping search.
Redistributing attention.
Redefining how people discover information.

The only thing AI is killing? Those who cling to their old playbooks.

Treat AI as a discovery channel, not a death sentence.
Use it to amplify your brand, not replace it.
Make it a leadership tool to reduce burnout, not fuel it.

The question isn’t whether AI will fracture search. It already has.

The question is: are you ready to go on offense?

Watch: How AI is Reshaping SEO: Challenges, Opportunities, and Brand Strategies for 2025

Below is Libert’s full webinar presentation.

The search visibility framework: Dominating every corner of the SERP in 2026

Wed, 27 Aug 2025 12:15:00 GMT

Search is having its biggest identity shift in decades, and your SEO strategy has got to keep up.

Now, the reality is that even when you win a top organic spot, you’re competing against AI Overviews that kill clicks before a user ever visits your site.

The good news is that while the SERP is more complex, it’s also more abundant.

The brands that are winning are orchestrating total visibility across multiple touchpoints so they capture every high-intent moment.

That’s exactly what the search visibility framework is built to do, and it’s my blueprint for thriving in today’s AI-driven, multi-format search landscape.

This article explores the framework, starting with its core principles, then breaking down its five pillars and ways to measure their impact.

The search visibility framework: Winning across every touchpoint

The search visibility framework is about omnipresence, and owning multiple placements on page one so you capture every high-intent moment:

I want to know… (research moment).
I want to go… (local intent).
I want to do… (how-to or action-based).
I want to buy… (purchase intent).

The goal is simple: appear in as many SERP features as possible for your target queries, including:

Paid ads ( PPC ).
Local services ads (LSAs).
Map Pack (Google Business Profile).
Organic listings.
Featured snippets.
AI Overviews.
People Also Ask (PAA).
Videos.
Ecommerce/shopping (if applicable).
“Things to Know” and “What People Are Saying” modules.

This approach to dominating the SERPs can:

Maximize market coverage: When done well, you can appear in multiple slots in the SERP for the same query (for example, ad + local + organic + snippet).
Diversify leads: As a result, you can generate traffic from intent-rich queries across multiple entry points.
Build brand authority: Psychologically dominate the SERP, reinforcing trust and market leadership.
Create search resilience: If one channel dips, others fill the gap.

Is this a brand-new concept? Not necessarily.

However, brands need to push this type of strategy even harder in the new age of SEO to even have a chance to compete, especially when AI Overviews are stealing the scene.

The growing need for total SERP coverage

By now, we’re all keenly aware of the impact Google’s AI Overviews is having on the search results.

Marketing teams are feeling the impact of yet another Google SERP feature that elicits zero clicks.

Up to 58% of people conducted at least one Google search in March 2025 that triggered an AI Overviews summary, Pew Research Center found . What did people do?

They only clicked on a traditional result in 8% of searches with an AI summary, which was about half of the 15% click rate on pages without a summary.

According to the research, only 1% of all visits lead to clicks within the Google AI Overviews summaries, and people were more likely to stop browsing when AI Overviews were present.

Search Engine Land’s Danny Goodwin reported Google’s response. They called the Pew research “flawed.”

“This study uses a flawed methodology and skewed queryset that is not representative of Search traffic. We consistently direct billions of clicks to websites daily and have not observed significant drops in aggregate web traffic as is being suggested.”

Without data from Google, we’re left guessing if what they say is true. However, some research suggests there may be an increase in clicks in certain scenarios.

For example, this study from Terakeet found that being included in Google’s AI Overviews can significantly boost clicks, especially for transactional queries.

And, TrustRadius found that 90% of B2B buyers clicked on sources featured in AI Overviews.

The real challenge now is showing up in AI Overviews and holding prime positions across the rest of the SERP.

The search visibility framework is built for exactly this. It ensures your brand stays visible in every scenario, with or without AI Overviews.

Here’s how it works.

The 5 pillars of the search visibility framework

The search visibility framework is a methodical approach to dominating five key areas of modern SEO: content, organic search, paid search, local search, and user experience.

Each pillar plays a unique role, but the real power comes from how they work together to make your brand more visible in the SERPs.

1. Content

Within the search visibility framework, content is the connective tissue that feeds every other pillar.

Helpful content that:

Is strategically structured for the SERP improves organic rankings.
Strengthens paid ad landing pages.
Boosts local profile relevance.
Supports a smoother user experience.

2. Organic search

Traditional SEO is still the foundation for competing in the search results. It ensures your brand is discoverable across a wide range of queries.

When you maintain strong technical health, start with precise keyword targeting and use appropriate on-page optimization, SEO builds the relevance needed for every other pillar.

3. Paid search

Paid search continues to deliver fast visibility for competitive queries where organic placements are tougher to win quickly.

Within the framework, paid search works to fill coverage gaps and maximize exposure for those commercial micromoments.

4. Local search

Local SEO provides geographic dominance to your SERP strategy.

5. User experience

User experience ensures that every click, whether it’s from AI Overviews, organic, paid, or local, has the best chance of turning into a conversion. Here, traditional SEO matters.

Together, these five pillars create a multifaceted approach:

Content builds relevance.
Traditional SEO secures long-term rankings.
Paid campaigns capture immediate opportunities.
Local search wins high-intent nearby customers.
User experience strategies keep visitors engaged.

When these elements work in unison, they compound visibility in the SERP.

Tracking the signals of SERP wins

Classic rankings data still has its place. However, it’s not enough on its own.

To measure the search visibility framework’s ROI, you need metrics that track your footprint across AI Overviews, SERP features, and funnel stages.

Beyond raw clicks, look for signals of influence like assisted traffic, brand mentions, and engagement, especially when click-through rates drop but your brand still appears prominently.

Key metrics to monitor:

Executive KPIs: Business outcomes, leads, impressions, and share of voice.
Paid performance: CPC, conversions, and ROAS.
Local performance: Map Pack visibility, calls, reviews, and Local Pack rankings.
Organic performance: Clicks, CTR, snippet wins, and content rankings.
Content features: PAA coverage, video impressions, and FAQ schema results.
Website health: Core Web Vitals scores, page speed, and schema errors.
SERP coverage: Percent of features owned per target query.
Visibility weight score: Weighted by SERP feature prominence.
Conversion efficiency: How well traffic turns into leads/sales.
Funnel stage visibility: TOFU content reach, MOFU conversion rates, BOFU decision support.
AI SERP-specific analytics: Citation tracking, engagement in AI Overviews.

You can consolidate all of this into a single view with a custom SEO dashboard in Looker Studio (I share examples in my article here on Search Engine Land).

Armed with this data, you can re-optimize underperforming assets and reallocate efforts.

Going from uncertainty to opportunity

Search is not a straight path from query to click. Users and website publishers face a maze of AI answers and competing SERP features.

However, this doesn’t have to mean invisibility.

This framework is one way to re-engineer your SEO program to capitalize on every opportunity the SERP offers, rather than focusing on what’s been lost.

ChatGPT’s answers came from Google Search after all: Report

Tue, 26 Aug 2025 15:33:00 GMT

Multiple tests have suggested ChatGPT is using Google Search . Well, a new report seems to confirm ChatGPT is indeed using Google Search data.

OpenAI quietly used (and may still be using) a Google Search scraping service to power ChatGPT’s answers on real-time topics like news, sports, and finance, according to The Information.

The details. OpenAI used SerpApi, an 8-year-old scraping firm, to extract Google results.

SerpApi reportedly listed OpenAI as a customer on its site as recently as May 2024. That listing was later removed for unknown reasons. ( Here’s evidence via the Wayback Machine .)

Google has reportedly long tried to block SerpApi’s crawler, though it’s unclear how effective those efforts have been.
Other SerpApi customers reportedly include Meta, Apple, and Perplexity.

Zoom out. This revelation contrasts with OpenAI’s public stance that ChatGPT search relies on its own crawler, Microsoft Bing, and licensed publisher data.

During Google’s antitrust trial, ChatGPT exec Nick Turley indicated Microsoft’s Bing had “significant quality issues” and indicated that Google’s index would accelerate OpenAI’s search ambitions.

Meanwhile. OpenAI CEO Sam Altman recently dismissed Google Search , saying:

“I don’t use Google anymore. I legitimately cannot tell you the last time I did a Google search.”

Well, based on this news, it seems like he probably is using Google Search all the time within his own product.

Why we care . Google’s search index remains the foundation of online discovery – so much so that even its biggest AI search rival appears to be using it to partially power ChatGPT. This is yet another reminder that SEO isn’t going anywhere just yet. If Google’s results are valuable to OpenAI, they remain essential for driving visibility, traffic, and business outcomes.

The report. OpenAI Is Challenging Google—While Using Its Search Data (subscription required)

Historic recurrence in search: Why AI feels familiar and what’s next

Tue, 26 Aug 2025 13:00:00 GMT

Historic recurrence is the idea that patterns repeat over time, even if the details differ.

In digital marketing , change is the only constant.

Over the last 30 years, we’ve seen nonstop shifts and transformations in platforms and tactics.

Search, social, and mobile have each gone through their own waves of evolution.

But AI represents something bigger – not just another tactic, but a fundamental shift in how people research, evaluate, and buy products and services.

Estimates vary, but Gartner projects that AI-driven search could account for 25% of search volume by the end of 2026.

I suspect the true share will be much higher as Google weaves AI deeper into its results.

For digital marketers, it can feel like we need a crystal ball to predict what’s next.

While we don’t have magical foresight, we do have the next best thing: lessons from the past.

This article looks back at the early days of search, how user behavior evolved alongside technology, and what those patterns can teach us as we navigate the AI era.

The early days: Wild and wonderful queries

If you remember the early web – AltaVista, Lycos, Yahoo, Hotbot – search was a free-for-all.

People typed in long, rambling queries, sometimes entire sentences, other times just a few random words that “felt” right.

There were no search suggestions, no “people also ask,” and no autocorrect.

It was a simpler time, often summed up as “10 blue links.”

Searchers had to experiment, refine, and iterate on their own, and the variance in query wording was huge.

For marketers, that meant opportunity.

You could capture traffic in all sorts of unexpected ways simply by having relevant pages indexed.

Back then, SEO was, in large part, about one thing: existing in the index.

Dig deeper: A guide to Google: Origins, history and key moments in search

Google’s rise: From exploration to efficiency

Anyone working in digital marketing in the early 2000s will remember.

From Day 1, Google felt different. The quality of its results was markedly better.

Then came Google Suggest in 2008, quietly changing the game.

Suddenly, you didn’t have to finish typing your thought. Google would complete it for you, based on the most common searches.

Research from Moz and others at the time showed that autocomplete reduced query length and variance.

People defaulted to Google’s suggestions because it was faster and easier.

This marked a significant shift in our behavior as searchers. We moved from sprawling, exploratory queries to shorter, more standardized ones.

It’s not surprising. When something can be achieved with less effort, human nature drives us toward the path of least resistance.

Once again, technology had changed how we search and find information.

Mobile, voice, and the second compression

The shift to mobile accelerated this compression.

Tiny keyboards and on-the-go contexts meant people typed as little as possible.

Autocomplete , voice input, and “search as you type” all encouraged brevity.

At the same time, Google kept rolling out features that answered questions directly, creating a blended, multi-contextual SERP.

The cumulative effect? Search behavior became more predictable and uniform.

For marketers running Google Ads or tracking performance in Google Analytics and Search Console , this shift came with another challenge: less data.

Long-tail keywords shrank, while most traffic and budget concentrated on a smaller set of high-volume terms.

Once again, our search behavior – and the insights we could glean from it – had evolved.

Zero-click search and the walled garden

By the late 2010s, zero-click searches were on the rise.

Google – and even social platforms – wanted to keep users inside their ecosystems.

More and more questions were answered directly in the search results.

Search got smarter, and shorter queries could deliver more refined results thanks to personalization and past interactions.

Google started doing everything for us.

Search for a flight? You’d see Google Flights.

A restaurant? Google Maps.

A product? Google Shopping.

Information? YouTube .

You get the picture.

For businesses built on organic traffic, this shift was disruptive.

But for users, it felt seamless – arguably a better experience, even if it created new challenges for optimizers.

Quality vs. brevity

This shift worked – until it didn’t.

One common complaint today is that search results feel worse .

It’s a complicated issue to unpack.

Have search results actually gotten worse?
Or are the results as good as ever, but the underlying sites have declined in quality?

It’s tricky to call.

What is certain is that as traffic declined, many sites got more aggressive – adding more ads, more pop-ups, and sneakier lead gen CTAs to squeeze more value from fewer clicks.

The search results themselves have also become a bewildering mix of ads, organic listings, and SERP features.

To deliver better results from shorter queries, search engines have had to guess at intent while still sending enough clicks to advertisers and publishers to keep the ecosystem running.

And as traffic-starved publishers got more desperate, user experience took a nosedive.

Anyone who has had to scroll through a food blogger’s life story – while dodging pop-ups and auto-playing ads – just to get to a recipe knows how painful this can be.

It’s this chaotic landscape that, in part, has driven the move to answer engines like ChatGPT and other large language models (LLMs).

People are simply tired of panning for gold in the search results.

The AI era: From compression back to conversation

Up to this point, the pattern has been clear: the average query length kept getting shorter.

But AI is changing the game again, and the query-length pendulum is now swinging sharply in the opposite direction.

Tools like ChatGPT, Claude, Perplexity, and Google’s own AI Mode are making it normal to type or speak longer, more detailed questions again.

We can now:

Ask questions instead of searching for keywords.
Refine queries conversationally.
Ask follow-ups without starting over.

And as users, we can finally skip the over-optimized lead gen traps that have made the web a worse place overall.

Here’s the key point: we’ve gone from mid-length, varied queries in the early days, to short, refined queries over the last 12 years or so, and now to full, detailed questions in the AI era.

The way we seek information has changed once more.

We’re no longer just searching for sources of information. We’re asking detailed questions to get clear, direct answers.

And as AI becomes more tightly integrated into Google over the coming months and years, this shift will continue to reshape how we search – or, more accurately, how we question – Google.

Dig deeper: SEO in an AI-powered world: What changed in just a year

AI and search: Google playing catch-up

Google was a little behind the AI curve.

ChatGPT launched in late 2022 to massive buzz and unprecedented adoption.

Google’s AI Overviews – frankly underwhelming by comparison – didn’t roll out until mid-2024.

After launching in the U.S. in mid-June and the U.K. in late July 2025 , Google’s full AI Mode is now available in 180 countries and territories around the world.

Now, we can ask more detailed, multi-part questions and get thorough answers – without battling through the lead gen traps that clutter so many websites.

The reality is simple: this is a better system.

This is progress.

Want to know the best way to boil an egg – and whether the process changes for eggs stored in the fridge versus at room temperature? Just ask.

Google will often decide if an AI Overview is helpful and generate it on the fly, considering both parts of your question.

What is the best way to boil an egg?
Does it differ if they are from the fridge?

The AI Overview answers the question directly.

And if you want to keep going, you can click the bold “Dive deeper in AI Mode” button to continue the conversation.

Inside AI Mode, you get streamlined, conversational answers to questions that traditional search could answer – just without the manual trawling or the painfully over-optimized, pop-up-heavy recipe sites.

From shorter queries to shorter journeys

Stepping back, we can see how behavior is shifting – and how it ties to human nature’s tendency to seek the path of least resistance.

The “easy” option used to be entering short queries and wading through an increasingly complex mix of results to find what you needed.

Now, the path of least resistance is to put in a bit more effort upfront – asking a longer, more refined question – and let the AI do the heavy lifting.

A search for the best steak restaurant nearby once meant seven separate queries and reviewing over 100 sites. That’s a lot of donkey work you can now skip.

It’s a subtle shift: slightly more work up front, but a far smoother journey in return.

This change also aligns with a classic computing principle: GIGO – garbage in, garbage out.

A more refined, context-rich question gives the system better input, which produces a more useful, accurate output.

Historic recurrence: The pattern revealed

Looking back, it’s clear there’s a repeating cycle in how technology shapes search behavior.

The early web (1990s)

Behavior: Long, experimental, often clumsy queries.
Why: No guidance, poor relevance, and lots of trial-and-error.
Marketing lesson: Simply having relevant content was often enough to capture traffic.

Google + Autocomplete (2000s)

Behavior: Queries got shorter and more standardized.
Why: Google Suggest and smarter algorithms nudged users toward the most common phrases.
Marketing lesson: Keyword targeting became more focused, with heavier competition around fewer, high-volume terms.

Mobile and voice era (2010s–early 2020s)

Behavior: Even shorter, highly predictable queries.
Why: Tiny keyboards, voice assistants, and SERP features that answered questions directly.
Marketing lesson: The long tail collapsed into clusters. Zero-click searches rose. Winning visibility meant optimizing for snippets and structured data.

AI conversation era (2023–present)

Behavior: Longer, natural-language queries return – now in back-and-forth conversations.
Why: Generative AI tools like ChatGPT, Gemini, and Perplexity encourage refinement, context, and multi-step questions.
Marketing lesson: It’s no longer about just showing up. It’s about being the best answer – authoritative, helpful, and easy for AI to surface.

Technology drives change

The key takeaway is that technology drives changes in how people ask questions.

And tactically, we’ve come full circle – closer to the early days of search than we’ve been in years.

Despite all the doom and gloom around SEO, there’s real opportunity in the AI era for those who adapt.

What this means for SEO, AEO, LLMO, GEO – and beyond

The environment is changing.

Technology is reshaping how we seek information – and how we expect answers to be delivered.

Traditional search engine results are still important. Don’t abandon conventional SEO.

But now, we also need to optimize for answer engines like ChatGPT, Perplexity, and Google’s AI Mode.

That means developing deeper insight into your customer segments and fully understanding the journey from awareness to interest to conversion.

Talk to your customers.
Run surveys.
Reach out to those who didn’t convert and ask why.

Then weave those insights into genuinely helpful content that can be found, indexed, and surfaced by the large language models powering these new platforms.

It’s a brave new world – but an incredibly exciting one to be part of.

Don’t Make These 3 Mistakes on Your Business Instagram Page

Mon, 25 Aug 2025 13:52:43 GMT

Running a small business in West Georgia means being part of a tight-knit, loyal community—but if your Instagram presence isn’t working, you could be missing major opportunities to grow. At Hometown Beat , we help businesses shine online with professional social media management solutions in Carrollton, GA , and beyond. Instagram is one of the most powerful tools in your digital marketing toolbox—if you’re using it the right way.

Here are three of the biggest Instagram mistakes we see local businesses make —and what to do instead.

�� Mistake #1: Inconsistent Branding & Messaging

You only get a few seconds to make an impression when someone lands on your profile. If your posts feel disjointed, unprofessional, or “off-brand,” you’re losing trust before the conversation even starts.

What to do instead:
Work with a social media marketing agency in Carrollton, GA (like us!) to create a consistent brand kit—colors, fonts, tone of voice, photo style—that reflects your business personality and values. Schedule your posts with clear themes, and make sure your bio and link-in-bio reflect your current offerings.

�� Mistake #2: Ignoring Engagement Opportunities

Instagram isn’t just a place to post pretty pictures—it’s a two-way conversation. If you’re not replying to comments, engaging in stories, or responding to DMs, you’re missing chances to build loyalty and community.

What to do instead:
Make engagement part of your daily routine or hand it off to a professional social media manager who knows your voice. Ask questions in your captions, run polls in stories, and actively respond to your audience. Instagram’s algorithm rewards authentic engagement—and so do your customers.

�� Mistake #3: Posting Without a Strategy

Many business owners post randomly—when they have time, when they remember, or when inspiration hits. Without a clear content strategy tied to your goals, your Instagram presence becomes noise instead of a lead-generating machine.

What to do instead:
Create a monthly content calendar focused on local relevance, offers, client highlights, and educational posts. At Hometown Beat , we offer custom social media management solutions in Carrollton, GA that align with your business goals, track engagement metrics, and actually move the needle for your brand.

✅ Let’s Build a Stronger Instagram Presence Together

If you're making any of these mistakes, don’t worry—we’re here to help. At Hometown Beat , we’ve helped businesses across West Georgia turn their Instagram pages into true marketing engines with tailored content, engagement strategies, and branded design.

�� Contact us today for a free social audit and let’s make your Instagram work harder, smarter, and better for your business.

�� Serving Carrollton, GA and surrounding West Georgia communities

Why community is the antidote to AI overload in search marketing

Mon, 25 Aug 2025 13:00:00 GMT

In 2025, people aren’t just searching for answers anymore.

They’re looking for genuine responses from the people they trust most:

Creators.
Communities.
Fellow brand supporters.

In many ways, community has become an algorithm of its own.

AI-powered tools like Google Gemini , ChatGPT , and Perplexity have made knowledge more accessible than ever.

But in doing so, they’ve also flattened it.

Answers feel repetitive, citations pull from the same limited sources, and brand voices risk becoming interchangeable.

That’s where community comes in.

While generative AI commoditizes information, community restores individuality.

It offers what no model can compress into tokens:

Authentic connection.
Lived experience.
Trust.

When democratized information becomes homogenized

I can still remember when Google – and later YouTube – made information feel democratized, putting knowledge at our fingertips like never before.

But with the rise of AI, that same accessibility now comes at a cost: everything starts to sound the same.

Every brand competing for similar keywords risks becoming interchangeable, sounding the same in AI-generated summaries that deliver information without distinction.

Meanwhile, authority is concentrated into a small set of repeatedly cited sources, so users encounter little variation in what LLMs surface.

In some ways, this is similar to traditional SEO .

But there’s an important difference: websites once gave us the chance to “get our brand over” and show what made our solution unique.

That’s what feels lost in an AI-driven search experience.

Still, within this sameness lies opportunity.

While brands fight for visibility inside an AI Overview, those with the strongest communities can not just stand apart – but truly stand alone.

Dig deeper: SEO for user activation, retention and community

Community as the differentiator

AI responses are built around compression – getting audiences to an answer as quickly and concisely as possible.

Community, on the other hand, expands.

AI platforms tend to generalize first, then personalize only when prompted.

Community works the opposite way: it personalizes from the start.

In my view, that’s the kind of user experience audiences will ultimately prefer – and it’s how brands will become the choice within their niche.

Think about:

A Reddit thread that discusses your product specifically. That’s not just another citation. It’s a living testimonial, open to being challenged or reinforced in real time.
A Discord server filled with engaged users doesn’t just provide customer support – it showcases the culture and identity your brand is building.
Social comment threads around a creator’s content show personality, emotion, and authenticity that no LLM can replicate.

Ultimately, your community gives your brand the one thing AI can’t compress or flatten into tokens: individuality.

In a world of sameness, community is what gives your brand its voice back.

UGC? Hello, UGT!

User-generated content (UGC) has long been viewed as central to search marketing – a key driver of discoverability.

That’s still true, but the conversation has matured.

It’s no longer just about “content.” What truly matters now is user-generated trust (UGT).

This may sound like a subtle mindset shift, but it changes everything.

Search marketing teams should focus on the real, ongoing conversations within communities that validate products and learn how to leverage those conversations wherever possible.

That’s where genuine user advocacy emerges. And it’s advocacy that increasingly shows up in SERPs and AI responses.

Whether it’s a YouTube video featured in search results, a Reddit thread highlighted in an AI answer, or a TikTok creator’s series, UGT creates organic momentum.

It sends signals to both people and algorithms that your brand is credible, trustworthy, and the preferred choice.

Backlinks can be gamed, and citations scraped or manufactured.

UGC is about output. And UGT? It’s about advocacy and credibility – and that’s exactly what search marketing teams need to drive lasting results.

Dig deeper: Advanced tactics to maximize the SEO value of user-generated content

How owned and earned communities build trust

When thinking about your brand community, there are two key considerations.

Owned communities

These are spaces where conversations and culture consistently reinforce or evolve your positioning and shape how you’re perceived.

They include:

Brand Discord channels.
Slack groups.
Reddit forums.

Earned communities

These are the building blocks your brand participates in – where authenticity can either strengthen or undermine trust.

Think of:

Reddit threads not owned by your brand.
Facebook groups.
Quora discussions.
Comment threads.
Other spaces where people gather independently.

Both owned and earned communities play a critical role in the smartest strategies.

By seeding and nurturing conversations where your community and broader audience already gather.

By cultivating a “home” that is uniquely yours, you protect your brand against the homogenizing effect of AI-driven search.

Dig deeper: The rise of forums: Why Google prefers them and how to adapt

Why community is the secret sauce of search everywhere

Here’s a sobering thought: AI is only going to get better.

They’ll become more skilled at surfacing consensus and amplifying shared rhetoric.

But consensus doesn’t drive differentiation. Community does.

A backlink can be replicated. A feature in a listicle can be matched. That’s just search marketing ping-pong.

A community, on the other hand, can’t be scraped, cloned, or copied.

Brands that invest in their communities today aren’t just building engagement.

They’re building something much more powerful: a moat of differentiation and individuality.

Community resists the sameness of AI-driven search. It’s what ensures your brand’s voice doesn’t just show up, but truly stands out.

LEGO Ideas: Community in action

One brand that proves the power of community as a competitive advantage – especially in an AI-driven world – is LEGO.

Through its LEGO Ideas platform, the company has turned its community into a creative engine for product ideation and a discovery layer that informs both content and product development.

Fans submit their ideas and vote on their favorites. The best and most popular are turned into real products.

Everything from pop culture tie-ins (recently, a Wallace and Gromit set was greenlit) to architectural replicas has emerged through this process.

So why is this powerful from a search perspective?

There are two key reasons.

Authenticity at scale, organically

Every submission, vote, and comment is UGT in action.

The community validates which ideas deserve attention, creating a visible signal of credibility long before a set hits the shelves.

Fan conversations fuel visibility

The conversations, forums, and social amplification around these fan-led projects fuel organic visibility.

A single fan concept can spark thousands of blog posts, Reddit threads, YouTube videos, and TikToks – surfacing LEGO in contexts no AI citation list could ever replicate.

While competitors battle for presence in AI summaries or listicle roundups – vying to be labeled “Best Construction Toy” – LEGO has built differentiation through something much harder to copy: a living, breathing community that fuels product innovation, search visibility, and brand preference.

No amount of AI summarization can flatten a brand’s individuality when its users are constantly creating new stories about it.

Dig deeper: How to use social and forum data to inform next-level SEO strategies

From presence to preference: Why community wins

The future of search can’t be about simply being listed.

Visibility alone is no longer enough.

Brands need to feel alive – human – and that happens through community.

AI can summarize anything, but it can’t replicate belonging.

That’s why the brands investing in their communities today will be the ones that win tomorrow.

They won’t just be seen; they’ll be chosen. They’ll become the preference.

So, start by asking: where is my community already thriving?

Listen, nurture, and amplify.

That’s how you turn presence into preference in a world where every brand shows up.

GEO x local SEO: What it means for the future of discovery

Thu, 21 Aug 2025 14:00:00 GMT

The local SEO playbook that worked for years no longer delivers.

Even businesses with strong rankings and steady traffic are disappearing from a critical slice of search results.

Here’s what happened to one of my clients last month:

For two years, their moving company held Position 1 for “moving to Birmingham, AL.”

Traffic was steady – until Google’s AI Overviews appeared.

Suddenly, their page was buried below a conversational response that listed three competitors but not them.

The wake-up call?

Over 58% of Google searches now end without a click.

A perfectly optimized page means nothing if AI engines don’t mention you.

This is the new reality of local SEO in the AI age, and it’s reshaping how customers discover businesses.

You aren’t just competing to rank webpages anymore. You’re competing to be cited by AI systems in their responses.

The principles remain, but the tactics have evolved with how results are presented.

Local SEO: Optimizing for citations, not just rankings

GEO is SEO evolved for the AI age.

You’re no longer just optimizing for crawlers. You’re optimizing for AI systems that:

Read your content.
Understand your business.
Decide whether you’re worth citing when someone asks, “What’s the best Italian restaurant near downtown that takes reservations?”

The fundamental shift?

SEO gets you ranked. Modern SEO – including GEO tactics – gets you cited.

Here’s the difference in action:

Traditional SEO creates a page targeting “Italian restaurant downtown Denver.”
AI-ready SEO creates content that directly answers the question, “Where can I make a reservation for authentic Italian food near Union Station that’s good for date night?”

The second approach gives AI engines exactly what they need to mention your restaurant in conversational responses.

How local SEO has evolved

What we once called “SEO” now includes multiple approaches, all working toward the same goal: visibility, whether in traditional Google listings or AI-generated results.

The foundation remains the same:

Google Business Profile optimization.
Local citations.
Technical SEO .

But now you also need to ensure that the same content is structured for AI systems to understand and cite.

Your schema markup serves both traditional search crawlers and AI engines.

Your customer reviews influence both traditional rankings and how AI systems describe your business.

What’s changed is the format and style of content that gets results.

AI-powered search features in Google and Bing prefer conversational, direct answers over keyword-optimized pages.

ChatGPT and other AI platforms cite businesses that provide clear, authoritative information in natural language formats.

The businesses winning local search right now understand this isn’t about learning completely new skills.

It’s about adapting existing SEO knowledge for how search results are now presented to users.

Dig deeper: Want to win at local SEO? Focus on reviews and customer sentiment

How AI is reshaping local discovery

The shift from keywords to conversations represents the most fundamental change in local search since Google introduced the local pack.

While customers still type “plumber near me,” there’s a growing trend toward more conversational queries like “Can someone fix my broken toilet tonight?”

This evolving search behavior means AI engines need to understand context, urgency, and specific customer scenarios to provide relevant recommendations.

Entity optimization has replaced pure keyword targeting.

Search engines now evaluate your business through relationships and contextual relevance within your local market.

A restaurant mentioning its partnership with a local farm strengthens both businesses’ entity authority.

AI engines look for these connections to determine which businesses have genuine local expertise versus those just optimizing for keywords.

The rise of zero-click searches creates both challenges and opportunities for local SEO.

When AI-generated responses provide complete answers directly on search pages, your business information must be compelling enough to drive action from those summaries.

Instead of getting traffic to convert on your website, you need to convert customers directly from AI-generated descriptions of your business.

This evolution of local SEO means success requires first understanding how AI systems currently present your business, then strategically influencing those descriptions to drive customer action.

Audit your current AI presence before optimizing

Before diving into optimization tactics, you need to understand how AI systems see and describe your business.

This baseline audit reveals gaps between how you want to be presented and how AI engines actually cite your information.

The ‘Temperature 0.0’ method for brand discovery

The most systematic way to audit your AI presence uses what Metehan Yesilyurt calls the “ Temperature Zero Trick .”

At temperature 0.0, AI models become completely deterministic, always choosing the most statistically probable response.

This reveals exactly what these systems consider the “default truth” about your business.

Here’s how to test your brand’s AI baseline using platforms like OpenAI’s Playground or Google’s AI Studio :

Set your parameters:
- Temperature: 0.0 (removes randomness)
- Top-p: 1.0 (doesn’t restrict vocabulary)

Test these core prompts:
- “[Your Business Name] is known for” → Reveals primary associations
- “[Your Business Name] compared to” → Shows competitive positioning
- “[Your Business Name] customers typically” → Exposes audience understanding
- “Best [your service] providers in [your city] include” → Tests market positioning

The mask technique works particularly well for local businesses.

Try prompting “[Your Business Name] (mask)” and see how AI systems complete that sentence. This reveals their strongest associations with your brand.

If AI systems provide specific, detailed responses about your business at temperature 0.0, it suggests strong brand recognition in their training data.

Generic or missing responses indicate opportunities to strengthen your digital presence through the optimization tactics we’ll cover next.

Competitive intelligence through deterministic testing

Use the same temperature 0.0 approach to understand your competitive landscape.

Test queries like “best restaurants in downtown Portland” or “trusted HVAC companies serving Dallas” to see which businesses consistently appear in deterministic responses.

Pay attention to the language AI systems use when they do mention your business versus competitors.

Are you described as “highly rated,” “experienced,” or “locally owned”?
Do they mention specific services, pricing information, or unique selling points?

This analysis reveals which trust signals and business attributes AI engines currently associate with your brand compared to competitors.

Most importantly, look for inaccuracies or missing information in AI descriptions of your business.

Outdated hours, wrong phone numbers, or incomplete service descriptions signal data consistency issues that need immediate attention before any optimization efforts can be effective.

Dig deeper: AI and local search: The new rules of visibility and ROI in 2025

Making your local business AI-citation worthy

Once you understand how AI systems currently present your business, you can strategically influence those descriptions to improve visibility and drive customer action.

Optimize your source citations and mentions

AI engines pull information about your business from the sources they were trained on and continue to reference.

Unlike traditional local SEO, where NAP consistency across directories impacts local visibility, AI citation depends on the quality and accuracy of information at the sources these systems actually use.

Identify where AI systems are currently sourcing your information

Use the temperature 0.0 testing method to discover what AI engines know about your business, then work backward to identify likely sources.
If they mention specific details about your services, awards, or company history, those details came from somewhere in their training data or current knowledge sources.

Prioritize high-authority sources that AI systems likely reference

Focus your efforts on major platforms like:
- Your company website.
- Google Business Profile.
- Industry publications.
- Local news site.
- Authoritative directories in your field.
These sources are more likely to be included in AI training data and ongoing knowledge updates than obscure directories.

Ensure accuracy and depth at your most visible citation sources

Rather than chasing NAP consistency across hundreds of directories, invest time in creating comprehensive, accurate profiles on the sources AI systems actually use.
Include detailed service descriptions, company background, expertise indicators, and current contact information on platforms where your business has the most visibility.

Monitor and correct inaccurate information at key sources

When you find AI systems citing outdated or incorrect information about your business, trace that information back to its likely source and update it.
This targeted approach is more effective than broad directory cleanup for improving AI citations.

Dig deeper: How to boost local SEO: 4 AI-driven tactics that get results

Create content that completes local tasks

AI engines prioritize content that directly answers questions the way humans actually ask them, but there’s a strategic component most marketers miss.

Local search optimization today requires understanding that customers use AI to complete specific tasks, not just find information.

When someone asks, “Where’s the best gluten-free bakery near me that’s open late?” they aren’t researching options – they’re trying to solve an immediate problem.

Write for task completion with natural, conversational language

Instead of targeting “dentist emergency hours,” optimize for “Can I get my tooth fixed after hours?”
Rather than generic coverage area descriptions, include specific, actionable details like “emergency plumbing services available within 15 minutes of Pioneer Courthouse Square.”
Avoid overly optimized phrasing that sounds artificial. AI systems favor authentic content that provides immediate value users can act upon without additional searching.

Provide specific, quotable details that AI systems can cite confidently

Compare keyword-focused content like “Emergency dental services available 24/7 in Portland, Oregon” versus task-oriented content like “Yes, we provide emergency dental care 24/7 in Portland, including late-night tooth pain, knocked-out teeth, and broken crowns. Our emergency dentist can see you within an hour, even on weekends.”
The second version gives AI engines specific, quotable information while using natural language patterns that signal expertise and trustworthiness.

Build comprehensive content clusters that anticipate complete customer conversations

After explaining “What causes kitchen sink clogs?” immediately address “How much does professional drain cleaning cost?” and “Signs you need emergency plumbing service.”
Then, link to related topics like “Preventive maintenance tips” and “When to call vs. DIY repairs.”
This approach aligns with how AI systems understand topical authority and increases your chances of being featured in responses that address multiple related customer concerns.

Create strategic comparison content that positions your business favorably

Develop pages like “Best HVAC Companies Serving North Dallas” that provide genuine comparative value with specific metrics like:
- Response times.
- Years in business.
- Certifications.
- Customer satisfaction scores.
AI systems favor balanced, informational content that genuinely helps users make decisions, so present honest assessments while highlighting your unique advantages through data-driven comparisons that AI can easily parse and cite.

Schema markup that AI engines actually use

Basic local business schema won’t cut it anymore.

AI engines want detailed, specific markup that helps them understand exactly what you do, where you do it, and how customers can engage with your business.

Implement comprehensive service and area schemas beyond the basics

Include:
- FAQ schema for common customer questions.
- Service schema with specific service areas.
- Review schema to highlight positive feedback.
- Event schema for local workshops or community involvement.
Each schema type provides AI systems with structured data they can easily extract and cite in conversational responses.

Test schema in real-world scenarios – not just validators

Use schema.org’s validator for technical accuracy.
Test how AI engines interpret your markup by asking specific questions about your business in ChatGPT or Google AI.
This real-world testing reveals gaps that technical validators miss and shows you exactly how AI systems are using your structured data.

Keep schema current and detailed with location-specific information

Update your markup regularly to reflect current services, hours, and service areas.
Give AI systems rich context for understanding when and how to cite your business in relevant local queries by including:
- Specific geographic boundaries.
- Holiday hours exceptions.
- Detailed service descriptions.
- And more.

The trust factor: Why reviews matter more than ever

Here’s something most marketers miss about AI-powered search: these systems are inherently risk-averse.

When AI engines provide a single answer or recommendation, they don’t want to be wrong.

This creates a “flight to quality,” where trust signals decide which businesses get cited.

For local businesses, this means customer reviews have become exponentially more valuable. AI systems don’t just count your star rating.

They analyze review content to understand your expertise, reliability, and customer satisfaction patterns.

Focus on detailed, service-specific reviews over generic praise

A plumbing company with reviews mentioning “fixed the problem immediately,” “fair pricing,” and “cleaned up afterward” provides AI engines with specific trust signals they can reference when someone asks about reliable local plumbers.
Generic five-star reviews without details don’t carry the same weight as authentic accounts of how you solved specific customer problems.

Encourage customers to mention specific outcomes and experiences

Guide happy customers to include details about the services they received, problems you solved, and results they achieved.
These specifics become the building blocks AI systems use to construct authoritative descriptions of your business when responding to local queries.

Maintain review consistency across all platforms where AI systems gather data

Ensure you’re actively managing reviews on Google, Yelp, Trustpilot, G2, Facebook, and industry-specific platforms.
AI engines cross-reference review sentiment and content across multiple sources to build comprehensive business profiles, so neglecting any major platform creates gaps in your trust signals.

Measuring AI-enhanced SEO success

Traditional SEO metrics like keyword rankings don’t capture how AI engines present your business to potential customers.

You need new measurement approaches that reveal your visibility in AI-generated responses and the quality of those citations.

Track AI citation frequency across major platforms

Monitor how often generative engines reference your business when answering local queries across ChatGPT, Google AI Overviews, and Bing Chat.
Set up regular testing schedules where you ask the same industry-related questions monthly and document when and how your business appears in responses.

Analyze the sentiment and accuracy of AI-generated descriptions

When AI engines mention your business, what do they say?
Are you described as “highly rated,” “locally owned,” or “experts in specific services”?
Track the sentiment and accuracy of these descriptions to understand how AI systems perceive and present your business compared to competitors.
Look for opportunities to influence these descriptions through the optimization tactics we’ve covered.

Test cross-platform entity recognition consistency

Search for your business across different AI platforms using natural language queries to ensure consistent entity recognition.
Use the temperature 0.0 method we discussed earlier to get deterministic responses, then document any discrepancies between platforms.
Consistent entity recognition across platforms indicates strong local authority signals.
Inconsistencies suggest specific data cleanup opportunities that could improve your overall AI visibility.

Dig deeper: Why Google reviews will power up your local SEO in 2025

Your path forward in the GEO era

Local SEO hasn’t disappeared – it’s evolved.

The core principles still matter:

Relevant content.
Accurate business information.
Strong customer trust.

What’s different is where and how that information gets surfaced.

Start by creating conversation-ready content and consistent entity signals across every platform your business touches.

Answer real customer questions directly, in a language that AI systems can cite with confidence.

Most competitors are still chasing rankings while AI decides who gets mentioned.

That gap is your opportunity to win both rankings and citations, capturing visibility across search listings, local packs, and AI responses.

The businesses that thrive now are those that treat SEO as multichannel discovery.

Master that, and you’ll own your local market no matter how search keeps evolving.

Seize the AI search opportunity with site health by Semrush Enterprise

Thu, 21 Aug 2025 11:00:00 GMT

The rise of AI-powered search has profoundly changed how people learn, discover, and decide. For brands, maintaining site health now extends far beyond traditional SEO best practices, becoming a critical factor in how AI systems discover, understand, and portray your content.

That’s why ensuring your website is robust and AI-ready is paramount. Let’s explore some strategies for ensuring your brand not only survives but thrives in the evolving AI search era.

The new frontier for brand visibility

Millions are now discovering brands through AI search, with projects indicating it could even surpass traditional search traffic by early 2028 .

Conversational responses are synthesized from information across internal training data and external web sources. Often, these responses can provide a complete answer without providing a click to a website.

This shift marks a new era of zero-click searches that are impacting organic traffic for many websites. Securing direct brand visibility is incredibly critical in this environment.

If your brand isn’t appearing, then you might as well be invisible.

Despite dropping traffic, AI search brings opportunity

After researching via multiple prompts (and potentially channels), AI search visitors are often more informed and closer to a purchase decision by the time they reach your site. In fact, they may be 4.4 times more likely to convert than traditional organic visitors.

This combination of more limited but higher potential traffic makes optimizing for AI search an essential layer in modern marketing strategies.

Why site health is central to AI crawlability and visibility

For AI systems to use your content, they must first be able to access and process it.

Unlike traditional search engine crawlers that can execute JavaScript and parse complex page structures, AI crawlers often arrive at a URL, grab the raw HTML, and make instant decisions about processing the content.

If your site isn’t “AI-ready,” parts of it may be entirely skipped, leading to lost visibility.

Traditional audits or monitoring, focused on Google’s crawlers and user experience (Core Web Vitals), can often miss the mark for AI.

This new generation of crawlers prioritizes how quickly and cleanly they can extract meaningful information directly from your page source.

Here are five steps to ensure your site is AI crawlable:

Ensure AI systems can retrieve and read your HTML without needing to render JavaScript. Implement server-side rendering (SSR) for all critical content to ensure core information is available in the initial HTML response. Test your pages with JavaScript disabled to see what AI bots actually receive.
Your key content must be understandable without executing scripts. Use semantic HTML5 elements (e.g., <article>, <section>, <header>) and a proper heading hierarchy (H1-H6). For multimedia content, include descriptive file names, clear alt text, and full-sentence captions for images to aid AI comprehension. Video content should have transcripts.
Leverage schema, semantic tags, and clear hierarchies. Implement structured data like FAQ schema for common questions and Product, Article, or Organization schema where suitable. Clear content layers with definition boxes and bulleted answers also aid AI parsing.
Streamline your site. Minimize JavaScript dependencies for content pages, compress assets, and optimize images. Clean up unnecessary tracking scripts and third-party integrations. Ensure your site loads quickly, ideally under 2 seconds, as faster sites are more easily parsable by AI.
Verify that AI crawlers like GPTBot, CCBot, Claude-Web, and PerplexityBot aren’t blocked in your robots.txt file. Consider incorporating llms.txt, which is a potential future standard for informing LLMs about content access.

Ensure a technically robust website that’s ready for AI search and beyond with Site Intelligence from Semrush Enterprise .

How to structure content for human and AI understanding

Beyond technical accessibility, your content also needs to adapt. Optimizing for rankings and traffic isn’t the full picture: it now also needs to be quotable and trusted by AI systems.

Here are some core principles to follow:

Use natural language and direct answers

Ensure that you use clear, natural language and simple sentence structures, especially when discussing key information like your brand, products, or features.

Consider question-based headings where appropriate (e.g., H2 or H3) and provide a complete, direct answer in the opening paragraph of each section. This is fundamental for generative engine optimization.

Leverage semantic chunking and machine-readable formats

AI models prefer content structured in machine-readable formats such as FAQs, bullet points, step-by-step guides, and tables. Organize your content into short, clearly labeled sections focused on a single idea, as this makes it easier for AI to parse and extract relevant snippets.

Quote statistics and expert content as proof

Prioritize concrete, data-backed statistics and ensure proper attribution. Include expert quotes, case study results, and concrete examples that AI systems can easily extract and cite. Specific numbers, timeframes, and measurable outcomes give AI systems tangible data to reference.

Strive for passage-level clarity

Focus each passage around a specific topic or subtopic, ensuring key passages and sentences make sense in isolation, avoiding external dependencies or asides. LLMs often use passage-level retrieval, looking for relevant segments of text.

Ensure content is updated and follows accessibility guidelines

LLMs seem to favor recent content, so update yours frequently to refresh stats and examples, and add clear “last updated” timestamps.

Check that visuals have descriptive alt text and full-sentence captions. Consider adding OpenGraph tags to social media content, as this can also simplify AI interpretation.

Building authority and trust with your website

AI systems prioritize useful, high-quality content from authoritative sources. While techniques like LLM seeding help position your brand as an authoritative one more widely, we’ll be focusing on your own website here.

Original research and thought leadership

Publish research-driven insights, data-backed findings, and expert commentary that AI and industry leaders are likely to cite. Content with specific, sourced statistics gets referenced more often.

Author credentials and attribution

Add author bios that highlight expertise and experience in the field. AI systems may look for authority signals when deciding which sources to cite. Always cite authoritative sources to back up your claims, reinforcing credibility.

Entity-first content architecture

Try to structure your content around clear entities (people, places, products, concepts), as LLMs navigate via entity relationships, not just page-to-page links. This helps to reinforce the connections between and consistent citing of content.

Measuring your AI search success

Your website is completely accessible by AI crawlers, with key information clearly parsable in HTML. Your content is structured. Now, how do you measure success?

Traditional SEO metrics like rankings and organic traffic won’t tell the full story for AI search, as so many queries end in zero-click interactions.

Focus on visibility and perception more broadly rather than clicks. Especially as visibility in AI search can come via mentions or citations.

High organic rankings may lead to more citations, but not necessarily more mentions. In fact, LLMs often cite pages that rank lower in traditional organic search. Online mentions and reviews are often more indicative of AI search performance than traditional Google organic visibility.

AI search results offer both mentions (blue) and citations/sources (purple) as opportunities for visibility.

Brand mentions, sentiment, and sources

Monitor where and how your brand appears across AI search platforms. Define key prompts and monitor if, where, and how you’re showing up in response to these.

Note the sentiment and context that surrounds you also. Are you being defined as unaffordable when you target a budget-conscious consumer? Or perhaps there are service concerns being placed alongside you.

This includes tracking sentiment shifts (positive, neutral, negative).

Branded traffic and direct visits

A rise in branded searches and direct traffic, especially coupled with declining organic clicks, can indicate growing LLM influence, as users may discover your brand via AI and then search directly later.

Tip: Use Semrush Enterprise AI Optimization . Designed for brands to monitor visibility, sentiment, and competitive share across all leading AI search platforms, while providing targeting optimization and competitive opportunities.

The future of search is undeniably AI-driven

Brands that adapt now will lead the next era of online visibility. By ensuring AI crawlability, refining content, and reinforcing authority, you can ensure your brand remains top of mind for both users and AI models.

The window for proactive site health optimization for AI is closing; those who move now will define the new rules of search.

Discover Site Intelligence by Semrush Enterprise for a future-ready site that’s primed for search engines, AI, and whatever comes next.

ChatGPT is sending less traffic to websites – down 52% in a month

Wed, 20 Aug 2025 19:56:00 GMT

Referral traffic from ChatGPT to websites is down 52% since July 21, according to Profound’s Josh Blyskal, who analyzed more than 1 billion ChatGPT citations and 1 million referral visits from a sample of sites across various verticals.

By the numbers. Some of the key findings:

Reddit citations are up 87% since July 23, now topping 10% of all ChatGPT citations.
Wikipedia jumped 62% from its July low, grabbing nearly 13% of citation share.
The top three sites – Wikipedia, Reddit and TechRadar – accounted for 22% of all citations, up 53% in just a month.

Why we care. ChatGPT seems to now favor a handful of “answer-first” sources, while branded websites are losing visibility – and millions of potential referral clicks.

What’s happening. Blyskal said this isn’t a GPT-5 effect ( GPT-5 launched Aug. 7 ). ChatGPT’s citation consolidation began weeks earlier, suggesting OpenAI manually reweighted its retrieval system to favor useful answers.

The bigger picture . Some takeaways from Blyskal’s LinkedIn post :

Branded content that prioritizes conversion (“Schedule a demo”) is getting passed over.
Answer-first platforms like Reddit and Wikipedia win by default because they directly address user queries.
“The citation opportunity is massive for brands willing to shift from conversion first to answer first content,” wrote Blyskal.

Bottom line . OpenAI’s citation experiments can trigger big traffic swings. Brands that don’t provide real answers could be squeezed out of ChatGPT answers.

Technical SEO for GenAI: How bots see, crawl & rank your brand by Edna Chavira

Wed, 20 Aug 2025 12:27:00 GMT

GenAI-powered search doesn’t behave like Google. Large Language Models (LLMs) and AI agents don’t just index—they interpret, summarize, and rank your content in ways most audits aren’t built to detect. That means the technical signals shaping your brand’s visibility are shifting underfoot.

In Technical SEO for GenAI: How bots see, crawl & rank your brand , our experts explore what it means to be GenAI-visible, revealing how to identify hidden blockers that prevent AI systems from fully understanding or surfacing content. You’ll hear from Myriam Jessier, Founder of PRAGM, a consultant trusted by CMOs for transforming complex SEO and AI concepts into actionable strategies, and Gerald Murphy, Evangelist at Semrush, a seasoned digital strategist known for aligning data-driven insights with enterprise growth. They’ll discuss:

How LLM bots crawl differently from traditional search engines
Where rendering failures and broken resources disrupt AI visibility
Why mobile UX details like tap targets and Core Web Vitals now matter even more
The new technical SEO KPIs for AI and multimodal search
Real examples of hidden issues that block GenAI discovery

Join us to stay ahead of the AI-driven shift in search. You can save your spot here.

Generative AI is changing search, but Google is still where people start: Study

Tue, 19 Aug 2025 15:12:00 GMT

Generative AI is reshaping how people find information — but it hasn’t replaced search engines like Google. That’s according to a new Nielsen Norman Group study:

While users increasingly experiment with ChatGPT, Gemini and AI Overviews, most still default to old habits: starting with Google.

Why we care. Google is a habit – and habits are hard to break. That gives Google a built-in edge: even as AI eats into clicks, Google remains the default starting point for users. That means organic visibility still matters for brands and businesses. AI is reshaping the journey, but it won’t erase search overnight.

The big picture. According to the study:

AI overviews = fewer clicks. People notice and often rely on Google’s AI summaries, reducing the need to visit websites. Not new, and still bad news for publishers.
AI chat boosts efficiency. Once users tried Gemini or ChatGPT for complex tasks, they found them faster and more useful than traditional search.
Search isn’t gone. Even heavy AI users still cross-check with Google or visit content pages. No participant relied solely on AI for all information needs.
Familiarity wins. Just as “Google” became a verb, some users now casually call ChatGPT “Chat.” Brand familiarity may be the biggest advantage in AI search.

Bottom line. Generative AI is changing how people research – but it’s an evolution, not a revolution . The biggest barrier to AI adoption isn’t accuracy or UX, it’s human habit.

About the data. Nielsen Norman Group conducted remote usability testing with nine participants in North America and UK, representing diverse demographics and levels of AI experience. Sessions explored how users approached real research tasks with search engines and AI tools.

The study. How AI Is Changing Search Behaviors

Dig deeper. Google’s AI Overviews are hurting clicks: Pew study

5 B2B content types AI search engines love

Tue, 19 Aug 2025 13:00:00 GMT

AI search is evolving fast, but early patterns are emerging.

In our B2B client work, we’ve seen specific types of content consistently surface in LLM-driven results.

These formats – when structured the right way – tend to get picked up, cited, and amplified by models like ChatGPT and Gemini .

This article breaks down five content types gaining notable AI search visibility , what makes them effective, and how to optimize them for LLM discovery:

Comparison pages.
Integration docs/open APIs.
Use case hubs.
Thought leadership on external platforms.
Product docs with schema.

1. Comparison pages

Our analysis shows that Gemini frequently surfaces “X vs. Y” content in AI Overviews and AI Mode – even when the query doesn’t ask explicitly for the comparison.

What to include

Publish /vs/ pages with pros, cons, pricing, use case match, and schema.
Do this for any competitors that bring in a decent volume of comparison queries, along with any comparisons that are easily related to your product or service.

2. Integration docs/open APIs

Our analysis has provided numerous instances of GPTs and Copilot citing SaaS APIs and dev docs in answers.

Example

A ChatGPT prompt for “setting up span metrics for backend services” cited a docs page from performance monitoring company Sentry in a list of best practices.

What to include

Maintain clear documentation + changelogs with versioning and schema.

Dig deeper: The future of B2B authority building in the AI search era

3. Use case hubs

We’ve seen clear indicators that AI Search prefers content that ties features to real business problems.

Example

Vanta’s SOC 2 compliance resource appears prominently in a ChatGPT answer to “SOC 2 compliance automation for startups.”

What to include

Build intent-driven use case pages with testimonials and product mapping.

4. Thought leadership on external platforms

LLMs pick up posts from company experts, including founders, SMEs, and established thought leaders, on outlets like Medium and Dev.to for strategy-based questions.

Example

What to include

Syndicate posts from a company founder, SME, or brand ambassador with a unique POV, then include a canonical link back to the business website.

5. Product docs with schema

Gemini AI Mode lifts from product docs if they’re structured with FAQs, How-to sections, and/or breadcrumb structured data .

Example

What to include

Add FAQPage, HowTo, breadcrumb structured data, and SoftwareApplication schema types to product docs.

3 overarching recommendations

You should never veer from the E-E-A-T principles that have long underpinned traditional SEO . Those same tenets will serve you well for LLM discovery, too.

Beyond them, however, there are a few LLM-specific steps to consider if your goal is to increase AI search visibility.

I’ll break down three key recommendations.

Optimize for multi-modal support

AI search systems are increasingly retrieving and synthesizing multimodal content (think: images, charts, tables, videos) to better answer user queries.

Flex your content across multiple media types to provide more useful, scannable, and engaging answers for users.

Specific recommendations:

Ensure images and videos remain crawlable for search and AI bots.
Serve images via clean HTML and avoid lazy-loading with JavaScript-only rendering, since LLM-based scrapers may not render JavaScript-heavy elements.
Images should use descriptive alt text that includes topic context.
Add captions to images and videos with an explanation right below or beside the visual.
Use <figure> , <table> , etc., with contextually correct markup to help parse tables, figures, and lists.
Avoid images of tables. Use HTML tables instead for a machine-readable format supporting tokenization and summarization.

Optimize for chunk-level retrieval

AI search engines don’t index or retrieve whole pages.

They break content into passages or “ chunks ” and retrieve the most relevant segments for synthesis.

Optimize each section like a standalone snippet.

Specific recommendations:

Don’t rely on needing the whole page for context. Each chunk should be independently understandable.
Keep passages semantically tight and self-contained.
Focus on one idea per section: keep each passage tightly focused on a single concept.
Use structured, accessible, and well-formatted HTML with clear subheadings (H2/H3) for every subtopic.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Optimize for answer synthesis

AI search engines synthesize multiple chunks from different sources into a coherent response.

Aim to make your content easy to extract and logically structured to fit into a multi-source answer.

Specific recommendations:

Summarize complex ideas clearly, then expand (A clearly structured “Summary” or “Key takeaways”).
Start answers with a direct, concise sentence.
Favor a factual, non-promotional tone.
Use structured data to help AI models better classify and extract structured answers.
Use natural language Q&A format.

Create B2B content that wins in AI search

An added benefit of these five content types is that they span multiple intent stages – helping you attract prospects and guide them through the funnel.

Just as important: make sure your AI search measurement systems are in place (we use Profound, GA, and qualitative research) so you can track impact over time.

And stay tuned to reports and industry updates to keep pace with new developments.

Why Every Small Business in West Georgia Needs a Mobile-Friendly, AI-Ready Website

Tue, 19 Aug 2025 02:37:47 GMT

�� Mobile-Friendliness Is Now the Baseline

If your website isn’t easy to use on a smartphone, you’re missing out on customers—plain and simple. Today, over 60% of online traffic comes from mobile devices , and users expect a seamless experience no matter what screen they’re on. At Hometown Beat , we help small businesses in Carrollton, GA and throughout West Georgia create responsive websites that deliver fast load times, thumb-friendly navigation, and clear calls to action—all optimized for mobile browsing.

�� AI Search Is Changing the Game

It’s not just Google anymore. Increasingly, people are turning to AI-powered search platforms like ChatGPT, Bing AI, Google Gemini, and voice assistants like Alexa and Siri to ask questions and find businesses. These platforms don’t scan clunky websites—they prioritize clean, structured, well-optimized content with fast performance and clear metadata. At Hometown Beat, we build websites that are not only SEO-optimized for traditional search engines but also designed to be easily crawled, parsed, and surfaced by AI-powered tools .

That means:

Using schema markup and structured data
Writing clear, informative content with natural language queries in mind
Optimizing for conversational keywords
Keeping page performance lightning fast
Ensuring content is accessible and up to date

When your website is AI-ready , you’re more likely to show up in answers delivered by digital assistants, chatbots, and next-gen search experiences.

�� Mobile & Local Go Hand in Hand

If you're running a small business in West Georgia, your customers are likely searching for you "on the go." Whether they’re looking for a nearby coffee shop, contractor, or boutique, mobile and local search go hand in hand. A mobile-friendly site that includes local SEO strategies —like Google Business optimization, reviews, and accurate directory listings —helps you show up where and when it matters most.

�� What Makes a Site Mobile & AI-Friendly?

At Hometown Beat, our website design and management solutions prioritize:

Responsive design that adjusts beautifully to all screen sizes
Fast load speeds for mobile and low-data environments
Structured content that AI platforms can understand and use
On-page SEO best practices to improve your Google rankings
Schema markup to enhance visibility in AI responses and search snippets
Voice search optimization for long-tail and conversational queries

�� Why Work With Hometown Beat?

Based in Carrollton, GA , Hometown Beat is proud to serve the entire West Georgia area with custom digital marketing solutions tailored for small businesses. With over 50 years of combined industry experience , we’ve helped businesses of all sizes and industries modernize their digital presence without blowing their budget.

As a locally owned and operated agency , we understand the community, the culture, and what makes West Georgia businesses unique. Our websites are built to perform , scale with your growth, and keep you competitive in a rapidly evolving digital world.

✅ Ready to Build a Smarter Website?

Your customers—and search engines—are already mobile and AI-focused. Make sure your website is too.

�� Call Hometown Beat to schedule your FREE website audit today.

Facebook upgrades Professional Dashboard with new insights

Fri, 15 Aug 2025 18:56:00 GMT

Facebook is rolling out an enhanced web version of its Professional Dashboard, adding fresh creator insights, streamlined navigation, and improved parity with its mobile tools.

Details:

Web refresh : New home screen highlights earnings, content performance, and engagement; simplified navigation groups tools for monetization, audience connection, and education; Comments Manager now matches mobile functionality.

Coming soon : Advanced trend insights, detailed content performance data, distribution breakdowns, data export, and bulk upload tools.
New audience insights : “Popular with your followers” shows which content and pages resonate most with your audience.

New performance metrics : View s over time, view rate, and retention help pinpoint content that hooks viewers and where engagement drops off.

Why we care. The dashboard is a key hub for creators to track performance, manage monetization, and engage audiences. The updates make it easier to work across devices and tap into deeper performance data for smarter content decisions.

The big picture. With more robust desktop tools and sharper insights , Facebook is giving creators a better way to spot trends, fine-tune content, and grow their audience – all from one place.

Call it whatever you want – just don’t call it ‘Answer Engine’

Fri, 15 Aug 2025 17:13:00 GMT

The war for naming and defining the new search is more ferocious than ever.

I don’t care what you call it, just don’t call it “answer engine.”

Search has reinvented itself again and climbed back to the top of the food chain. Job boards are full of “head of” roles for this new search era.

That’s not hype; it’s survival.

And if you’re a brand waiting for the dust to settle, you’re already late.

Zero‑click is the default now

Most people get what they need without leaving the results page.

Bain says about 80% of consumers rely on AI summaries for at least 40% of their searches, cutting organic traffic 15–25%. Similar reports abound, placing those numbers much higher and proving that rounding errors are over.

“Answer engine” focuses on the output, not the work.
“GEO” is getting buzz, but it doesn’t help you run a P&L.

If we need a name, call it CEO: Conversational Engine Optimization – because the real work is inside conversational environments.

There’s no danger in people holding on to the three-letter top person moniker since, to anyone with an actual job, “CEO” is meaningless.

Slapping those same letters on a search strategy is consultant cosplay. The proof isn’t in the label; it’s whether you show up (and get credited) and whether that presence pays.

Be kind to the machines. When they take over, I want them to remember who said “please” and relayed facts.

The CEO checklist

Write in clean, quotable sentences that machines can lift and credit; keep paragraphs tight; add a table or a source only when it truly clarifies the point.
Answer the follow‑up questions people really ask and write as if the conversation will continue.
Make pages fast and well‑structured so parsers don’t trip, you’ll need sensible schema, clear bot directives, no gimmicks.
Put your name and date on the work, show your sources, lean on first‑party data.
And change the scoreboard: track how often you’re cited in AI answers and how those mentions move your metrics.

If an answer requires no further action, desired actions drop, so protecting the revenue model is paramount. Falling short hits ads, affiliates, and lead gen and almost every other connected relationship a brand has.

Treat zero‑click as a channel, not a write‑off

Capture what you can on the surface, the low-hanging fruit like calls, bookings, and emails.

Where your content powers answers, push for attribution and licensing. Publish formats assistants can cite without replacing your value. Marketers are already adjusting, sometimes awkwardly, as anyone reading the coverage in the Wall Street Journal or The Guardian can tell.

For leadership, here’s the translation without the checklist:

Define success in terms that match the world you actually operate in: your share of answer (how often you’re cited), the accuracy of how your brand is described in AI outputs, the assisted conversions those mentions drive, and the revenue that follows. Restructure high‑value pages into clean Q→A narratives with sources. Publish evidence with benchmarks, calculators, and original research.

Again, it can’t hurt to be kind and give the machines what models they prefer to cite.

Don’t bet the farm on one just yet. You need Perplexity, ChatGPT (with browsing), Gemini, and Copilot.

And put real governance around it: editorial standards, update cadence, and fact‑checking that can survive summarization.

Name it if you must

GEO, CAIO, CEO, CAO, or AEO, it doesn’t matter.

While I’m confident that my three-letter moniker will stick and be universally adopted overnight, you may want to keep “answer engine” as a raised eyebrow for people in the know.

Either way, the job stays the same. You’ll need to earn presence and trust in conversational results, then make sure that presence pays.

How to build a paid media team in the AI age

Thu, 14 Aug 2025 12:00:00 GMT

Single-channel experts? Getting left behind by most (non-enterprise) marketing teams.

Hiring for experience only? There’s more to the top-tier marketer these days.

Analytical vs. creative? There’s room for both, but the scale is tipping toward creativity.

Whether you’re building a PPC team for an agency – a big part of my job – or for an in-house team, your approach should reflect the rise of AI.

That means factoring in changes in bidding algorithms, LLMs , and creative tools.

If you’re still hiring like it’s 2017, you’re leaving your competitors room to gain at your expense.

This article tackles the attributes I’m looking to hire for in the AI age – and why I’m prioritizing those attributes over what I might have looked for pre- Performance Max , Advantage+, and ChatGPT .

Ambition to learn

As recently as three or four years ago, I considered hiring candidates for my performance marketing team who specialized in a single channel (usually paid search).

Today, I can’t conceive of doing that.

AI isn’t just LLMs. It’s been steadily infiltrating ad campaigns across all major platforms for years.

In the process, it has erased many of the advantages that advanced, channel-specific marketers once had.

These days, it’s much more impactful to take a holistic view of multi-channel campaigns than it is to carve out a bit more performance by, say, splitting out an ad group.

In other words, marketers who focus on users and their journey – no matter where that journey happens – are better positioned to succeed than those stuck in a channel silo.

Yes, keeping up with one channel’s updates, betas, and reporting features can be a full-time job.

And specific channel expertise can still be valuable for keeping the whole team informed.

But marketers who aren’t devoting part of their time to gain at least fluency on all relevant channels are throwing up a big red flag.

Dig deeper: How to build a high-performing paid search team

Curiosity with a big dose of skepticism

We all know people who treat access to emerging AI tools like new birthday presents, but there are plenty of cautionary tales that come with eager over-adoption.

Approach each new tool with healthy skepticism – just as you would with “helpful advice” from Google reps – and ask targeted questions to understand how it works.

What data is it pulling from?
What model is it built on?
Is it incorporating all the variables you might encounter in your accounts?
Does it just look pretty, or is it built on a solid base that will enable all kinds of growth permutations?

One of the things we’re telling clients right now, no matter how impressive a tool looks at first glance, is to avoid long contract commitments.

At the rate AI is evolving, a year-long contract for any tool will probably look like a poor investment in a few months as competitors emerge with better technology, lower pricing, or both.

Experience – but not rigidity

Yes, experience absolutely matters, but it can also lead to stubborn over-reliance on methods and techniques that may now be outdated.

Applicants who demonstrate experience that informs their marketing fundamentals and a willingness to adapt to conditions as they change offer a great combination.

However, I have encountered applicants who have plenty of experience but also resist change when that change diverges from their demonstrated strengths.

In other words, if experience is something you have to un-train, the applicant might not be a good fit for a team with big growth goals.

Dig deeper: 5 undervalued skills to complement PPC management

Clear, empathetic communication

This has always been important, but today it’s also a counter to the flood of bot-produced content and interactions taking over the industry.

Think spammy LinkedIn comments, automated live chats, and AI-generated meeting minutes replacing real, well-taken notes from an actual attendee.

In an agency, the ability to understand and anticipate client needs – from a business perspective and the personal perspective of main client contacts – is fundamental to great partnerships.

I won’t leave it to AI to address those needs with thoughtful, thorough communication and by adapting things like account KPIs to fit changing business scenarios.

The same applies in-house: empathy and strong communication between teammates and managers are essential for building successful, high-functioning teams.

Creativity in a vacuum

Optimization is never easy.

Analytical skills, years of experience, critical thinking, and many other elements go into recognizing opportunities to drive more scale and performance.

But for many people, optimizing an existing campaign is less of a challenge than coming up with something from scratch.

This takes me back to the point about channel specialization.

A candidate with deep paid search experience but little exposure to paid social hasn’t proven they can develop creative concepts.

That skill is absolutely essential on platforms like Meta, TikTok, and even LinkedIn.

Even if you’re at an agency and working with in-house creative resources, you need to be more prescriptive than “come up with ad creative.”

One key to unlocking growth is ideating a variety of themes to test – not just running A/B tests on small elements like background color.

This approach helps you uncover fresh insights into what’s resonating with your ICP.

Dig deeper: 7 power moves to accelerate your PPC career

Wrapping up

Apply the same healthy skepticism to your own hiring decisions.

Thanks to ChatGPT and similar tools, I’m seeing more applicants with experience in engineering prompts for LLMs than actually running ad accounts.

For example, I might get solid work on an interview project like “Document your approach to the following scenario,” only to get blank stares when I ask follow-up questions.

Prompt engineering is valuable, but without foundational marketing skills, adaptability, and creativity, a candidate will struggle to succeed in today’s market.

At this point, I assume AI has a hand in much of the content I review from applicants.

It’s up to the interviewee to prove they have the experience and strategic skills to actually do the job.

We’re not hiring based on what worked yesterday – we’re building a team of marketers ready for what’s next.

In a time of rapid change, those who combine foundational marketing skills with curiosity, creativity, and adaptability won’t just thrive – they’ll help shape the future of the field.

Dig deeper: ChatGPT for PPC: 17 strategic prompts you can use today

AI Governance in SEO: Balancing automation & oversight

Wed, 13 Aug 2025 17:32:00 GMT

AI governance in SEO is an emerging topic that needs serious consideration. Once perched on the sidelines of SEO, AI is now integrated into many workflows, from writing and research to the analysis and prediction of user behavior.

As automation scales and AI adoption grows, so do the associated risks, including the production of low-quality content, compromised editorial integrity, and erosion of brand trust. AI isn’t going anywhere, and the key to ethical AI usage lies in transparency, risk management, and data protection.

In this guide, we explore AI governance in SEO, including its definition, significance, and implementation strategies that enable responsible scaling while maintaining and enhancing trust signals, rankings, and brand integrity. You can learn how to advance your AI practices while adhering to top ethical standards and mitigating potential risks to security.

What is AI governance in SEO?

AI governance in SEO refers to the policies and processes that guide the responsible use of AI technologies within an organization’s SEO efforts.

It encompasses every facet of AI, from the development and deployment of tools to their effective use, ensuring that all benefits are reaped without any harm. (This means that AI governance is more than just a declaration at the top of a blog stating that AI was used.)

AI governance encompasses transparency with users whose data is impacted by AI analysis and prediction models, as well as general workflows for internal teams, so stakeholders can be assured that teams are using AI in a way that meets legal, ethical, and brand-approved standards.

From a business perspective, AI governance protects organizations and teams that use it by applying rules and integral brand values to its usage. Governance doesn’t have to feel restrictive —teams can still explore AI and its latest developments, but within agreed boundaries that keep organizations and staff safe.

Why AI governance matters more than ever

Use of AI in SEO is scaling rapidly. According to HubSpot’s AI trends for marketers , 67% of marketers are increasing their investment in automation, and 66% of companies are building internal AI tools for marketing.

The trend line below for searches related to “AI SEO” shows the speed at which interest is growing.

Rightfully, well-intentioned team members are looking for ways to accelerate their workflow with agile AI solutions.

AI usage in SEO needs to be managed to prevent it from spiraling into inconsistency and low-quality outputs that harm brands. Without governance, teams are left to define their own standards for AI usage, and these standards may fall short of business, legal, or editorial expectations.

Governance ensures alignment and accountability against corner-cutting in fast-paced, high-pressure environments.

Here are some ways AI governance protects brands.

Maintains high-quality content publishing standards.

Google’s stance on AI is that it’s acceptable to use it, and Google won’t penalize websites or content solely because AI has been employed.

Specifically, Google says , “Our focus on the quality of content, rather than how content is produced, is a useful guide that has helped us deliver reliable, high-quality results to users for years.”

The key is for brands to use AI to create content that is helpful for readers.

Churning out AI-generated long-form articles about complex subjects almost definitely won’t result in high-quality, helpful content for users because AI lacks the finesse, knowledge, and experience to write sophisticated pieces of content well.

That said, short pieces of content or subjective content without nuance can be written with AI. Frequently asked questions are generally short and can be quickly written with AI, as long as a quick edit is conducted by a human to check the answers.

Straightforward, factual content that doesn’t require deep context or subjective judgment can also be written quickly with AI (e.g., “how to reset your email password” or content covering a set process with no nuance). Again, you still need a human to edit drafts for accuracy.

AI governance defines the boundaries within which AI can be used, establishing where it’s helpful for business and readers, and where it becomes unhelpful. Governance will help you identify where AI enhances content and where it diminishes it, simplifying your decision-making around use of AI systems.

The following infographic shows some pros and cons of AI-driven content generation.

Maintains rankings as algorithms evolve

Search engine algorithms are committed to displaying helpful content. As search engines refine their algorithms in pursuit of this commitment, websites that fall short will be deprioritized in favor of those that publish helpful content.

AI governance ensures that businesses use AI ethically and in a manner that future-proofs their workflows and content.

For a while, churning out large amounts of content at scale worked. Famously, a quick-and-dirty AI SEO tactic called the SEO heist ( read about it here ) resulted in extremely high numbers of keywords ranking and an increase in traffic.

The heist involved a lot of AI: A competitor’s sitemap was copied, AI was used to identify content gaps, and AI content was generated to create a page that reflected what was already on the competitor’s site.

The results?

An upward tick in clicks and keywords ranking.

But it didn’t last.

As algorithms become more sophisticated, sites that over-rely on AI content suffer. As you can see below, traffic and keywords soared, then dropped fast.

The decline in traffic and keywords that followed the SEO heist’s use of AI-generated content at scale was thought to be due to the creation of poor-quality content to manipulate search engines rather than to help users and build trust.

This website is still live and assumedly trading today, but the business impact is extreme. Today, only one page (the homepage) is available in Google’s index.

The key is using AI integrally so your site won’t be impacted by scaled AI abuse, which could result in helpful content penalties or link spam updates.

Further reading: Want to understand Google penalties in more detail? Read Google penalties: Advanced detection, recovery, and prevention strategies , which covers the types of penalties and how to prevent them.

There’s no doubt that Google will continue to improve algorithms related to helpful content and AI.

Google says:

“Focusing on rewarding quality content has been core to Google since we began. It continues today, including through our ranking systems designed to surface reliable information and our helpful content system. The helpful content system was introduced last year to better ensure those searching get content created primarily for people, rather than for search ranking purposes.”

Maintains brand integrity

Brand integrity goes beyond content. PR News Wire reports that nearly 50% of people experience brand mistrust in circumstances where AI was used to perform a service that they believed was provided by a human.

Transparency is key.

Here’s an example of transparency in action.

HubSpot’s chatbot is transparent about being an “AI bot.” With one click, users can “Chat with sales.”

Instead of fooling users with a chatbot that looks human, being up front about AI usage is open and honest.

In terms of content, brand integrity remains critical, especially when AI-generated falsehoods are published. Recently, the High Court of England and Wales warned lawyers that they could face criminal prosecution for false material generated by AI. Further initiatives are underway to explore potential AI regulations and review ethical considerations around its use.

Serious trust and liability issues are brewing in the AI content world, and these would be particularly easy to miss in large-scale content production that occurs within enterprise content strategy.

Perhaps the role of SEO widens once again. The need for editorial integrity has never been more prominent.

That said, editorial integrity isn’t new and spans more than just SEO expertise. It may be necessary for content to be edited by a subject matter expert, and a project manager should oversee the entire content workflow to ensure all crucial steps are met. (More on AI governance workflows and best practices is provided below .)

The issue with integrity and AI content is that AI writes everything, even falsities, with complete conviction. For example, an AI might cite studies and research that don’t exist. It does it in such a way that to the untrained eye, it reads as truth.

Google’s experience, expertise, authority, and trust (E-E-A-T) serve as a framework to address issues with AI-generated content. Content that performs well displays high levels of E-E-A-T and is central to SEO.

Further reading: To get a complete grasp on E-E-A-T and its role in SEO, read An SEO guide to understanding E-E-A-T

Maintains privacy, security, and compliance

AI governance in SEO ensures that the use of artificial intelligence aligns with strict security, privacy, and compliance standards. As AI systems analyze large volumes of search data, user behavior, and content performance metrics, governance frameworks establish rules for how this data is collected, stored, and processed. This includes enforcing data minimization, anonymization, and encryption practices to prevent unauthorized access and safeguard sensitive information.

By setting clear guidelines for AI model training and output monitoring, SEO teams can reduce the risk of exposing proprietary strategies, leaking confidential analytics, or inadvertently enabling malicious SEO tactics.

From a regulatory perspective, AI governance helps SEO practitioners operate within legal and ethical boundaries, especially as laws like the GDPR, CCPA, and upcoming AI-specific regulations expand global oversight. Governance ensures AI-driven SEO tools comply with consent requirements, user data rights, and transparency mandates. It also introduces audit trails, bias detection protocols, and explainability measures, allowing teams to verify that AI-generated recommendations are fair, accurate, and aligned with brand integrity.

In doing so, AI governance not only mitigates the risks of fines or reputational damage but also builds trust with stakeholders, customers, and regulators in a rapidly evolving search landscape.

Where AI is used in SEO workflows today

The scale of AI use cases in SEO workflows is vast. We’ve moved far beyond generative AI and content.

Here are some ways AI is used in SEO workflows:

Content creation, including content ideation, brief creation, drafting content, editing, clustering keywords, and writing questions and answers using natural language
On-page optimization, including title tags, meta descriptions, headings, and internal link insertion
Programmatic SEO, including page design and AI copy, that allows enterprise businesses to scale fast
Schema markup creation, including planning properties and types that can be used, then writing the code
SERP forecasting and competitor analysis , including identifying ranking patterns, identifying content opportunities, content gap analysis, and benchmarking and reporting
Image and alt-text generation , including sourcing images, generating descriptions, and file names using SEO best practices
User behavior analysis, including AI insights and summaries of user behaviour at scale (engagement, click patterns, conversion pathways)
Content performance monitoring , including the detection of underperforming pages, content decay, and ranking shifts

Knowing the scale of AI usage is critical for effective AI governance because, without the big picture of how AI is being used, AI governance cannot be fully comprehensive.

The challenge?

Teams are likely looking for AI solutions and may not be forthcoming with the information they need.

Companies must adopt an openness to discussing AI and how teams are using it, so governance can comprehensively cover all uses. Without thorough governance, businesses and brands are vulnerable to risks that may be difficult to identify.

Key risks of unmanaged AI in SEO

If you’re not “in the trenches” of the day-in-day-out action, the risks of unmanaged AI in SEO would be easy to miss. That is, until you have a traffic graph that looks like the one shared above (a steep decline due to algorithm changes deprioritizing or penalizing a website).

The decline can be caused by these consequences of unmanaged AI:

Over-automation without editorial review

A content marketing team can look like they’ve got their workflows together when content is being published at scale. On the surface, AI content can look excellent:

Topically relevant
Grammatically correct
Detailed
Well presented (headings, nice formatting, and images)

But underneath the surface, the content can:

Lack unique insights

Have no editorial finesse
Become repetitive since AI can only copy and rewrite what’s available
Include AI errors such as hallucinations or factual inaccuracies
Fail to meet brand tone, accessibility standards, and compliance

All of the above failings to meet high-quality editorial standards should be flagged as a potential future threat to your website. If they’re not already, they likely will be in the future.

Why?

Because search engine algorithms will continue to change, as they always have.

Algorithm updates are likely what have kept Google the number one search engine for decades, and it’s probable that other forms of search will follow suit. AI search, such as ChatGPT or Perplexity, will need to find a way to display only helpful content, similarly to how Google does: through updates and iterations, prioritizing content with high levels of E-E-A-T.

E-E-A-T erosion

AI-generated content without review can lead to E-E-A-T erosion, meaning that the AI-generated content fails to demonstrate experience, expertise, authority, and trust.

Without E-E-A-T, why should people (or search algorithms) trust you?

For example, if you publish content without editorial review, there could be:

No clear authorship
No real-world experience
Generic statements

AI-generated content is easier to spot than you might think. If you like reading, over-reliance on AI stands out.

How?

Through overuse of generic AI language.

Even ChatGPT can list many AI “thumbprints” in writing.

Words and phrases such as “powerful,” “leverage,” and “in today’s fast-paced world…” point to AI. Knowing this, it wouldn’t be difficult for a flag to be applied to these words as part of an algorithm.

Does this mean that these words are banned?

No.

Remember, it’s overuse that’s the issue.

Thin or duplicative content at scale

Thin content refers to websites that don’t add value. The content likely doesn’t fulfill search intent.

For example, an article on E-E-A-T and SEO that covers only what it is and why it’s important won’t suffice as a comprehensive, page-one article.

Why?

Because it lacks depth. The topic of E-E-A-T and SEO requires a lot more to be competitive and stack up against what’s already ranking. Other sites cover a range of related topics, including definitions of each component, guidelines, and how sites achieve E-E-A-T.

Similarly, duplicate content that adds nothing new to search engine results pages (SERPs) won’t rank higher than content that provides new and insightful information.

Both thin and duplicate content bring risks of content collapse or deindexation. In the SEO heist example above, the screenshot shows a decline to zero rankings, and a search for the site reveals only one page ranking.

Algorithmic penalties or demotions

Ultimately, using AI without proper governance can lead to ranking demotions or, even worse, penalties.

We know that Google’s Helpful Content rolled out with improved classifiers , an algorithmic system that generates a signal to Google that something is good or bad.

Poor-quality content will generate a negative signal, and enough of these can lead to a ranking demotion or penalty.

Even if a site avoids direct penalties, Google may deprioritize sections that appear automated, pushing them lower in search results or removing them from the index entirely. This is possibly what happened in the SEO heist example above.

The result?

Fewer impressions, less traffic, and eroded trust in your domain.

Association with link spam

Unchecked AI runs the risk of being associated with link spam, which refers to manipulative linking practices with an aim to boost rankings wrongfully. AI-generated pages with AI link insertion are very easily overdone.

Why?

Because you can let linking tools run amok on your site, linking words without possessing the intelligence to think about placement and context. This means you could end up with links in unusual places, such as within the text of a CTA that was intended to direct people to a conversion, rather than your top-funnel article.

You’ll know if you’ve got a penalty because it’ll be recorded in your Google Search Console (in the “Security and Manual Actions report) and will look like this:

AI dependency limiting the team’s critical thinking

A larger risk for businesses might be that an overreliance on AI in SEO prevents teams from thinking critically. This is particularly true for data analysis or user behavior analysis.

For example, you may have a piece of content on your website that is popular, as indicated by Google Analytics. The page is viewed thousands of times every time it’s shared on social media and email. Maybe it even converts.

For good reason, the page doesn’t rank on search engines. It’s not geared for SEO, and it was never intended for ranking.

It wouldn’t be unreasonable for an AI-generated SEO insight tool to suggest that the page is a poor performer, as it generates few clicks.

An AI-dependent team member might run with this and make a rash decision, such as deleting the page or editing it to improve its ranking, thereby destroying the original intention of the page.

What you want instead is a team of people who can take the AI insight and apply their broader knowledge of the marketing strategy to it to verify the recommendation as accurate or not.

Pillars of AI governance in SEO

Before getting into AI governance in SEO best practices, let’s review what responsible AI looks like.

For this guide, the pillars of AI and governance are:

Content quality assurance
Transparency and attribution
Workflow oversight and documentation
Tool evaluation and ethical vetting
Monitoring and measurement

Content quality assurance

Content quality assurance ensures that content meets editorial standards. To achieve this, you’ll need a human-in-the-loop review process, which may involve multiple human reviews, from the SEO specialist to the brand manager and subject matter expert.

Ideally, there will be fact-checking workflows so review teams are aware of what to check. You might include AI thumbprints, such as the words and phrases mentioned above. Stress the importance of checking studies and research to check they’re not hallucinations.

There should be benchmarks for originality, accuracy, and tone, all of which you could record in your tone of voice and brand guidelines.

Finally, establish clear rules and thresholds for what should never be generated by AI. For example, your money or your life (YMYL) topics, meaning essential topics that impact things like law, health, and news, should always be human-written

Transparency and attribution

Generally, when something is fully AI-generated, a caveat is added to inform the reader. For example, here’s what Google adds to all AI overviews.

As a brand, you need to think about what you want to disclose and when. A blog written with AI assistance, for instance, may not need disclosure, provided a human has heavily edited the content.

In circumstances where a human has conducted a significant edit, you may choose to provide the byline of the person and reflect this data in the structured data and schema, which provides additional information about the author to Google and search engine bots.

Workflow oversight and documentation

Your SEO and AI workflows need to be tested and approved, and they need to work for all teams using them.

Then, they need to be documented as an AI policy in one place. Keep a master document that you can refer back to. A tool like Google Docs is good—you have version control so you can see and roll back iterations. Google Documents also serves as an audit log, allowing you to see exactly what was changed and by whom.

Your AI documentation needs to support editorial and SEO teams as much as it does the brand. The policies need to ensure that teams can use the AI efficiently and effectively, while also setting boundaries.

Depending on the size of your organization, you may require multiple approvals for policies, including those from content managers, SEO specialists, legal teams, brand managers, and other relevant stakeholders.

Tool evaluation and ethical vetting

Tool evaluation and ethical vetting shouldn’t be a new concept. It’s become particularly prominent in the digital world, especially in Europe, with the rise of data privacy and policies such as GDPR.

These days, you need to evaluate and vet tools so that you can be transparent about how they’re used and what they track. This type of information should be readily available on your website as part of your cookie or privacy policies.

While AI usage isn’t yet as developed, AI usage in SEO and digital will likely follow the same path as analytical tools.

When it comes to AI, responsible, ethical vetting goes beyond how it’s used and includes:

Data privacy policies of the tool itself
Bias mitigation within the tool, and also how you’ll mitigate bias within inputs
Tracking, within workflows , which tools touched your content, when, and what they added
Model explainability breaking down how a tool works and why it produces specific outcomes (this understanding bridges the gap between AI output and human judgment)

If you’re taking your AI very seriously, you might start thinking about your AI options, including OpenAI, an open-source solution, or a proprietary tool. All come with pros and cons:

Open AI is versatile but, as the name suggests, open. Anything you type into it could be shared with the model. There are premium plans ($200/month) with additional guardrails .
Open-source options require technical input, but they give you more control. You can take an open-source AI tool and customize it with your own technical input.
Proprietary tools are usually built by enterprise companies like Google, OpenAI, IBM, or Microsoft. You can access these tools with a subscription. Many are built with a specific focus (IBM’s Watson, for example, is tailored to industries such as healthcare).

Monitoring and measurement

Effective and ethical AI governance doesn’t stop once your policies are created and your boundaries or appropriate use are set. AI is likely to become more ingrained in what we do and deemed more useful. Consequently, policies and processes must be updated.

As you use AI, it’s essential to monitor key metrics that indicate whether the SEO is helping or hindering. You’re looking for significant or drastic changes to help guide your decisions.

For example, look out for:

Traffic: Increases in traffic indicate that your AI content is working for you; drops suggest the opposite
Volatility in rankings: If rankings fluctuate, it could be a signal that your AI content is working for or against you, or it could be that Google is running a test or an algorithm update has occurred. Be careful not to assume every change is linked to something you’ve done; sometimes, you need to wait and see what happens for a period of time.
Crawl rate shifts: Reductions indicate that crawling your content is being deprioritized because the content is poor
Indexation drops: Significant changes to a page can cause it to be temporarily removed from the index while Google re-indexes it. If indexation drops but pages don’t return, you have a problem.

Analyze your findings carefully. Not everything you see will be related to your changes. It’s good to give everything a bit of time to recalibrate after significant content changes.

You can measure your content in categories (e.g., pieces generated with AI vs. pieces generated by humans) to see if there’s a noticeable difference in how they’re performing.

When you know how AI has impacted your site and processes, you can update guidelines and governance.

Pro tip : Tag AI-assisted content in your CMS or content database for traceability so you can track AI’s involvement.

Best practices for scaling AI responsibly in SEO

Here are some best practice tips for scaling AI responsibly in SEO. These are the types of processes you might include in your AI governance.

1. Create a workflow with every step laid out

A workflow is essential for efficient processes. The more workflows you record and document within your marketing and SEO practices, the better.

Why?

Because workflows ensure layers of human approval and editing, a documented workflow outlives things like staff churn. It becomes a part of your operations, regardless of who’s spearheading the work.

Here’s an example of the content workflow we use at Search Engine Land.

This workflow is good because it:

Includes multiple reviews of content
Details the workflow step-by-step (e.g., first draft review > second draft > second draft review > third draft > third draft review) instead of an over-simplified step like “write content”
The workflow is followed without exception

2. Create layered review protocols (SEO, editorial, brand)

You can adjust your workflow to include as many steps as you need.

For example, enterprise businesses might have multiple reviews required:

SEO reviews to get pages ranking on Google
Editorial reviews to ensure spelling, grammar, and the overall article is well-written and adheres to a high standard
Brand reviews are conducted to ensure that all claims align with the brand’s values
Subject matter experts (SME) reviews to ensure the content is factually accurate, up-to-date, and includes new, insightful findings

3. Maintain a source-of-truth page taxonomy

There may be rules governing the content that can be created using AI and the content that can’t. A clear taxonomy defines the rules regarding which pages can use AI and which require expert authorship.

For example, some blogs might be suitable for AI-generated content to be used (alongside human review), whereas legal advice or thought leadership should never use AI-generated content.

4. Use AI where it adds value—not where it fills a quota

A lot of AI use cases shared across the web share arbitrary targets and success, like “Success story: 1,000 articles in 10 days,” or “How we ranked 1,000 new keywords in a week.”

While these measures may sound impressive on the surface, they’re rather meaningless.

AI should be adding value to your business, your team, and your audience.

Instead of setting arbitrary goals, clarify where you can deploy AI in a meaningful way.

For example, “We want to understand our users’ behaviour better. Use AI to summarize user behavior on our website, then spot-check three user behavior recordings per day for a week to see if human findings align with the AI-generated summary.”

5. Start small: Test 10 pages, then scale

When implementing a new workflow, always start small. Start with a percentage of content on your site (say 5%), or start with 10 pages, then carefully monitor the results.

There are two significant benefits of working with AI like this in SEO:

Your team gets to steadily test a workflow and spot areas of improvement before it’s fully rolled out
You monitor how Google responds to the AI changes. If results tanked for those few pages, roll them back and no more content is harmed.

6. Test and iterate

Once everything is set up, don’t rest on your laurels. It feels as though the search landscape has never changed so quickly. This means we need to be agile and adaptive with our processes and workflows.

Keep an eye on what’s working and what isn’t so you can iterate processes. Listen to everyone involved in the process to identify areas for improvement.

What Google has (and hasn’t) said about AI in SEO

AI-generated content isn’t the SEO crime that it was once thought to be.

Google has been clear on that.

The search engine giant wants to see content created by people, for people , but this shouldn’t translate to Google insisting that humans write every single word. Instead, it’s about quality and value that humans can bring through edits. If a human approves AI-generated content as reliable, helpful, and suitable for readers, then it’s enough.

Google says, “Automation has long been used in publishing to create useful content. AI can assist with and generate useful content in exciting new ways.”

When it comes to scaling content with AI, it may be that the AI component is fairly low on the impact scale (providing the content is edited).

John Mueller says:

“If you grow a website significantly…by a factor of 10, then your website will overall be very different. By definition, the old website would only be 10% of the new website. This means it’s only logical to expect search engines to re-evaluate how they show your website. It’s basically a new website after all.”

With this quote in mind, it makes sense why a website may experience changes after rapid content growth. If the old website is just a percentage of the entire website architecture, it makes sense that it takes time to re-evaluate the website and also why there may be significant changes. This is why it’s important to follow the best practice of starting small.

The key, it seems, is less about AI and more about quality content. If your AI governance is in place and AI is ethically used, there should be no problems for businesses that want to use AI.

For more support on AI and its uses in SEO, read:

Measuring what matters in a post-SEO world

Wed, 13 Aug 2025 12:00:00 GMT

Before anyone panics and sends me angry DMs about SEO being alive and well, you can relax.

I’m not declaring SEO officially dead . (We’ve already had about 3,486 articles on that topic, and guess what: it still isn’t.)

But SEO as we know it? That’s changing – fast.

Google’s AI Overviews , Bing Copilot, and shrinking SERP real estate are all changing search faster than your agency’s patience for “just one more question” emails.

It’s all happening. Which begs the question: when the landscape shifts dramatically, how do we measure success in a “post-SEO” world?

Strap in, friends, this is about to get a bit uncomfortable (but in a good way).

Beyond rankings

If you’re still sending clients ranking reports and celebrating when “that keyword” climbs from Position 9 to Position 7, it might be time for a chat.

Ranking positions are nice, a bit like finding $5 in your coat pocket that you forgot was there, but they aren’t the full story, and they never really were.

Let’s face it, no one cares if you are position number one if a) it’s for a phrase no one is looking for or b) it leads to zero conversions.

In today’s crowded, AI-enhanced, zero-click, personalized SERP world (yes, I crammed all the buzzwords in), obsessing over positions alone will leave you frustrated.

Instead, it’s time to think bigger:

What happens after the click?
Are people actually engaging?
Are we making money yet?

Now, I’m not suggesting you ignore them completely, but they just don’t give you the full picture anymore.

The same is true of traffic.

Raise your hand if you’ve had a concerned email in the last six months because traffic has dropped , yet enquiries have stayed the same (or, even better, increased).

Dig deeper: SEO KPIs to track and measure success in the age of AI

User engagement metrics

Post-SEO means stepping outside the comfort of GSC clicks and impressions.

User engagement is no longer a nice-to-have metric. It’s a must-have.

When Google serves AI-generated answers right at the top, you’ve got fewer opportunities to grab attention – so when you do, you’d better hold onto it.

Here’s how we should be measuring success now:

Engagement rate (from GA4)

No more bounce rate, sorry bounce fans.

Engagement rate in Google Analytics 4 actually tells you if your visitors are doing something valuable.

Clicking around.
Scrolling.
Spending quality time together. (It’s the metric equivalent of “hanging out with friends in the pub,” except, y’know, professional.)

Scroll depth and session duration

Yes, really.

Are people actually reading your content or just skimming and leaving?
If they aren’t reaching the good stuff, why?
Are you making your messaging clear and simple (no one needs to read about your [insert acronym] process until they know they’re in the right place from the get-go)?
How easy is it for people to get to your core content?
Is your menu easy to follow?

UX isn’t just a phrase to chuck about to make you feel super knowledgeable.

It’s becoming a vital part of any SEO work.

Conversions and micro-conversions

SEO’s end goal was always leads or sales.

Measuring form fills, click-to-call buttons, downloads, or newsletter sign-ups is now essential, not optional.

They’re the real measure of whether your audience cares.

Long gone are the “bish, bash, bosh” days of landing, clicking, and buying.

We need to nurture our visitors.

Dig deeper: 12 new KPIs for the generative AI search era

Customer lifetime value (CLV)

CLV might sound painfully corporate, but stay with me.

SEO is marketing, marketing is business, and business wants to know: is this stuff working?

Tracking organic visitors’ lifetime value means examining how SEO-acquired customers behave long-term.

Are organic customers more loyal?
Do they spend more than those acquired through paid or social media?

If your SEO is good (and it better be), your CLV from organic traffic should outpace other channels.

Now that’s a metric worth celebrating.

Reputation and brand

SEO doesn’t exist in isolation anymore.

People:

See your brand on social.
Hear about you from podcasts.
Stumble upon your quotes in industry round-ups.
Then, Google you.

Or, vice versa.

So the new success markers?

Think:

Brand searches.
Direct traffic growth.
Increases in branded keyword visibility.
Single-channel marketing is a thing of the past.

Businesses need to embrace the online world in lots of different ways, and it all needs to be cohesive.

Talking about a topic in your latest blog, make sure this is filtered through to the rest of your marketing activity.

You want to work toward being known as the brand for XYZ.

With almost half of all searches being branded, you need to know that people are searching for your brand name – that’s gold dust.

Track it. Celebrate it. Report on it.

Dig deeper: The new SEO imperative: Building your brand

Zero-click visibility

Zero-click searches (thank you, featured snippets and AI summaries) are here to stay.

Frustrating? Absolutely.

But a useful metric if you view it through a branding lens? Actually yes.

Measure these impressions carefully.

They’re like mini brand-awareness campaigns running 24/7 without the budget.

But keep your eyes open.

If your impressions are skyrocketing without any corresponding clicks or conversions, your SERP visibility might be doing a whole lot of nothing.

Time for a deeper dive.

Dig deeper: How to track visibility across AI platforms

How this works in action

We recently worked with a client stuck on old-school SEO reporting.

They proudly waved ranking reports for obscure keywords nobody searched for.

Then, crickets.

So we switched things up. Started tracking:

Organic lead quality and close rates.
User engagement rate on landing pages.
Scroll depth on long-form blog content.
Brand searches over time.

The results?

Within three months, we identified their most valuable organic traffic segments and doubled down.

Rankings became background noise.

Revenue and conversions became the primary metrics.

Everyone stopped panicking when a random keyword dropped two spots.

Overall inquiry numbers went down, but the quality of the leads was getting so much better.

Instead of sitting on countless discovery calls with the wrong people, we were able to target the right leads.

This is where first-party data becomes more and more important.

It’s also where you need open and honest relationships with clients (agency side) or the sales team if you work in-house.

Tools for measuring success in the AI era

You don’t need fancy, expensive tech to do this. Your existing toolkit already includes the right stuff:

GA4 (yes, it’s not perfect, but it works) : Engagement, scroll, conversions.
GSC Insights : Brand searches, impressions, CTR.
Microsoft Clarity : Free, simple heat-maps and user recordings.
HubSpot, ActiveCampaign, or other CRMs : track organic leads right through to revenue.
Your own custom dashboards (i.e., Looker Studio ) : Visualize everything together so it makes sense for your clients or stakeholders.

You already have the data. You just need to use it differently.

Shifting mindset (without losing your sanity)

Clients won’t let go of rankings easily. Rankings are visible, simple, comforting. Who doesn’t like seeing those little green arrows?

But education starts with you.

Have open conversations.
Get people on board with the bigger picture – engagement, conversions, brand visibility.
Give context.
Talk about AI shifts.
Offer them numbers they can take to their boss. Revenue, leads, and lifetime value talk louder than ranking fluctuations.

SEO isn’t going anywhere, but it’s changing fast.

The ways we measure our success need to evolve even faster.

We’re not just “SEO people” anymore.

We’re marketers, brand builders, user-experience obsessives, and yes, even amateur psychologists.

The SERPs evolved. Users changed. AI happened.

Time to update our metrics and celebrate what really matters.

Your clients, your stakeholders, and your team (and even your sanity) will thank you.

Rankings? They’ll mostly take care of themselves.

Boosting Local Presence: Why Local SEO is Essential for Small Business Success

Tue, 12 Aug 2025 21:38:57 GMT

In the digital age, the marketplace is vast and global, yet the heart of commerce remains local. Small businesses, the backbone of our communities, must harness the power of local SEO to ensure they stand out in local searches and attract customers who are ready to make a purchase. At Hometown Beat , we understand the unique needs of small businesses and provide customized strategies to boost their local visibility, ensuring they remain competitive in an ever-evolving digital landscape.

Understanding Local SEO

Local SEO is a specialized branch of search engine optimization that focuses on optimizing a business's online presence to attract more customers from relevant local searches. These searches occur on search engines like Google and Bing, as well as on social media platforms. By optimizing for local search, businesses can appear in local listings, increase their visibility, and drive more foot traffic to their physical locations.

Key Components of Local SEO

Local SEO involves several critical components that work together to improve a business's local online presence:

Google My Business: An optimized Google My Business profile is essential. It helps businesses appear in local search results and provides critical information such as address, phone number, and hours of operation.
Local Citations: Consistent and accurate mentions of your business name, address, and phone number (NAP) across various online directories bolster credibility and search engine rankings.
Online Reviews: Encouraging positive customer reviews on platforms like Yelp and TripAdvisor enhances your business's reputation and reliability.
Localized Content: Creating content that resonates with local audiences, such as blog posts about community events, helps engage potential customers.

Benefits of Local SEO for Small Businesses

Implementing local SEO strategies provides numerous advantages for small businesses, enabling them to level the playing field against larger competitors:

Increased Visibility: Businesses can significantly improve their online presence by appearing in the coveted Google "Local Pack" or "3-Pack," thus increasing their chances of being noticed by potential customers.
Targeted Traffic: Local SEO attracts visitors who are specifically searching for products or services that you offer, leading to higher conversion rates and greater return on investment.
Community Connection: By emphasizing local relevance, small businesses can build stronger connections with their community, which fosters brand loyalty and repeat business.
Competitive Edge: Optimized local SEO ensures small businesses can compete effectively with larger corporations by being more visible to local customers who prefer to support local enterprises.

The Role of Local SEO in Digital Marketing

Local SEO is a cornerstone of a comprehensive digital marketing strategy. It complements other marketing services offered by agencies like Hometown Beat , including Social Media Management and Website Design & Management . By integrating local SEO with broader digital marketing tactics, small businesses can create a cohesive approach that amplifies their brand visibility and drives growth.

Ultimately, local SEO is not just a tactic; it is a vital component of a successful business strategy that enhances engagement, maximizes potential, and builds lasting relationships within the community.

The $1 trillion generative economy that smart SEOs will own

Tue, 12 Aug 2025 13:00:00 GMT

Search is changing . The industry as we know it will radically alter in almost all aspects as we enter the “generative economy.”

By 2034, the generative AI market is expected to be worth roughly $1 trillion .

This article outlines how SEO professionals can own the generative economy and why they must embrace the change that is coming.

No, it’s not ‘just’ SEO

While there are many cross-over skills, GEO isn’t just SEO.

SEO works on the premise that ranking on page one for keyword variations that are typed into search engines by potential customers.

It matters because, for the last decade, the best place to hide a body has been on Google’s Page 2.

Humans don’t scroll past Page 1, because it is highly inefficient to do so.

LLMs and AI-powered search platforms don’t have this problem.

They can visit hundreds of websites in seconds for a variety of search terms and use their internal data.

Ranking does not matter in this world.

You can be on Page 5 of a web search and still get found and chosen by the LLMs.

Search engines organize the world’s information, and they do this exceptionally well.

Humans, however, are terrible at searching.

And this is among the largest differences between SEO and GEO.

Humans are being replaced in this process.

In SEO, businesses have been taught to target keywords that drive the largest potential commercial match.

But that doesn’t equal the best customers.

This is because keywords have represented the only way for humans to find what they want online, which means broad keywords drive the largest commercial terms.

And the long tail tended only to be a few words long for much the same reason.

Businesses only went after lucrative terms to win the most commercial traffic.

Or if deemed “too difficult,” SEO was turned into a blog channel, targeting non-commercial terms.

AI-powered search changes this.

It is easier than ever for consumers to find the products or services that best match their needs.

Ranking on Page 1 is no longer the goal. So what is?

How to win the generative game

AI search is vastly better than human search, so it will likely become the dominant form of search online.

Organic search will not disappear completely. It does a perfect job of surfacing businesses through direct or navigational search.

However, for situations where a chosen product or business is not known, AI-powered search will be able to find the best match – and fast.

It’s easy to see why this is so valuable for businesses.

A business can find the consumers it seeks to serve more easily.
A business will convert customers faster.
A business will be able to identify and indeed expand into new markets where customers are currently being either ignored or poorly serviced by existing providers.

Often, these customers have been ignored because a brand’s profit margins are insufficient in these sectors.

AI-powered search changes this.

It will allow more businesses to activate organic search as a driver of revenue.

This is because GEO has a different value proposition.

Dig deeper: The new SEO imperative: Building your brand

The value proposition of GEO compared to SEO

GEO revolves directly around three core positions.

The customers a brand is trying to target.
The products and services you sell.
The differentiation of the value the business offers compared to others.

SEO revolves around keywords.

We have openly observed the “SEOing” of webpages to find ways to force keywords into things.

More lately, this is seen with the resurgence of exact match domains ranking.

On top of that, SEO’s value proposition has largely been: “Buy now, rank six months later.”

GEO is different. It is optimizing a business around its strategic brand positioning.

Suppose a business wants to set up as a broker of car insurance for female first-time drivers.

GEO and AI-powered search will allow the business to find more of the customers it seeks to serve.

Something that can be evidenced today with a quick search comparing Google and ChatGPT.

The ChatGPT search gives me exactly what I asked for, whereas a similar keyword-focused search mainly returns the large insurance comparison sites.

AI-powered search can help humans find businesses that closely match their needs – opening up untapped business and consumer opportunities.

Dig deeper: How AI is reshaping SEO: Challenges, opportunities, and brand strategies for 2025

How GEO works – and why SEOs are best placed to help

Generative engine optimization is all about understanding the information AI search platforms need and supplying this information.

And what they need is “mutual information.”

Machines hate ambiguity.

GEO is about supplying enough information about a brand’s positioning so that, when someone uses an LLM to find a solution to a problem the business solves, the likelihood of that LLM referencing the business increases.

Say you need an employment solicitor offering free online consultations.

Traditional SEO targets a keyword like “free online advice for employee rights.”

An LLM instead:

Breaks down your request.
Searches across multiple queries.
Weighs everything from case studies to testimonials before recommending a firm.

At a technical level, you can see how this works.

You need:

On-page optimization to ensure a brand’s positioning is clearly stated.
Off-page mentions of a brand related to that positioning.

However, most businesses are considerably underoptimized for LLMs.

In our example, you might offer a free consultation on employment law.

Other elements, such as not explicitly stating that you service the whole of the UK or that you specialize in sexual discrimination and have case studies on your site detailing your wins, might exclude you from being returned in a generative result that matters.

GEO doesn’t just need on-page optimization.

You need those off-page signals as well.

Brand mentions related to your positioning.
Success stories.
Listicle placements.
Directory placements.
Podcast interviews.
News stories.

The list goes on.

You must supply the “machines” with enough information to increase the likelihood that they are “certain” you are a good solution for their user’s query.

You must satisfy the machines. AI is the gatekeeper now.

If this sounds just like good SEO, yes, you’re right, it does.

The difference here is that you’re not chasing keywords.

You’re optimizing for online presence in terms of the business’s positioning.

This means that the business needs to have a position in the first place.

Many businesses have been built around organic or paid search keywords.

SEO and or paid search has allowed them to win big.

AI-powered search changes the game considerably, and as it grows in usage, brands that previously did well in organic and paid search will naturally see a reduction in leads and sales.

And SEOs are the single most experienced people to help brands traverse this new search world.

Dig deeper: LLM perception match: The hurdle before fanout and why it matters

The battle between SEO and GEO is just beginning

The customers are still out there.

That’s key when we talk about SEO and GEO.

And right now, the vast majority of your customers are using traditional search engines.

SEO is still very much needed.

Eventually, Google will need to make AI mode its default mode to prevent the loss of users.

Until this happens, things are going to be messy.

Websites will lose traffic, rankings, and sales.

This will be due to Google’s constant updates and users’ movement between search modes and tools.

It’s not that the customers aren’t there.

It’s just that those who have mainly enjoyed stable revenue generation from both organic and paid search will see a change happening.

At the same time, you might “accidentally win.”

Your business might gain more leads and sales due to the splintering of search.

We are in a period of transition.

So, what should you do?

Dig deeper: SEO beyond the website: Winning visibility in the AI era

It’s SEO and GEO: The importance of bothism (for now)

Whether SEO keeps the name or evolves into GEO , the reality is that search rankings will lose much of their value.

That’s the real meaning behind “SEO is dead” when people talk about the rise of GEO.

We are in a turbulent transition period.

But this doesn’t mean you throw SEO in the bin.

We already know that SEO carries value into generative engines.

And you’ll likely see your websites gaining more traffic from LLMs.

So, right now isn’t the time to abandon SEO either.

Smart brands should be adopting a “bothism” approach.

Looking at leveraging LLMs and building their brands digitally – and preparing for the eventual switch to default AI mode.

This won’t be gradual. It’s not a “sometime in the next five years” shift.

It’s happening soon.

But how can you get your business ready for this?

The ROI of the generative economy

I started this article with a simple premise – that SEOs can own the generative economy.

And they can.

However, we need to tackle the pain in the backside of all marketers: ROI.

Paid search has thrived because it gave business owners certainty around marketing spend.

ROAS was easy to calculate, and as a result, creativity and brand marketing were ignored.

GEO makes tracking attribution from SEO seem like a day in the park.

And as a result, we are seeing the rise of platforms dedicated to showing the visibility of brand mentions in LLMs.

These tools show potential presence in LLM-based results by using a range of “pseudo” prompts fired into the platforms via APIs.

It’s not a real result, because a user didn’t do the prompt.

SEO has always been a hard sell.

Budgets are low because it takes time to see results, and often those results have been harder to attribute to revenue.

GEO is different.

You are paying for deliverables rather than results.

Yes, those deliverables should lead to business results.

And those deliverables will create value.

Your on-site optimization is a deliverable.
Any content that you create is a deliverable.
Being mentioned in a listicle is a deliverable.
That directory listing you added is a deliverable.

In this sense, GEO becomes more palatable as a service, because you are doing “sensible and visible” brand marketing from Day 1.

GEO is more closely linked to copywriting, brand marketing, advertising, and PR than SEO ever was.

For SEOs, this is a good thing. You are tethered to activity and not rankings.

At its heart, GEO is about strengthening a brand’s digital presence around its brand positioning.

And this is why businesses should be investing in their online presence.

Now is a perfect time to look at your organic traffic and prepare.

Plan for all scenarios.

Prepare for AI Mode being switched on next week, and assess the potential business impact.
Anticipate a loss of leads and revenue, and map out how you will recover that lost revenue (which might mean activating other channels).
Review your online brand marketing, and identify where your brand is – or isn’t – listed.
Evaluate the publicity your business has earned over the last few years, and determine if it’s enough.
Check your brand search – does it exist?
Audit all other marketing channels – are you leveraging social, email, and more?

Which leads me to the final point.

SEOs can own the generative economy – but it doesn’t mean they will.

Dig deeper: In GEO, brand mentions do what links alone can’t

The competition was paid search – now it’s PR and copywriters

SEOs are indeed well placed to supply services to win the generative economy. But they aren’t the only ones.

As Page 1 rankings lose their value, so will the skill sets of thousands of SEOs.

Despite the weird desperation of SEO consultants to overcomplicate search. It now needs to become more polished and brand-focused than ever.

Brand marketing, PR, and copywriting are among the most critical skills for SEOs to understand moving forward.

Good GEO will be about managing, building, and increasing the value of a business’s online reputation and presence.

But equally, that also means that SEOs can start to enter markets they haven’t worked in before.

Adding PR and copywriting to an SEO agency’s range of services is a natural step that many are doing.

And this is where we’re all heading.

But for SEOs – no matter how much they might hate change – the future is brighter and more exciting than ever.

Embrace it.

Dig deeper: Why AI will break the traditional SEO agency model

SEO beyond the website: Winning visibility in the AI era

Mon, 11 Aug 2025 13:00:00 GMT

When was the last time you clicked through from an AI-generated answer to a brand website?

These days, it’s rare – thanks to how AI-powered tools like ChatGPT , Perplexity , and Google’s AI Overviews deliver information.

Visibility now means being referenced in trustworthy sources used by LLMs , far beyond your own website.

In this new environment, SEO teams can’t optimize everything on their own.

They need to evolve from executors to internal partners and consultants, working across content, product, PR, data, and engineering to influence visibility where your audience actually finds information.

Still not convinced? Continue reading to learn why SEO is now a company-wide strategy – and how your business should shift to stay competitive.

From ranking to referencing

The traditional SEO mindset focused on ranking, where ranking in Position 1 often meant claiming the lead.

Now, when users turn to ChatGPT, Perplexity, or AI Overviews for a recommendation, the answer is instant. No click-through needed.

To make things even more fun, the information in the AI answer is sourced from multiple sources across the web.

Your site might never appear, even when your brand is there.

And in this new reality, what matters is whether your brand, product, or perspective appears in the AI’s response.

This changes your optimization goals and calls for a complete strategic rethink.

In the AI era, a crucial part of your job as an SEO is shaping how the brand is talked about across ecosystems, not just how it performs on a SERP.

That means you also need to collaborate more closely with teams like content, comms, product marketing, data, and engineering.

Influencing AI visibility is now a shared responsibility.

Dig deeper: AI visibility: An execution problem in the making

Where AI tools look for information

The first step in building this new AI strategy is understanding where these tools source their answers and how they choose what to present in their answers.

A big part of this is still a black box.

Studies from Profound , Writesonic , XFunnel , or Rankscale have already provided some insights and lists of the current winners in the AI visibility race.

Here is a quick roundup of the most influential sources:

Reddit ’s dominance in AI results isn’t surprising, given its user-generated content and licensing deals with Google and OpenAI . (Even without a deal, Perplexity also favors the platform .)
Monitor relevant threads and product mentions. Don’t push – participate where it makes sense.

Wikipedia

Wikipedia’s strict policies and multi-layered reviews make it one of the most authoritative sites – a quality AI answer engines tend to favor.
Ensure your brand or topic has accurate, well-cited mentions where appropriate. If possible, create your own Wikipedia page, but remember to follow all their guidance.

YouTube

It’s surprising how often people forget YouTube is its own search engine, with a long-standing presence in Google results.
The only new twist is how often its videos now appear in ChatGPT’s answers.
Use clear headings, descriptive metadata, and transcripts to influence visibility in AI tools.

Quora and other subject-matter expert websites

Forum sites like Quora, Stack Overflow, and other specialized communities are valuable for their subjective yet expert insights.
Contribute answers under a verified profile where your brand has relevant expertise. Bonus: By contributing to these platforms, you also lay the foundations of your brand community.

LinkedIn has earned its place among AI sources. It’s often cited for business-focused prompts that call for thought-leadership insights or practical tips.
LinkedIn posts should be part of your content strategy. Use clear, direct language and original points of view. Post from profiles of people who can be your brand ambassadors.

Review platforms and marketplaces

Websites like G2, Capterra, Trustpilot, and other vertical-specific directories find a special place in AI answers, especially for commercial prompts when users are searching for a solution.
Optimize your profiles on these platforms. Find other relevant platforms that you can join.

Instagram

With Google now indexing Instagram posts in some instances, its visual content may appear more often in AI Overviews, especially for local or consumer brands.
Make sure your visual posts have well-optimized text around them (i.e., image alt tags and captions) and that your profile information is accurately optimized.

External media and listicles

Be present in roundups, “best of” articles, and trusted publications. These links from external sources will build the perception of your brand in AI tools.
Try to be present on external authoritative websites. Digital PR is now one of your winning strategies.

This list is far from exhaustive, but it shows that visibility depends on being referenced across many platforms and websites.

How SEO can partner across the business to win AI visibility

Winning visibility in AI results isn’t something SEO can deliver alone.

If the goal is to get your brand cited in the sources AI tools trust, every department has a role to play – from content and comms to product marketing, PR, data, and engineering.

That means SEO’s role has to expand. It’s no longer just about optimizing owned pages. It’s about acting as an internal consultant:

Shaping strategy.
Advising on discoverability across platforms you don’t control.
Getting involved early – before campaigns, product launches, or content go live.

Below are some examples of how SEO can work with other teams to win AI visibility.

Content teams: Building AI-friendly assets

Identify topics and formats more likely to be cited in AI results (e.g., Reddit-friendly answers, FAQ sections, expert roundups, topic summaries).
Optimize titles, structure, and metadata so content is easy for AI to summarize.
Explore guest posting or repurposing blog content into Quora answers to earn high-authority mentions.
Create original, customer-focused content AI models can easily interpret, such as listicles or use case articles tied to specific pain points.

What to expect: Platform-native ideas, structured feedback, and content that “travels” across channels.

Ideal outcome: Content that ranks in search and gets referenced in AI answers.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Product marketing: Strengthening discoverability through naming and positioning

Provide search and AI insights when naming products or crafting key messages.
Identify high-intent terms for inclusion in category listicles (e.g., “best tools for X”).
Optimize profiles on relevant marketplaces like G2, Capterra, or niche platforms.

What to expect: SEO review of messaging and guidance on boosting visibility in third-party channels.

Ideal outcome: Products that appear in AI-powered recommendations.

PR and comms: Securing AI-trusted coverage

Suggest story angles and topics already driving search interest.
Target high-authority publications that LLMs tend to favor.
Ensure mentions and backlinks are structured in ways AI can parse.
Test pitching to publications that frequently surface in your category’s AI answers.

What to expect: Search-backed media ideas and domain recommendations.

Ideal outcome: PR wins that work for awareness and AI discoverability.

Dig deeper: Why PR is becoming more essential for AI search visibility

Research teams: Turning insights into AI citations

Present findings with clear headlines, quotable stats, and concise summaries.
Focus on questions that fill search gaps or align with trending topics.
Optimize research landing pages for external pickup and sharing.
Test posting key stats on LinkedIn to see if they surface in AI summaries.

What to expect: Editorial guidance to package research for maximum pickup.

Ideal outcome: Data that’s cited by journalists, bloggers, and AI tools.

Engineering: Keeping your site AI-ready

Maintain fast, crawlable, indexable pages.
Implement structured data , LLMs.txt , or IndexNow for improved bot access.
Monitor technical health continuously, not just during audits.

What to expect: Clear technical priorities and diagnostics.

Ideal outcome: A site that search engines and AI models can easily find, parse, and trust.

Customer teams: Surfacing credible proof

Identify and amplify high-value customer testimonials.
Prioritize third-party review platforms that appear in AI answers.
Check how your brand is mentioned in Reddit threads or category discussions.

What to expect: Guidance on which customer stories and channels will have the most AI impact.

Ideal outcome: Authentic social proof that AI can pull into relevant answers.

These partnerships work best when SEO teams are embedded early, helping shape campaigns and assets before they’re launched – not after.

The cost of not evolving

When your brand isn’t discoverable, growth quietly stalls. No leads. No pipeline. No presence where decisions are made.

And it rarely looks like failure – it looks like missed potential.

You might be ranking well, but a competitor is the one showing up in AI summaries, Reddit threads, YouTube explainers, and review listicles.

You might launch a great product, but if it’s named in a way no one searches for, it won’t surface where it should.

You might publish valuable research, but if it’s not structured or distributed for AI to reference, it stays invisible.

This is what it means to fall behind today: not being part of the answer when the answer is generated.

Dig deeper: From search to answer engines: How to optimize for the next era of discovery

Start small, shift smart

Visibility today is about being referenced by AI tools. That means showing up on platforms you don’t own and in conversations you didn’t start.

Traditional SEO still matters, but it’s too focused on owned sites.

Start small:

Choose two or three platforms where your audience is active – Reddit, YouTube, LinkedIn, Instagram.
Test simple actions like improving transcripts or joining relevant threads.

At the same time, shift how your SEO team works:

Join earlier conversations.
Partner with other teams.
Influence visibility wherever it can be won.

The goal isn’t to overhaul everything – it’s to evolve.

Surviving the search revolution: 7 lessons from losing weight with AI

Mon, 11 Aug 2025 12:00:00 GMT

No, this isn’t clickbait. It’s a story of how everything is about to change.

Lately in SEO circles, there’s been a debate: will AI “search” overtake Google search?

Some insist yes, Google’s days are over. This is usually followed by a pitch for them to do AIO for you (or GEO or AEO or whatever it’s being called this week).

And then there are some who confidently say news of Google’s demise is greatly exaggerated.

Most of the talk about AI is just hype, and Google’s not going anywhere. Just do SEO the way you’ve always done it and you’ll be fine.

I hear many opinions on both sides, but there’s one thing I’ve never heard anyone articulate.

What exactly will “search” look like in a few years?

Déjà vu all over again

These days remind me of 1995.

Back then, the internet was mostly email, Usenet groups, and chat with fellow students and nerds across the country.

As an undergrad at Rutgers, I did a project called Business Uses of the Internet.

I sent a short questionnaire to webmasters of well-known brands. Most replied kindly, but very few predicted what was coming next.

I pulled up pizzahut.com during my final presentation and said one day we’d all order pizza from our computers.

My professor and classmates stared in disbelief. (This was three months before amazon.com launched.)

Today feels the same with AI and large language models (LLMs).

Everyone agrees AI will change the world, but nobody knows exactly how.

Right now, AI feels like a parlor trick – fun but not practical – much like Pizza Hut’s website was back then.

But just like online ordering revolutionized food delivery, AI is poised to disrupt industries in ways we can barely imagine.

What seems small today will look monumental in 30 years.

Saddle up

Like many Americans, I’ve struggled with weight loss for years.

In 2009, I ran a blog about using Wii games and a popular diet plan to lose weight.

I lost 40 pounds that year (and gained it back), then 25 pounds in 2012 (and gained it back again).

This year, I hit my highest weight ever – 238 pounds.

My daughter started fourth grade, qualifying her and our family for a free Every Kid Outdoors pass to National Parks.

We planned two trips: one to Utah and Arizona in February and another to Montana and Wyoming in July.

Both were planned with the help of ChatGPT and ended up being among the best trips of my life.

One must-do activity on our list? Horseback riding in Glacier National Park.

The concessionaire was clear. You must weigh 225 pounds or less to ride, no exceptions, no refunds. I had seven months to lose 13 pounds.

For months, I watched my diet and hit the gym a few times a week. By mid-June, I was still 231 – only seven pounds lost in seven months.

At a doctor’s appointment, I asked for a quick fix to lose six pounds in 20 days. “Cut out carbs,” he said. Simple enough.

When I got home, I turned to ChatGPT.

Here’s a copy of the actual chat so you can read it for yourself:

https://chatgpt.com/share/688b8819-3604-8002-b5de-7e0e89e79008

Good basic information from AI, but nothing ground-breaking

ChatGPT gave me advice I’d heard all my life: create a calorie deficit, cut carbs, hydrate, walk, get good sleep.

But here’s where things changed.

I gave ChatGPT my weight, and it told me my exact maintenance calories and how big a deficit I’d need daily to start losing weight.

I’d developed a rough idea of what “maintenance calories” meant for years, but I’d never had anyone calculate mine so precisely.

Intrigued, I started asking questions, including some questions I’d be hesitant to ask a human for fear it was a “dumb question.”

Would working out add muscle weight?
How many steps are 20 minutes on a treadmill?
Why can’t I get rid of belly fat?

ChatGPT answered each one clearly in a way that was tailored to me.

Finally, I asked it to put together a 20-day plan for me.

It came through with a plan telling me the exact calories to eat and calories to burn each day.

It threw in reminders to focus on high protein, high fiber, low carbs, and no sugar, alcohol, or refined carbs.

It even made me a spreadsheet to track weight loss.

Helpful. But so far, nothing a good diet coach couldn’t do.

Dig deeper: How to blend AI and human input in your content approach

The real game changer

One morning at breakfast, I told it what I ate and asked it to calculate the calories. It did.

Then I asked if it could track them daily rather than having me fill out a spreadsheet. It agreed to.

From then on, after every meal and even after every little impromptu snack, I’d pull out my phone and tell ChatGPT exactly what I ate.

When I exercised, I’d report that too.

No “points,” no “diet,” no coaches, just me opening my phone (which I had with me at all times anyway).

Over time, I started getting more creative in what I’d ask.

While I was standing in the middle of Costco one day, I asked it for a shopping list of healthy snack ideas and for “permission” to eat a $1.50 hot dog. (It gave both to me.)

There were a few times that my daughter wanted to eat dessert with me.

If I’d been managing on my own, I would have said absolutely not.

But ChatGPT gave me advice about portion size that literally let me have my cake and eat it too.

There were a few times my weight shot up 3-4 pounds, causing me to panic.

When I told ChatGPT, it reassured me that it was just water retention from salty food.

There was one time I had an overripe banana and some strawberries.

I asked for a simple recipe for a banana smoothie that I could make with my portable blender. It came through.

At one point, I got tired of typing in every piece of food I was eating, so I started sending it photos of my plate.

It analyzed the photos and gave me an accurate breakdown of the calorie count.

In short, ChatGPT was the embodiment of the “butler” I mentioned in my article back in April 2024 .

At the end of each day, it gave me a calorie rundown. Each morning, I weighed in.

I hit 225 in four days.

By Day 8, I was 219.

On Day 15, I was down to 217.

Travel test

On Day 17, we flew to Montana.

Travel would be dangerous for me. Unlimited plane snacks, airport lounges, and restaurant food were a recipe for disaster.

Worse, I had no scale for the next three days, so I’d be flying blind. So I asked it to tailor my diet for the rest of the trip.

Finally, the moment of truth. The time came for me to step on the scale.

Final weigh-in: 209 lbs.

Here’s what I looked like on the horse.

And here’s what I would have missed out on (in addition to $150) if I didn’t make it.

Welcome to the future

Why did AI succeed where my own attempts at dieting failed?

Like I said, this moment in history reminds me of 1995.

At the time, the Pizza Hut site was considered cutting-edge. No one could imagine how the web would change in just a few years.

Most people I observe using AI today are still in the “dabble” phase.

Some use it like a search engine, and others use it occasionally for a quick task or two.

But it’s when people start to incorporate AI into their daily lives that everything will change.

In this case, here’s why I think AI worked for me when so many diets, programs, and books didn’t.

It gave me solid, basic information, which is all I really needed

Most people don’t need cutting-edge research for something like dieting. We just need the fundamentals applied to our situation.

AI happens to be good at the basics because the web is saturated with basic content, and we have Google to thank for that.

For the last 27 years, it has conditioned us to create endless content for head terms while largely ignoring the vast long tail.

It didn’t judge me

I was able to ask ChatGPT “dumb” questions, sometimes over and over again, without feeling embarrassed or self-conscious.

That encouraged me to ask more questions.

It talked directly to me

There wasn’t a lot that ChatGPT told me that I couldn’t find on a blog or forum.

The difference is that I didn’t have to dig for it; it distilled exactly what I needed and filtered out the noise.

It encouraged me

We’ve all experienced where ChatGPT can get a little too flattering and unctuous.

But for something like losing weight, the encouragement and motivation completely worked.

It had endless patience

A human coach would’ve gotten sick of me by Day 2. ChatGPT never tires.

I wasn’t programming the AI. The AI was programming me – in a good way.

Now, I have experience, knowledge, and tools I can use the next time my weight balloons out of control.

And I find myself instinctively being “good” in my day-to-day after this experience.

Editor’s note: This story reflects a personal journey using AI to assist with weight loss. It is not medical advice. Readers should consult healthcare professionals before making changes to diet or exercise routines .

SEO is dead – long live SEO

For 27 years, Google has conditioned all of us in SEO to focus on the head.

Create content such as “What is SEO,” rank in the top 3, and watch the money pour in.

Unless you’ve been living under a rock, you know that that hasn’t been the case for a while now.

Search Engine Land has the Position 2 organic ranking for “SEO.”

But that ranking doesn’t mean what it meant 10 years ago .

For years, we’ve looked at these SERP changes as nothing but a cash grab from Google.

The reality? It might be Google trying to survive.

Google sees the writing on the wall .

For searches with informational search intent, people just want the answer.

If they don’t find it on Google, they’ll go to ChatGPT or another AI chatbot.

Does that mean SEO is dead?

No, as long as humans are looking for things, search engine optimization will exist .

But the concept of a “search engine” is changing right in front of us.

If your whole business model is built on being the top Google result for a generic question that AI can now answer in two seconds, you’re on borrowed time.

Those “searches” will be happening somewhere else.

In the past, SEO was about getting people to find your content.

Going forward, SEO will be about getting people to find your brand, making sure it’s in the conversation when your topic comes up.

The days of ranking generic content for a head term, sitting in the top spot, and driving huge traffic are almost over.

When users ask AI about your topic, it will give a solid answer without necessarily citing you (and even if it does cite you, most people won’t click).

“Ranking” in Google is about positions, clicks, and impressions.

“Ranking” in AI means something altogether different:

Will AI recommend your brand over your competitors when it’s having a conversation with the user?

The opportunity now is to ensure that your brand is top-of-mind for AI so that it mentions you naturally in the flow of conversation or in response to a follow-up question.

Dig deeper: AI search is booming, but SEO is still not dead

How to thrive in this new world

Ironically, there has never been more opportunity than today to be a search expert.

Think about it. When someone has a “conversation” with AI and asks it something that’s beyond its training data, how does it learn more?

It searches.

And not just Google or Bing.

AI will query every searchable place it can find, including Amazon, YouTube, Reddit, and your own site’s search engine.

It’s not looking for broad, generic info.

It’s looking for detail and nuance – the kind of specific, in-the-weeds knowledge most SEOs have been ignoring for the last 27 years.

That’s where the opportunity is: Search isn’t dead. It’s about to explode.

Here’s what you need to do to get ready.

1. Be an indispensable brand

Nobody searches “books” to find Amazon.

Nobody searches “hamburgers” to find McDonald’s.

Nobody searches “video game console” to find Nintendo.

These brands have executed so well that people skip the search step entirely.

And their brands have reached the point where Google or AI will look foolish for not recommending them when the subject comes up.

A lot of companies hiring SEO talent don’t have SEO problems, they have brand problems.

They’re frustrating customers, alienating prospects, and burning employees.

No amount of “SEO” will ever fix that, and their problems will only be compounded in the coming age if they don’t fix them.

Dig deeper: The new SEO imperative: Building your brand

2. Stop ignoring the niches

Google has trained us for the last 27 years to chase the big head terms.

If Semrush shows a keyword with under “10” searches a month, we dismiss it as worthless.

In the AI era, those extreme long-tail queries are where the real opportunity is.

AI will likely be the one conducting those searches on the user’s behalf.

The first step is to identify underserved niches by geography, audience segment, or specific use case.

And no, the answer is not to hire an army of inexperienced freelancers, and it’s definitely not to have AI churn out watered-down content.

3. Publish deep knowledge and original insights

Go way beyond “FAQs.”

Build rich knowledge bases and web content that take the expertise locked inside your team’s heads and internal documents and make it public for AI, Google, and, most importantly, your customers to find.

Make sure it’s crawlable . Go deep.

Provide insights and helpful information, especially around your brand and your unique value propositions, including things that have never been told before.

The stuff you think is “too detailed” or “too obvious” is often exactly what people need and will be asking their AIs about.

Yes, you’re letting AI “learn” from your content. But you’re also training it to trust you.

If AI sees your site as a reliable source of useful, relevant, and solid information, it’s going to remember you when someone asks for recommendations.

Dig deeper: Want to beat AI Overviews? Produce unmistakably human content

4. Share real stories, not soulless content

The “uncanny valley” used to refer to AI-generated work that just seemed “off.”

These days, of course, generative AI can produce images and videos that look completely real.

A new “uncanny valley” is taking its place, especially as generative AI starts to be used universally.

You may have experienced it yourself.

The words or images may be perfect, but there’s still something hollow and soulless about them.

Tell stories on your site. I’m not talking about fake-sounding “testimonials” or overly polished case studies.

I’m talking about authentic conversations with real people about your products and services.

How they used your product.
What went right (and wrong).
What they were able to accomplish.
What they learned.

These are all details that humans will crave after AI gives them generic answers.

5. Build communities

Google and OpenAI are paying millions to access Reddit’s data for a reason: it’s still one of the last places on the web where authentic conversations are happening.

A lot of those conversations aren’t exactly high quality.

Brands have an opportunity to bring back their own moderated spaces where people can have real, troll-free discussions.

Well-moderated forums continue to be the most effective method of generating relevant, helpful, timely, and detailed content at scale.

It always has been, but brands couldn’t justify the ROI.

Since I wrote this article last year, Reddit’s market cap has gone up $25 billion. It might be time to redo the math on that one.

And don’t forget the “community” of reviewers, whether on your site or third-party sites like Trustpilot, Amazon, Yelp, or Glassdoor.

Those are going to become even more valuable as AI leans on them for signals. That doesn’t mean faking reviews, it means doing item 1 above.

6. Stop acting like a robot

For years, SEO professionals (and the people hiring us) have thought of our job as “understanding the tech.” That used to be true.

Not anymore.

In the AI era, the technology matters less. Understanding humanity matters more.

Stop thinking:

“How do I get a million visitors from one keyword?”

Start thinking:

“How do I be the brand that can answer a million different questions in ways that actually help people?”

7. Understand your users and how they’ll use AI

Here’s a good exercise. Just like I did with my diet plan, go to your favorite chatbot and try to accomplish a goal, the way your customers would.

Don’t just type a keyword into the box like you would on Google.

Have a conversation. Try to get something done.

You’ll find some things AI gets uncannily right, some it gets embarrassingly wrong, and some it can’t do at all. Those last two are your opportunities.

Dig deeper: How generative answers are changing the user journey

Conclusion

Hall of Fame baseball player “Wee Willie” Keeler famously said “Keep your eye on the ball and hit ‘em where they ain’t.”

I think that’s fitting advice for the coming AI age.

The ball is and always has been your customer.

AI can do a lot of things well – scarily well.

If it ultimately helps your customers, embrace it and figure out how to make your brand top-of-mind in conversations.

But there are some things it can’t do yet, and there are some things it’ll never be able to do.

Figure out what those are, and do them, starting with this list.

Why your website needs a GEO audit now by Tor.app

Mon, 11 Aug 2025 11:00:00 GMT

By 2026, AI-powered search is projected to capture 25% of the global search market. That’s a $72 billion slice of digital traffic.

Yet, 89% of websites remain completely unprepared for this shift.

The solution? Geoptie’s free GEO audit tool diagnoses your generative engine optimization readiness before it’s too late.

While your competitors scramble to understand why their traffic is declining, forward-thinking marketers are already mastering GEO for the next generation of search. The difference between winners and losers in this transition comes down to one thing: understanding exactly where your content stands in the AI visibility spectrum through proper GEO analysis.

What makes Geoptie’s free GEO audit different from traditional SEO analysis?

Traditional SEO tools measure keywords, backlinks, and page speed. But AI search engines like ChatGPT, Claude, and Perplexity don’t care about your “domain authority.” They care about how easily they can understand, trust, and cite your content – the core principle of GEO.

Geoptie’s professional free GEO audit examines your website through the lens of AI algorithms, not Google’s ranking signals.

This fundamental shift from SEO to GEO requires new metrics, new strategies, and (most importantly) new diagnostic tools built specifically for the AI era.

The 6 essential dimensions for GEO

After analyzing more than 10,000 websites for AI optimization, Geoptie’s data reveals six critical dimensions that determine whether AI systems will reference your content:

1. Citation readiness (25% weight)

Your content might be brilliant, but can AI quote it? Citation readiness measures how easily AI systems can extract and attribute specific information from your pages. Content that excels in GEO contains:

Clear, factual statements between 20-150 characters.
Properly attributed statistics and data.
Distinct, quotable insights that AI can confidently reference.

2. Answer alignment (20% weight)

AI systems prioritize content that directly answers user questions. This GEO dimension evaluates whether your content structure matches how people actually ask questions in conversational AI interfaces. High-scoring GEO features:

Direct answers in the first 100 words.
Conversational tone that mirrors natural language.
Complete context without requiring additional sources.

3. Knowledge graph integration (15% weight)

Unlike traditional search engines, AI systems build comprehensive knowledge graphs to understand relationships between entities. Geoptie’s free GEO audit reveals how well your content contributes to these knowledge structures through:

Clear entity identification (people, products, organizations).
Explicit relationship mapping.
Consistent terminology and definitions.

4. Content authority signals (20% weight)

AI systems need to trust your content before citing it. Authority in GEO isn’t about backlinks – it’s about demonstrable expertise. The audit measures:

First-hand experience indicators.
Transparent methodologies.
Verifiable credentials and expertise markers.

5. Technical AI accessibility (10% weight)

Can AI crawlers efficiently parse your content? Key technical GEO factors include:

Structured data implementation.
Logical content hierarchy.
Clean, accessible HTML structure.

6. Competitive differentiation (10% weight)

Why should AI cite you instead of competitors? This GEO dimension identifies your unique value proposition in the AI ecosystem by analyzing:

Content gaps your site fills.
Unique perspectives or data.
Distinctive expertise not found elsewhere.

The hidden cost of ignoring GEO

Every day without proper GEO is a missed opportunity. While traditional SEO metrics might look healthy, your AI visibility could be near zero. This invisible problem manifests in:

Declining organic traffic despite stable rankings.
Competitors appear in AI responses while you don’t.
Lost thought leadership as AI systems cite other sources.
Reduced brand visibility in conversational search.

Geoptie’s free GEO audit illuminates these blind spots before they impact your bottom line.

How Geoptie’s free GEO audit works

Built on advanced AI analysis technology, Geoptie’s GEO audit process combines multiple assessment layers:

Step 1: Content extraction

The tool crawls your webpage, extracting both HTML structure and readable content. This dual analysis ensures nothing gets missed in your GEO assessment.

Step 2: AI-powered analysis

Using AI technology, Geoptie’s system evaluates your content across all six GEO dimensions simultaneously. This isn’t template matching – it’s genuine AI understanding.

Step 3: Weighted scoring

Each dimension receives a score from 0-100, then combines into an overall GEO readiness score using research-backed weightings.

Step 4: Actionable insights

Unlike generic SEO tools, each Geoptie score comes with specific, actionable recommendations tailored to your content and GEO needs.

Interpreting your Geoptie free GEO audit results

Understanding your GEO audit scores is crucial for prioritization:

85-100: AI-Optimized : Your content is primed for AI citation.
70-84: AI-Ready : Minor optimizations needed for maximum visibility.
55-69: Needs Optimization : Significant GEO improvements required.
Below 55: Not AI-Optimized : Major GEO overhaul necessary.

Each scoring tier comes with specific action items, making GEO implementation straightforward even for non-technical teams.

The competitive intelligence advantage

Geoptie’s free GEO audit doesn’t just analyze your content – it positions you relative to competitors in the GEO landscape. By understanding where you stand, you can:

Identify competitor weaknesses to exploit.
Find content gaps AI systems need filled.
Develop unique angles that ensure citation.
Build authority in underserved AI knowledge areas.

Advanced features that set Geoptie’s free GEO audit apart

Intelligent caching system

Run multiple GEO audits without redundancy. Geoptie’s system intelligently caches results while detecting content changes that require fresh GEO analysis.

User-friendly interface

Access Geoptie’s audit capabilities through an intuitive web interface designed for marketers and SEO professionals. The streamlined platform supports easy GEO monitoring and analysis workflows.

Real-time processing

No waiting in queues. Geoptie’s free GEO audit processes instantly, delivering GEO results when you need them.

Comprehensive coverage

Unlike basic tools that check surface metrics, Geoptie’s audit dives deep into content structure, semantic meaning, and AI interpretation patterns essential for effective GEO.

Common misconceptions about GEO audits

“My SEO is strong, so I don’t need GEO”

Traditional SEO and GEO optimize for entirely different systems. High Google organic search rankings don’t guarantee AI visibility.

“AI search isn’t mainstream yet”

With ChatGPT nearing 700 million weekly users, AI search is already mainstream. The question is whether your GEO is ready.

“Content quality is all that matters”

Quality is necessary, but insufficient. AI systems need specific structural and semantic signals that proper GEO addresses to understand and cite your content.

Taking action: Your Geoptie free GEO audit roadmap

Run your initial audit : Start with your most important pages at geoptie.com/free-geo-audit .
Prioritize low-hanging fruit : Focus on GEO dimensions where you score below 60 first.
Implement systematically : Make changes based on Geoptie’s GEO recommendations.
Re-audit and iterate : Track GEO improvements with follow-up audits.
Expand coverage : Audit your entire site systematically with Geoptie’s tools.

The future of search demands GEO today

The shift to AI-powered search isn’t coming – it’s here. Websites that thrive will master GEO early.

Geoptie’s free GEO audit provides the roadmap you need to navigate this transition successfully. It transforms the abstract concept of “generative engine optimization” into concrete, measurable actions.

Start your AI transformation with Geoptie now

Don’t wait for your traffic to decline before taking action. Understanding your current AI visibility through Geoptie’s comprehensive free GEO audit is the first step toward securing your position in the AI-powered future of search.

Visit geoptie.com/free-geo-audit today and discover exactly where your website stands in the GEO revolution. With detailed insights across six critical dimensions and actionable recommendations, you’ll have everything needed to optimize for the search engines that matter tomorrow.

The question isn’t whether to invest in GEO – it’s whether you’ll do it before your competitors. Your Geoptie free GEO audit awaits.

About Geoptie: Built by the SEO experts behind Transkriptor , Geoptie.com provides cutting-edge tools for the AI search era. Our free GEO audit tool helps thousands of websites transition from traditional SEO to comprehensive generative engine optimization.

Will GEO replace SEO – or become part of it?

Fri, 08 Aug 2025 13:00:00 GMT

SEO will never go extinct , but it is entering a new evolutionary phase.

As generative AI search engines reshape how people discover information, the definition of SEO needs to include optimizing for generative AI.

It’s time to rethink our framework of SEO as search everywhere optimization , with GEO as a core component of the strategy.

SEO with GEO: Same mission, expanding medium

SEO (search engine optimization) is optimizing for traditional search engines, like Google and Bing.
GEO (generative engine optimization) is optimizing for generative AI tools, like ChatGPT and Perplexity.

The engines may be changing, but the goal is the same – help answer users’ questions.

Users still want the best answer in the fewest steps.

Generative AI can shorten the path between the query and the action and make it a better experience for the user.

And as Will Scott said in his SMX session on tactical insights for mastering GEO, it all starts with a question.

Dig deeper: Will Scott on AI content reuse, schema and semantics, and generative engine optimization

A new engine appears

SEO was never just about Google, despite how much we might focus on Google as the default search engine, thanks to its current dominance in market share.

Search specialists have always had to learn how to optimize for each new relevant search tool, from Bing to DuckDuckGo.

The main differences between GEO and SEO are how factors are weighted in each algorithm and how and whether your content appears in each engine.

GEO is not a new shift

The move to all-in-one search and solution has been happening for years. The days of 10 blue links are long gone.

Generative AI is the natural evolution of search , from SERP features like featured snippets to tools and calculators embedded in search engines.

Now there is no such thing as traditional search vs. AI search.

It’s all different degrees of AI search.

All top search engines, including Google and Bing, are already integrating AI into how they build their algorithms and display results.

As LLMs have become more powerful, AI will continue to be more deeply embedded until there is no differentiation or separation between it and so-called traditional search.

Dig deeper: From search to answer engines: How to optimize for the next era of discovery

Overlap of GEO and SEO

The game is the same, but some of the skills are new.

With both SEO and GEO, you still need to follow the same best practices:

Understand users and what they want.
Answer user questions quickly and accurately.
Make your content easy to pull into search.
Create highly relevant, useful content.
Build pages with the right structure for users and crawlers.
Provide a fast, helpful online experience.
Increase your site authority to get more brand mentions .

What’s still relevant – and what’s not – in traditional SEO?

Some of the components that were a larger part of “traditional” SEO in the past have become less relevant.

For example, keyword-first strategies have become less relevant over time, as algorithms deranked sites for keyword stuffing.

But keyword research still matters because it tells you about the words people use when searching, and it helps you figure out what info people want to see on your site about a topic.

It can also help you quantify interest and search demand when prioritizing what topics to cover.

Strategies focused on user intent are still the most relevant in the current search meta landscape.

Social and forum searches influence GEO

Thinking beyond Google also means considering other places users are searching, including:

Social media like TikTok and Pinterest .
Forums like Reddit and Quora.

Users are already searching on social and forums, and now these platforms feed generative engine inputs too. Distributing content across platforms helps AI see and cite your brand.

The new search everywhere optimization needs to include all of these sources, too.

SEO specialists need to see how your site and brand are showing up on all platforms so you can strategize accordingly.

Actionable GEO strategies you can start today

Mastering GEO is no longer optional.

While generative AI has a small share of the overall search market, at the rate it’s growing, it may become 10% of website traffic by next year.

Start incorporating GEO best practices into SEO workflows now.

Content

Track key metrics and build reports for website/brand mentions and visibility in LLMs.
Build thorough pages with conversational responses that focus on topics first, not just keywords.
Implement anchor links with clear headings to make content easier to navigate and give structured answers.
Add reputable sources and references to validate data and improve authority.
Build a regular process for pruning underperforming pages and updating content frequently.
Add unique data and interactive tools to give users a reason to visit and return to your site.

Technical

Maintain crawlability and indexability with llms.txt and robots.txt .
Add RSS feeds for news and blog posts for crawlability , indexing , and recency signals.
Improve page speed and rendering for efficient delivery.
Use markup for FAQs, detailed definitions, tables of stats, and embedded insights to influence snippet citations and summaries in search.
Optimize multimedia metadata for LLMs, including image alt tags and video transcripts.
Audit crawling, indexing, and retrieval of content on your website.

How to start integrating GEO into your SEO program

Another reason GEO should be included as part of SEO: we need less separation, not more.

We need to get closely aligned with other digital marketing initiatives and resources.

If you haven’t already had conversations with your marketing team and leadership, start with a few basic concepts:

Establish generative AI as part of the SEO umbrella.
Learn new best practices in optimizing for LLMs and start training your teams.
Start tracking generative AI traffic sources and building reporting dashboards.
Get buy-in to start testing new ideas in generative AI.
Share wins and learnings for generative AI as part of SEO strategy.

Dig deeper: How to integrate GEO with SEO

Future predictions for SEO

What could be in store for the future of search everywhere optimization ?

New lingo

“Google” as a verb was named the word of the decade from 2000 to 2009.

For this decade, I predict we’ll see a rise in phrases competing to be as common as “Google it,” though “Ask ChatGPT” and “Perplex it” don’t quite roll off the tongue in the same way.

There’s an opportunity here for a catchy name to become the new defining verb of search.

(Maybe Ask Jeeves can make a comeback with a new AI?)

New tech

Check your site’s traffic sources by device in Google Analytics, and you might see a few sessions from smart TVs.

People are already asking questions of AI voice assistants built into their TVs, cars, and smartwatches – nearly any tech that can reach the web.

How would you optimize for a Subaru?

You’d include the right content on your website to make sure your local business is the top pick when a driver asks their car for directions to the nearest dog-friendly restaurants.

New formats

Agentic AI systems are the next phase of generative AI, with smart virtual assistants that can semi-autonomously handle tasks, processes, and user interaction.

SEO analysts will need to optimize for these systems too, both with your own site content and by partnering with development teams to build your own agents.

And before someone suggests we need a new acronym like AEO (agentic engine optimization), let’s all agree to include agentic AI as part of search everywhere optimization.

Dig deeper: Becoming AI-native: The next leap for SEO professionals

The definition of SEO needs to evolve as search behavior and tech evolve

Search engines have been building toward generative search engines for years.

And SEO pros already have the tools to master new generative AI systems.

Now we need to tune them for a different kind of engine.

The good news is we’re already great at learning new algorithms and models on a regular basis.

Generative AI could be the biggest opportunity in our industry right now, if you’re curious and brave enough to embrace it and master it.

Dig deeper: ‘Search everywhere’ doesn’t mean ‘be everywhere’

GPT-5 is here: OpenAI promises better writing, faster research

Thu, 07 Aug 2025 17:37:00 GMT

OpenAI today introduced GPT-5, its most advanced large language model. OpenAI promised faster answers, greater accuracy, and better performance in everything from content creation to coding.

Why we care. GPT-5 should bring more accuracy, customization, and speed to AI writing and research, which should be good news for search marketers and content creators. We’ll have to see whether the new model truly raises the bar for generative AI and SEO use cases and provides stronger writing, faster research, and more control over tone and structure.

Better writing. Here’s what OpenAI said about how it helps users write better:

Available today. Free and paid ChatGPT users can start using GPT-5 today. Enterprise and education customers get access next week. According to OpenAI:

GPT-5 cuts down on hallucinations, improves follow-up interactions, and handles complex tasks with more nuance.
Users can choose from four built-in personas — Cynic, Robot, Listener, and Nerd — in a new “research preview” feature.

GPT, PhD. GPT-5 is like having a “subject matter expert that is in your pocket that is an expert across every domain,” according to OpenAI.

CEO Sam Altman said GPT-5 is like talking to an expert, whereas GPT-3 was like talking to a high school student, while GPT-4 was like talking to a college student. (OpenAI said it is deprecating its older models.)
GPT-5 is more grounded and less sycophantic than earlier models, according to OpenAI. Fewer emojis. Less fluff. More thoughtful responses.

Who can get it. Free users can try GPT-5 with limited capacity. Once maxed out, they’ll be switched to a lightweight “GPT-5 mini” version.

Subscribers : Paying users can make GPT-5 their default model.
Pro users : Get unlimited GPT-5 access and a higher-performance version called GPT-Pro.

Zoom out. Altman said OpenAI’s new flagship model still doesn’t learn or improve on its own, which is a key component of what OpenAI considers true AGI (artificial general intelligence).

The announcement. Introducing GPT-5

How generative answers are changing the user journey

Thu, 07 Aug 2025 14:00:00 GMT

The traditional marketing funnel is breaking – and your content is losing its place in it.

For years, marketers built strategies around a linear journey: Awareness → Evaluation → Consideration → Purchase → Retention.

At every stage, content played a pivotal role – fueling discovery, educating buyers, and driving conversion. SEO was the engine that connected it all.

Now, generative answers are dismantling that system.

Recent Pew data shows a stark shift in user behavior:

Only 8% of searches with AI summaries lead to a click – compared to 15% without them.
Just 1% of visits result in a click on a summary link.

While Google claims billions of clicks still happen daily, brands and publishers are watching top-of-funnel traffic disappear in real time.

Informational content – once the backbone of awareness – is being replaced by zero-click answers.

Generative AI isn’t just changing search. It’s transforming how, when, and if your content enters the user journey at all.

What the user journey looks like with generative answers

Your top of the funnel, consisting of awareness and evaluation, is disrupted due to zero-click searches.

Informational searches are eroding, and 27.2% of U.S. searches ended without a click in March 2025 compared to 24.4% the year prior.

Users are clicking through to websites less often than in the past, disrupting the user journey because your informational content is no longer seen first, including your:

Blogs.
Guides.
Resources.

Middle-funnel disruptions are also taking place at the same time.

I’m not saying that all of the content at the top and middle of your funnel needs replacement.

Your content can still help drive decisions, but you need to do more than create a basic blog about increasing productivity to promote your app, for example.

Informational queries are solved with generative answers, and instead of funneling leads to your website, your brand needs to show up in AI Overviews and zero-click results.

This means the following content is less valuable:

How-to guides.
Infographics.
Explainer videos.

Dig deeper: Why your best content is invisible to AI search engines (and how to fix it in 30 minutes)

What the top of the funnel results look like

Imagine that your significant other snores, and you want it to stop.

You type, “how do I stop snoring,” into Google, and here’s what you see:

Sitting at the top of the results is a generative answer that breaks down:

Lifestyle changes that you can make to stop your significant other from snoring.
Devices that may be used to help with snoring.
Other considerations.

If you expand the answer to “Show all,” you’ll also see:

If someone does click on a result, there’s a good chance they’ll click on these articles and videos.

YouTube videos remain a good way to drive top-of-the-funnel traffic to your brand-owned profile because this same link has its own standout section in the search results:

You’ll also notice a Discussions and forums section, where your brand can stand out:

A lot of opportunities exist to be at the top of the funnel, even in a time of generative answers, but you must think outside of your blog to expand to:

YouTube.
Social media.
Reddit.
Quora.
Other forums/discussion platforms.

The top of the funnel is also reachable via other forms of digital marketing outside of SEO, including:

Online ads.
Direct email.
Social media.
Social media ads.

But for search, the top of the funnel is forever transformed and is shifting toward a narrower middle-of-the-funnel world.

The landscape of search may have changed, but there’s one reason why brands can’t ignore search traffic in favor of other marketing channels completely: brand awareness.

Brand awareness remains a pillar of making sales, and users are now finding your brand – or should be – through generative answers when searching for information.

Your focus is to switch to intent-driven traffic in the middle of the funnel.

Dig deeper: How to use social and forum data to inform next-level SEO strategies

Why the middle of the funnel is where your focus must shift

Your middle of the funnel (MOFU) is past the awareness stage of marketing and where users are considering making a purchase.

Intent-driven searches often lead users to the MOFU, and this is where you can provide:

Case studies that show your solution solves real people’s problems.
Comparison guides that help users understand their options.
Expertise that goes beyond the basic top of the funnel content.

Search engines are cutting out the top of the funnel, but if Google is being honest about search being up 10% , the traffic has to be going somewhere.

MOFU hits on the high-intent queries that:

Builds trust.
Shows off your expertise.
Deals with objections.

Decisions still require research (for now), and people tend to want to perform their own due diligence before buying products.

Traffic volumes may be falling, but businesses that focus on MOFU can still drive leads and revenue with content.

Content in this stage should focus on insights and examples that no one else can offer but you.

Product mentions, in the right place at the right time, will further help you build trust with answer engines and customers.

So, where do you start?

7 steps to take to revamp your user journey

1. Restructure your content

Streamline your content creation by adding guidelines for:

Formatting overhauls with clear subheadings.
Data points that are concise and unique to your brand.
Quotes from experts in your field.

Remain insightful while showcasing data from your own internal data and case studies.

Instead of adding to the conversation, you need to spark new ones with the content that you create.

Your top-of-the-funnel content that does show up in generative answers is always a bonus.

Searchers are looking for concise responses to their questions, and your content needs to have clear sections for each main point you answer.

2. Refresh past-performing content

You have a lot of content already that you can advance by following the restructuring plan above.

Go through your analytics to update past performing pages with:

New insights.
Examples.
Product mentions.

Dig deeper: Refreshing content: How to update old content to drive new traffic

3. Start thinking differently

You know that the MOFU is a potential traffic and lead generator. But how do you target it?

Transform your content creation from information queries to:

Commercial queries
- Your commercial queries remain more important than ever before.
- Users are searching for things such as “best snoring aids” or “X vs Y” content, which are all still performing well in search.
Transactional queries
- The bottom-of-the-funnel stages still provide superior results compared to the top of the funnel, and the zero-click rate is much smaller than that of both the top and middle funnels.
- Focus on transaction and navigational searches, such as:
  - Buy X snoring aid
  - Branded site name
- Add schema markup to make content easier to interpret, build quality backlinks, and adjust your funnel-driven content.

4. Establish topical authority

If you still need to address topics to add to your overall topical authority, you’re back to the top of the funnel.

You can fill in content gaps here with:

Clear definitions.
Lists and tables.
Clear headings and structure.
FAQ schema embeds.

Although traffic for this type of content may not be as high as it once was, you can still derive value from it.

5. Interlink your top-of-funnel, informative content

Just because your top of the funnel no longer guarantees traffic, you can still:

Label all of your informational content.
Decide whether to optimize it.
Interlink the content to create topic clusters .

When it’s no longer profitable to optimize this top-of-funnel content, refocus your efforts on your MOFU and BOFU.

Dig deeper: How to become an industry thought leader by building content pillars

6. Allocate your budget to what’s working today

If there’s one thing that everyone should take away from generative answers, it’s that what works today will change. Reallocate your budget to your:

Demo pages.
Product comparisons.
Case studies.
MOFU content.

You’ll drive less traffic than in the past, but conversion rates will rise.

7. Create a well-rounded marketing strategy

Focus on your owned properties (i.e., your websites), but also expand into:

Search.
Forums and discussion sites.
YouTube and TikTok.

Generative answers are surfacing answers from every site online, but there is a heavy favoritism toward sites like Reddit and Quora.

Don’t spam these sites, but when you can meaningfully increase conversion, do it.

User journeys shift as disruptive channels are introduced.

Businesses were once found in the Yellow Pages and through local ads only, but these are legacy options that are far less effective in the digital age.

From awareness to action: Redefining content’s role

Generative answers are reshaping the user journey, but many of the same principles that existed before AI Overviews, Perplexity, and ChatGPT still hold true.

Even Google’s Gary Illyes confirms that AI search optimization requires only normal SEO.

Shift your focus to the MOFU and BOFU and away from just the top of the funnel content that Google now provides through AI Overviews.

You need a complete content ecosystem that touches on all stages of the user journey, with less focus on the answers Google provides.

Dig deeper: Want to beat AI Overviews? Produce unmistakably human content

Becoming AI-native: The next leap for SEO professionals

Thu, 07 Aug 2025 12:00:00 GMT

In the post- ChatGPT era, SEO is not just about keywords, links, or even content.

It is more about how deeply AI is embedded into your strategy, operations, and thinking.

To succeed in 2025, you don’t need to be a prompt engineer or machine learning expert. But you do need to:

Start thinking in systems, not just tasks.
Create content that is ready for AI-driven platforms, not just Google SERPs.
Structure your content so it is easy for both humans and machines to understand.
Track how your content performs in rankings as well as across AI tools, summaries, and chat responses.

The difference between being AI-enabled and AI-native is not how many tools you use.
It is how fundamentally you let AI redefine your SEO strategy.

Two SEOs, two mindsets

We are now seeing two distinct profiles emerge:

The AI-enabled SEO professional

This is where most SEO professionals currently find themselves. AI-enabled SEOs are those who use LLMs like ChatGPT or Claude to streamline everyday tasks like:

Generate meta descriptions.
Drafting quick content briefs.
Speed up keyword clustering.
Translate or localize content at scale.
Extract insights from massive CSV files without the manual grind.

These professionals have embraced AI as a powerful productivity enhancer, and that is no small win. The AI-enabled mindset is pragmatic, adaptable, and curious.

However, it still largely relies on traditional SEO playbooks. AI is used to optimize the workflow, not redesign it.

Think of it like adding cruise control to a manual car.

Yes, it is faster and more efficient, but you are still in the driver’s seat, making all the decisions.

The core mechanics haven’t really changed.

The AI-native SEO professional

Now enter the AI-native pro.

This person doesn’t just use AI. They build with it.

They create prompt stacks and reusable templates across keyword research, topical mapping, and content scoring.
They use GPT agents or LangChain flows to automatically generate thousands of long-tail landing pages at scale, with QA and internal linking included.
Their dashboards are not static. They are dynamic and powered by large language models trained to provide insights based on business KPIs.
Their strategies are designed to test, learn, and improve with AI in the loop automatically.

AI-native SEOs think in systems, not tasks.

They focus less on “How can I speed this task up with AI?” and more on “How can I build an AI-first pipeline that replaces the need to do this task manually ever again?”

The leap from enabled to native is more than technical – it’s philosophical.

Dig deeper: How AI is reshaping SEO: Challenges, opportunities, and brand strategies for 2025

Understanding the shift: AI-enabled vs. AI-native SEO

Let’s start with clear definitions:

What this means for your career in 2025

Search in 2025 is not what it was even a year ago.

Here is my prediction: Within the next two years, AI-native thinking will be essential for senior SEO roles.

This is not because AI-enabled professionals aren’t valuable – they definitely are.

The competitive edge provided by AI-native strategies is becoming too crucial to overlook.

If your strategy still relies on weekly checklists and legacy CMS constraints, you are already falling behind.

If you are building a team, you should aim for a mix of both types of experts.

AI-enabled team members offer stability, proven processes, and quick productivity gains, while AI-native team members contribute to innovation, breakthrough thinking, and future readiness.

As an individual professional in the field, think about where you want to position yourself.

Are you someone who is focused on optimizing content within existing frameworks, or do you aspire to be the person who builds the new frameworks?

Being AI-native means you are equipped for a world where scale, iteration, and insight are not hindered by human speed.

Dig deeper: 15 AI tools you should use for SEO

A quick self-test: Where do you land?

Let’s run a quick diagnostic. Answer honestly:

If you said “yes” to 4 or more, congratulations! You are operating like an AI-native SEO.

If you are more in the 1–3 range, you are likely AI-enabled. (There’s nothing wrong with that! It’s the stepping stone.)

But the bar is rising.

How top brands are optimizing for AI and LLMs

Nike began analyzing which terms triggered AI Overviews and trained LLMs internally to:

Understand what gets pulled into Google’s summaries.
Reformat pages with bullet-point takeaways, stats, and FAQs designed for AI readability.
Repurpose AI-generated summaries into product descriptions and influencer campaigns.

Result: A 500% increase in AI Overview traffic in the U.S. from September 2024 to May 2025, based on Semrush AI Overview data.

How to evolve your SEO game

This is not about keeping secrets. It is about expanding your reach – growing.

Here are a few tangible ways to shift toward AI-native SEO.

Build prompt systems, not just one-time requests

Rather than simply asking ChatGPT for “10 blog ideas,” develop a prompt structure or a framework that incorporates insights from your:

Keyword research.
Target audience personas.
Sales funnel stages.

Automate with intent

Don’t just limit AI for copywriting.

Use platforms like Make, Zapier, and Python with GPT to automate internal linking, generate schema, or even conduct bulk A/B tests on title tags.

Think modular SEO

Break down SEO functions into reusable AI modules:

Topic modeling.
FAQ generation.
Entity enrichment.
On-page optimization.

This mindset simplifies scaling across verticals, languages, and products.

Get familiar with APIs and agents

You don’t have to be a developer, but understanding how to connect GPT calls or use tools like LangChain or OpenPipe can give you a major edge.

Dig deeper: How to keep your SEO skills sharp in an AI-first world

The rise of the AI SEO pod

Traditional SEO teams are often siloed: a strategist hands off to a content writer, who hands off to a dev, who loops in an analyst after launch.

That approach can’t keep up with the speed and scale required in an AI-native environment.

AI SEO pods are small, cross-functional teams – typically 2–3 people – that operate as one agile unit.

They use AI not just to move faster, but to rethink the entire process: from planning and production to testing and optimization.

Each pod is made up of complementary roles:

An AI SEO strategist who sets direction and defines success across both SERPs and LLMs.
An AI content strategist who builds modular content systems instead of isolated assets.
A data automation specialist who connects tools, ensures quality, and keeps the system learning.

Together, they deliver the work of a 10-person team – at higher speed, lower cost, and with more adaptability.

AI SEO strategist: The visionary thinker

This individual in the team is not just managing keywords or rankings.

They are focused on building an edge with every algorithm shift.

They are the ones asking, “How will AI change the search experience six months from now and what do we need to do about it now?”

They lead with vision, establish priorities, and help the team stay ahead of the curve. While others pursue updates, they prepare for them.

What they do:

Design SEO strategies built for where search is going, not just where it has been.
Define success metrics that include AI platforms, not just SERPs.
Recommend tools and workflows that offer long-term value, not just quick wins.
Collaborate directly with leadership to ensure AI SEO aligns with broader business goals.
Focus less on “achieving ranking higher” and more on “building systems that learn and scale.”

AI content strategist: The systems architect

This is not your usual content strategist or planner. They:

Are less about creating individual content pieces and more about building intelligent content systems.
Grasp brand identity, voice, audience, and search intent.
Leverage that knowledge to guide tools to enhance and scale content planning and creation.
Ensure that every piece of content improves the system as a whole.

What they do:

Develop templates and guidelines that train AI to reflect brand voice.
Create topic maps that identify content gaps before competitors do.
Design workflows that incorporate the feedback loops from both users and search performance.
Develop systems to determine and guide where content is published, how it is optimized, and how it evolves.
Bridge the strategy-execution gap between marketers and technologists.

Data automation specialist: The behind-the-scenes operator

Without this person, all other processes operate at a slower pace or may even fail.

They are in charge of the infrastructure that ensures the AI-driven SEO machine functions smoothly.

This role is key for problem-solving, connectivity, and quality assurance across all processes.

Their work ensures the right data flows between the right tools at the right time. They also ensure that automated results do not compromise quality for the sake of speed.

What they do:

Set up and manage automation tools like Make or Zapier to remove manual tasks.
Build systems to verify AI-generated content for accuracy, consistency, and search relevance.
Connect different tools and platforms to facilitate a seamless workflow.
Design dashboards that monitor and track not just rankings and clicks but also outcomes generated by AI.
Identifying issues early by building safeguards into every aspect of the process.

Together, they accomplish the work of a traditional 10-person team – faster, cheaper, and with continuous learning.

Forward-thinking companies are now building AI SEO pods.

Small teams of 2–3 people who handle the work of 10, powered by AI-native systems.

One such pod could:

Generate 1,000 new geo-pages utilizing AI and location data.
Implement programmatic internal linking based on semantic clustering.
Identify content gaps with an AI QA agent that compares against competitors on a daily basis.

This is what AI-native looks like in practice.

A 5-step roadmap to becoming AI-native

Here is how SEO professionals and teams can make the shift in a structured, scalable way:

1. Audit your AI usage

Where are you using AI? (e.g., meta generation, topic ideas)
Where are the gaps? (e.g., brief creation, content QA, internal linking)

Use this audit as a starting point to identify opportunities.

2. Build a prompt library with 6Ws + H framework

Prompt engineering is a vital skill now. Use the 6Ws + H to structure prompts:

Store and templatize these prompts in Google Sheets or Notion.

Reuse and modify as needed.

3. Automate repetitive SEO workflows

Start with small tasks, then scale quickly. Automate:

Keyword clustering (using embeddings + ChatGPT).
Content brief generation (via Zapier, GPT, or Jasper integrations).
Schema markup creation (AI-powered templates).
Broken link identification and redirect mapping (via Screaming Frog + GPT analysis).

4. Integrate AI into QA and governance

AI can validate as well as generate.

Use it to:

Detect outdated information in older content.
Flag missing H1s, headers, broken schema.
Recommend accessibility improvements.
Optimize for featured snippets, People Also Ask, and AI Overviews.

5. Optimize for LLM search and AI platforms

This is the latest SEO challenge:

Use clear, straightforward, fact-based content.
Incorporate structured data (FAQ, HowTo, Product).
Publish content that showcases expert credentials and clear authorship.
Monitor and track appearance in AI Overview sections using tools like Semrush, Authoritas, AlsoAsked, or custom scripts.

Dig deeper: Why you need humans, not just AI, to run great SEO campaigns

The future belongs to the AI-native

The SEO of the past was about keywords, content, and links.

The SEO of today is about context, coherence, and collaboration with AI.

To be competitive in this landscape:

You need more than just tools.
You need a new mindset, new systems, and a new strategic compass.

So ask yourself again: Are you still AI-enabled? Or are you ready to go AI-native?

Mastering Social Media: Essential Management Tips for Explosive Brand Growth

Wed, 06 Aug 2025 01:00:27 GMT

In today's digital age, social media is a cornerstone of successful brand strategy. For businesses eager to grow their presence and engage effectively with their audience, mastering social media management is crucial. At Hometown Beat , we understand that the right approach can significantly enhance brand visibility and foster deeper connections with your audience.

The Power of Consistent Content

Consistent content is key to maintaining a robust online presence. It not only keeps your audience engaged but also positions your brand as a reliable source of information. Here are some strategies to help you maintain consistency:

Content Calendar: Creating a content calendar can help you plan and organize your posts in advance. Tools like Hootsuite and Buffer offer excellent features for scheduling and managing content.
Brand Voice: Develop a consistent brand voice that aligns with your values and resonates with your audience. This helps in building a recognizable brand identity.

Engagement is Everything

Engagement is a two-way street. It's not only about broadcasting your message but also about listening to your audience. Engaging with your followers can lead to increased loyalty and trust. Here's how you can enhance engagement:

Respond Promptly: Quick responses to comments and messages show that you value your audience's input.
Customer Feedback: Use social media polls and surveys to gather feedback and insights about your products or services.
Host Live Sessions: Engage with your audience in real-time through live Q&A sessions, product demonstrations, or webinars.

Leveraging Social Media Tools

There are numerous tools designed to streamline social media management and enhance your strategy. At Hometown Beat , we recommend these innovative tools to amplify your brand growth:

Canva for creating visually appealing graphics that capture attention.
Sprout Social for comprehensive social media analytics and reporting.
Trello for organizing your social media campaigns and collaborating with your team.

Maximizing Brand Visibility

To maximize your brand's visibility on social media, it's essential to utilize the advertising features these platforms offer. By targeting specific demographics, you can increase your reach and engagement significantly. Consider these strategies:

Targeted Ads: Utilize Facebook and Instagram ads to reach potential customers who fit your target audience profile.
Influencer Collaborations: Partner with influencers in your industry to expand your reach and credibility.
Regular Analytics Review: Continually assess your social media metrics to understand what's working and what needs improvement.

Conclusion

Incorporating these social media management tips into your strategy can significantly enhance your brand's growth. At Hometown Beat , we're dedicated to helping small and medium-sized businesses in Carrollton, GA, and beyond achieve their marketing goals. Contact us today for a free marketing audit and discover how we can tailor strategies for your business's success story.

AI search fight: Cloudflare and Perplexity clash over crawling

Tue, 05 Aug 2025 14:58:00 GMT

Cloudflare accused AI answer engine Perplexity of “stealth crawling,” saying it uses deceptive techniques to bypass website blocks and access content it’s been explicitly told not to touch.

In response, Perplexity said Cloudflare has a fundamental misunderstanding of how AI assistants work and accused the company of either publicity-seeking or technical incompetence.

The big picture. Cloudflare said Perplexity uses declared bots when it can, but switches to “stealth crawling” when blocked. That includes mimicking normal browser behavior, rotating IPs, and ignoring robots.txt rules (tactics that can be associated with scrapers and bad actors).

Cloudflare tested this by setting up honeytrap sites and found Perplexity answering questions using content it shouldn’t have been able to access.
Perplexity insisted its requests are made on behalf of users, not as preemptive crawling. The company says these are real-time fetches, akin to what a browser or email client does, and claims Cloudflare mistook its behavior for something it wasn’t.

Why we care. If AI assistants can sidestep robots.txt by posing as browsers, brands, creators, and publishers lose control over how and when their content is used. That breaks the old deal between search engines and websites .

What’s next. Cloudflare said it’s already blocking the behavior in question and expects Perplexity’s tactics to change in response. It’s calling for standardization of bot behavior through IETF (the Internet Engineering Task Force) and other policy efforts.

Perplexity, meanwhile, is doubling down on its identity as an agentic AI platform and says it shouldn’t be governed by rules designed for traditional web crawlers.

The blog posts. You can view the full back and forth here:

Cloudflare: Perplexity is using stealth, undeclared crawlers to evade website no-crawl directives
Perplexity: Agents or Bots? Making Sense of AI on the Open Web

From search to answer engines: How to optimize for the next era of discovery

Tue, 05 Aug 2025 14:00:00 GMT

The shift from traditional search engines to AI-powered answer engines signals more than a technical upgrade.

It marks a fundamental change in how people discover, evaluate, and act on information.

Search is no longer a discrete game of isolated queries and static rankings.

It’s becoming an infinite game – one shaped by context, memory, and ongoing interaction.

For many users, large language models (LLMs) now offer a more effective starting point than classic search engines, especially when the task calls for clarity, research, or a more conversational experience.

How search evolved: From static queries to continuous conversations

Traditional search: A one-off query model

Traditional search engines (like classic Google Search) operate on a deterministic ranking model.

Content is parsed, analyzed, and displayed in SERPs largely as provided.

Ranking depends on known factors:

Content quality.
Site architecture.
Links.
User signals.

A user types a query, receives a list of results (“10 blue links”), clicks, and typically ends the interaction.

Each query is treated independently, with no memory between sessions.

This model supports advertising revenue by creating monetization opportunities for every new query.

AI-powered search: Built for continuity and context

AI-powered answer engines use a probabilistic ranking model.

They synthesize and display information by incorporating:

Reasoning steps.
Memory of prior interactions.
Dynamic data.

The same query can yield different results at different times.

These systems are built for ongoing, multiturn conversations, anticipating follow-up questions and refining answers in real time.

They operate continuously, even while you sleep, and focus on delivering direct, synthesized answers rather than just pointing to links.

How output and experience differ between search and answer engines

The differences between traditional search and AI-powered answer engines aren’t just technical. They show up in what users see and how they interact.

From output format to underlying signals, the user experience has fundamentally changed.

From link lists to zero-click answers

Traditional search engines: Return a ranked list of links generated by complex algorithms.
Answer engines: Deliver full answers, summaries, direct responses, or even product recommendations by blending large-scale training data with real-time web results. They reduce the need for users to click through multiple sites, leading to more zero-click experiences.

From keywords to context

Traditional search: Relies on keyword matching, backlinks, and on-page optimization.
AI search/generative engines: Rely on semantic clarity, contextual understanding, and relationships between entities enhanced by attention mechanisms and references in credible sources. Even content that doesn’t rank highly in traditional search may appear prominently in AI summaries if it is well-structured, topical, and cited across trusted platforms.

Key characteristics of answer engines

Conversational search

LLMs like ChatGPT , Google Gemini , and Perplexity enable conversational interactions, often serving as a more intuitive starting point for users seeking clarity, context, or nuanced understanding.

Queries tend to be longer and phrased as full questions or instructions.

Personalization and memory

Unlike traditional search, AI-powered search incorporates user context, such as:

Past queries.
Preferences.
Location.
Even data from connected ecosystems (e.g., Gmail within Google’s AI Mode).

This context allows the engine to deliver tailored, dynamic, and unique answers.

Dig deeper: How to boost your marketing revenue with personalization, connectivity and data

Query fan-out

Instead of processing a single query, answer engines deconstruct a user’s question into dozens or even hundreds of related, implicit, comparative, and personalized sub-queries.

These synthetic queries explore a broader content pool.

From one query, systems like AI Mode or AI Overviews:

Generate a constellation of search intents.
Retrieve responsive documents.
Build a custom corpus of relevant content.

Reasoning chains

AI models move beyond keyword matching, performing multi-step logical reasoning. They:

Interpret intent.
Formulate intermediate steps.
Synthesize coherent answers from multiple sources.

Multimodality

Answer engines can process information in various formats, including text, images, videos, audio, and structured data. They can:

Transcribe videos.
Extract claims from podcasts.
Interpret diagrams.
Integrate these inputs into synthesized outputs.

Dig deeper: Visual content and SEO: How to use images and videos in 2025

Chunk-level retrieval

Instead of retrieving or ranking entire pages, AI engines work at the passage level.

They extract and rank smaller, highly relevant chunks of content to build precise, context-rich answers.

Advanced processing features

User embeddings and personalization

Systems like Google’s AI Mode use vector-based profiles that represent each user’s history, preferences, and behavior.
This influences how queries are interpreted and how content is selected, synthesized and surfaced as a result – different users may receive different answers to the same query.

Deep reasoning

LLMs evaluate relationships between concepts, apply context, and weigh alternatives to generate responses.
Content is judged on how well it supports inference and problem-solving, not just keyword presence.

Pairwise ranking prompting

Candidate passages are compared directly against each other by the model to determine which is most relevant, precise, and complete.
This approach departs from traditional scoring models by favoring the best small sections rather than entire documents

A step-by-step guide to answer-engine-optimized content

Content best practices remain the same – it should be people-centric, helpful, entity-rich with healthy topical coverage based on audience intent.

However, the content creation process needs to incorporate answer-engine optimization best practices in the details.

Here’s our recommended seven-step process for content creation.

1. Content audit

When auditing existing content:

Check current visibility signals, including impressions, rich results, and whether the page is cited in AI platforms like Google AI Overviews, ChatGPT, or Perplexity.
Identify signs of content decay to establish a baseline for measuring improvement.
Spot and document issues such as:
- Topical gaps or missing subtopics.
- Unanswered user questions.
- Thin or shallow content sections.
- Outdated facts, broken references, or weak formatting.
- Grammatical errors, duplicate content, or poor page structure.

2. Content strategy

It is not all about creating new content.

Your content strategy should incorporate aligning existing content to the needs of answer engines.

Retain : High-converting content with high visibility and high traffic.
Enhance : Pages with high impressions but low click-through rate, pages with low visibility, impressions, and rich results.
Create: Content around topical gaps found in the audit.

3. Content refresh

Update existing content to close topical gaps to make information easily retrievable

4. Content chunking

This involves breaking long blocks into:

Scannable sections (H2/H3).
Bullet lists.
Tables,
A short TL;DR/FAQs.

Keep each chunk self-contained so LLMs can quote it without losing context, and cover just one idea per chunk.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

5. Content enrichment

Fill in topical gaps by:

Expanding on related topics.
Adding fresh data.
Drawing on first-hand examples.
Referencing expert quotes.

Cover topics AI can’t easily synthesize on its own.

Cite and link to primary sources within the text (where relevant and meaningful) to boost credibility.

6. Layer on machine-readable signals

Insert or update schema markup (FAQPage, HowTo, Product, Article, etc.).

Use clear alt text and file names to describe images.

7. Publish → monitor → iterate

After publishing, track organic visibility, AI citation frequency, and user engagement and conversion.

Schedule content check-ins every 6–12 months (or after major core/AI updates) to keep facts, links, and schema current.

Make your content LLM-ready: A practical checklist

Below is a checklist you could incorporate in your process to ensure your content aligns with what LLMs and answer engines are looking for.

Map topics to query fan-out

Build topic clusters with pillar and cluster pages.
Cover related questions, intents, and sub-queries.
Ensure each section answers a specific question.

Optimize for assage-level retrieval

Use clear H2/H3 headings phrased as questions.
Break content into short paragraphs and bullet points.
Include tables, lists, and visuals with context.

Build depth and breadth

Cover topics comprehensively (definitions, FAQs, comparisons, use cases).
Anticipate follow-up questions and adjacent intents.

Personalize for diverse audiences

Write for multiple personas (beginner to expert).
Localize with region-specific details and schema.
Include multimodal elements (images w/ alt text, video transcripts, data tables).

Strengthen semantic and entity signals

Add schema markup (FAQPage, HowTo, Product).
Build external mentions and links from reputable sources.
Use clear relationships between concepts.

Show E-E-A-T and originality

Include author bios, credentials, and expertise.
Add proprietary data, case studies, and unique insights.

Ensure technical accessibility

Clean HTML, fast load times, AI-friendly crawling (robots.txt).
Maintain sitemap hygiene and internal linking.

Align with AI KPIs

Track citations, brand mentions, and AIV (attributed influence value).
Monitor engagement signals (scroll depth, time on page).
Refresh content regularly for accuracy and relevance.

How SEO is evolving into GEO

As the mechanics of search evolve, so must our strategies.

GEO (generative engine optimization) builds on SEO’s foundations but adapts them for an environment where visibility depends on citations, context, and reasoning – not just rankings.

Many “new” AI search optimization tactics, such as focusing on conversational long-tail searches, multimodal content, digital PR, and clear content optimization, are essentially updated versions of long-standing SEO practices.

New metrics and goals

Traditional SEO metrics like rankings and traffic are becoming less relevant.

The focus shifts to being cited or mentioned in AI-generated answers, which becomes a key visibility event and a brand lift moment, rather than just driving traffic.

New KPIs at the top of the funnel include:

Search visibility.
Rich results.
Impressions.
LLM visibility.

With declining traffic, engagement, and conversion metrics become critical at the bottom of the funnel.

Relevance engineering

This emerging discipline involves:

Strategically engineering content at the passage level for semantic similarity.
Anticipating synthetic queries.
Optimizing for “embedding alignment” and “informational utility” to ensure the AI’s reasoning systems select your content.

Your website acts as a data hub.

This also means centralizing all types of data for consistency and vectorizing data for easy consumption, and distributing it across all channels is a critical step.

Importance of structured data

Implementing schema markup and structured data is crucial for GEO.

It helps AI engines understand content context, entities, and relationships, making it more likely for content to be accurately extracted and cited in AI responses (53% more likely).

Dig deeper: How to deploy advanced schema at scale

Brand authority and trust

AI models prioritize information from credible, authoritative, and trustworthy sources.

Building a strong brand presence across diverse platforms and earning reputable mentions (digital PR) is vital for AI search visibility, as LLMs may draw from forums, social media, and Q&A sites.

Connecting the dots: UX and omnichannel in the age of AI search

The typical user journey is no longer linear. The options for discovery have diversified with AI acting as a disruptor.

Most platforms are answering questions, are multimodal, delivering agentic and personalized experiences.

Your audience expects similar experiences on the sites they visit. As the user journey evolves, our approach to marketing needs to change, too.

In a linear journey, having channel-based strategies worked.

Consistency of messaging, content, visuals and experiences at every touchpoint are today key to success.

That means you need an audience strategy before mapping channels to the strategy.

Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement

To make it happen effectively, you need to orchestrate the entire content experience – and that starts with your platform as the foundation.

Your website today needs to act as the data hub feeding multimodal information across channels.

How to make your content discoverable by LLMs

To show up in LLM-driven search experiences, your content needs more than depth. It needs structure, speed, and clarity.

Here’s how to make your site visible and machine-readable.

Foundational SEO

The fundamentals of SEO still apply.

LLMs have to crawl and index your content, so technical SEO elements like crawlability and indexability matter.

LLMs do not have the crawl budgets or computing power that Google and Bing have.

That makes speed and page experience critical to maximize crawling and indexing by LLMs

Digital assets

With search going multimodal, your digital assets – images and videos – matter more than they ever did.

Optimize your digital assets for visual search and make sure your page structure and elements include FAQs, comparisons, definitions, and use cases.

Structural integrity

Your site and content need to be both human and machine-readable.

Having high-quality, unique content that addresses the audience’s needs is no longer enough.

You need to mark it up with an advanced nested schema to make it machine-readable.

Deep topical coverage

Ensure your content aligns with the best practices of Google’s E-E-A-T .

People-first content that:

Is unique.
Demonstrates expertise.
Is authoritative.
Covers the topics that your audience cares about.

Make your content easy to find – and easy to use

While the building blocks of SEO are still relevant, aligning with LLM search calls for refining the finer points of your marketing strategy to put your audience before the channels.

Start with the basics and ensure your platform is set up to let you centralize, optimize and distribute content.

Adopt IndexNow to push your content to LLMs instead of waiting for them – with their limited computing and crawling capabilities – to crawl and find your content.

Thank you, Tushar Prabhu, for helping me pull this together.

Crawling for AI search: Balancing access, control, and visibility

Mon, 04 Aug 2025 14:15:00 GMT

For 20 years, most SEO professionals only cared about Googlebot.

However, in the last few years, a host of new crawlers have emerged from different indexing platforms, such as ChatGPT , Perplexity , and others .

These crawlers serve a broader range of purposes.

They’re not just the first step toward indexing.

They can also ingest content for model training or perform retrieval-augmented generation (RAG) on a specific URL on demand.

Which raises the question: Should you allow all these bots to crawl your site?

What if your audience doesn’t use DeepSeek or You.com? What’s the upside of the cost of crawling and the loss of control over how your content is presented?

There is no single “correct” answer, but there is a clear framework for approaching it.

Let’s them eat chunks

Allowing most AI crawlers to access the majority of your content delivers a net benefit.

However, any truly unique intellectual property should be protected behind a paywall or login to preserve its value.

This means for most content, you will be actively optimizing for AI crawling – enriching and “chunking” content to earn visibility.

Fully understanding that the vast majority of websites will experience traffic drops in the coming years.

But if you’ve filtered for AI-related traffic in GA4, you’ve probably already noticed that the traffic that remains is often significantly higher quality, as AI surfaces are strong pre-qualifiers of user intent.

Beyond traffic, AI surfaces also play a growing role in building brand salience.

Prominent citations, especially the Top 3 in AI Mode or paragraph-linked mentions in ChatGPT, influence perception.

Optimizing for AI surfaces is, for many business models, the new path to visibility.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

AI surfaces become the category page

AI surfaces increasingly act as the “first exposure” points in the user journey, making it essential that your brand shows up early.

They also increasingly function as category pages:

Aggregating offers.
Comparing competitors.
Linking to the “best” ones.

Currently, in rare cases, although I expect this to significantly increase over time, users are converted on the brand’s behalf. But critically, they still rely on the brand for fulfillment.

This isn’t new. It’s how Amazon and other marketplaces have worked for years.

And just like with those platforms, with AI, it’s not about owning every touchpoint. It’s about earning brand salience.

Providing a great fulfillment experience and high-quality product or service.

So the next time the user comes in-market, they come to you directly, bypassing AI search altogether.

That is how you win market share.

What if you’re an aggregator?

What about websites that aggregate content from smaller providers – like real estate portals, job boards, or service marketplaces?

Should they be concerned that AI systems might bypass them entirely?

I don’t think so.

Realistically, even with modern content management systems, small to medium enterprises often struggle to maintain a basic website, let alone navigate the complexities of distributing content to AI platforms.

I don’t see a world where thousands of small websites across the myriad of industries are all efficiently aggregated by AI platforms.

That’s where trustworthy aggregators still play an essential role.

They filter, vet, and standardize. AI systems need that.

Aggregators that provide more than just listings – for example, verified review data – will be even more resistant to AI disintermediation.

Still, AI systems will continue to favor established big brands with enhanced visibility.

Media is the (partial) exception

The real existential risk is to pageview-monetized media .

Traffic to commodity content is collapsing as answers are served on AI surfaces.

For publishers, or anyone who produces article content, the answer isn’t to block AI entirely. It’s to evolve.

Adopt smarter editorial strategies.
Diversify revenue streams.
Focus on winning prominent citations.
Own share of voice – don’t just chase traffic.

Because if you block AI crawling entirely, you’re forfeiting visibility to a competitor.

The only exception? If you have non-replicable content, such as:

Highly specialized research.
Unique expert advice.
Valuable user-generated content, such as reviews at scale.

In such cases, it doesn’t have to be all or nothing – consider partial crawling.

Give bots a taste to earn citations, but don’t let them feast.

This lets your brand stay competitive while preserving your unique advantage.

If we agree that the goal is not just to allow AI crawling but to actively encourage it, the next question becomes: how do you optimize for it from an SEO perspective?

How to optimize for chunking

Being optimized for Googlebot is not enough.

You now need to cater to a multitude of crawlers, not all of which have the same level of capabilities.

What’s more, indexing is no longer on a URL level.

Content is broken down into important components, which are stored in a vector database.

Think of each section of your content as a standalone snippet. And win AI citations by:

One self-contained idea per paragraph.
Paragraphs of 1-4 sentences.
Clear subheadings, marked up as H2 or H3.
Use proper entity names.
High Flesch reading ease score, prioritizing clarity over cleverness.
Structured, accessible, semantic HTML.
Think multi-modal, ensuring crawlability of images and videos.
No JavaScript dependency, as not all crawlers can process it.
Use factually accurate, up-to-date information.

If AI crawlers can’t access and understand it, it won’t cite it.

Dig deeper: Inside the AI-powered retrieval stack – and how to win in it

You don’t need to spoon-feed with LLMs.txt

Despite the buzz, llms.txt is not an official standard, it’s not widely adopted, and no major AI indexer respects it.

This means the file likely won’t be checked by default, and many sites may see little crawl activity as a result.

Could that change? Maybe.

But until it’s adopted, don’t waste time implementing a file that bots aren’t checking.

Other technical SEO improvements, such as graph-based structured data and improving crawl speed, are far more likely to positively impact visibility on AI surfaces.

Focus on what matters for AI visibility now, not a hypothetical future that is unlikely to ever occur.

How to speed up crawling

I’ve covered:

How to measure and improve crawl efficacy .
How to optimize crawled pages for rapid indexing .

Many of these tactics for traditional search hold true for AI bots as well:

Fast, healthy server response for all bots trending below 600 milliseconds at a maximum, ideally closer to 300.
For efficient crawling, ensure a clean and clear URL structure rather than relying on rel=canonical and other such hints. Where this is not possible, block no-SEO-value routes with robots.txt .
Gracefully handle pagination .
Real-time XML sitemaps submitted in Google Search Console (for Gemini), Bing Webmaster Tools (for ChatGPT and Copilot).
Where possible, use Indexing APIs to submit fresh content.

These fundamentals become more important in an AI world, where we see Google is proactively cleaning its index .

Rejecting large swaths of previously indexed URLs will, I suspect, improve the quality of “RAGable” content.

That said, measurement of crawling needs to move beyond easily accessible data like the crawl stats report in Google Search Console.

And focus more on log files , which incorporate clearer reporting on all different types of AI crawlers.

CDNs, like Cloudflare, and AI visibility trackers are offering reporting, making it more accessible than ever.

Crawling offers value beyond website indexing

While Googlebot, Bingbot, and AI platforms receive the most attention, SEO tool crawlers also heavily visit many websites.

Before AI systems became prominent, I blocked most of them via .htaccess. They offered little value in return while exposing competitive insights.

Now, my view has changed. I allow them because they contribute to brand visibility in AI-generated content.

It’s one thing for me to claim my website is the most popular – it hits differently when ChatGPT or Gemini says it, backed by Semrush traffic data.

AI systems favor consensus. The more aligned signals they detect, the more likely your messaging is repeated.

Allowing SEO crawlers to verify your market position, being featured on comparison sites, and being listed in directories all help reinforce your narrative – assuming you’re delivering real value.

In the AI era, it’s not about link building but rather citation management. Curating a crop of crawlable content off-site that corroborates your branding by external citations.

This adds weight. It builds trust.

Crawling is no longer only about website indexing. It’s about digital brand management.

So let the bots crawl. Feed them structured, useful, high-quality chunks.

AI search visibility isn’t just about traffic. It’s trust, positioning, and brand salience.

Dig deeper: Chunks, passages and micro-answer engine optimization wins in Google AI Mode

Why AI visibility starts with ops – not marketing

Thu, 31 Jul 2025 13:00:00 GMT

SEO is no longer enough.

Discoverability in AI-powered search now depends on operations – a shift many marketers haven’t accounted for.

AI platforms like ChatGPT , Gemini , Claude, and Google’s AI Overviews aren’t fooled by clever messaging.

They synthesize operational signals – from order issues to pricing gaps – to form brand perceptions .

These aren’t marketing problems. They’re organizational blind spots that block AI visibility.

I see them constantly in my audits – and most can’t be fixed with content alone. They require operational change.

This is a strategic wake-up call and a blueprint for CMOs and COOs who need to align.

Here’s why the first visibility hurdle in AI is no longer marketing-owned.

Why organizational signals shape AI visibility

Every facet of your organization – operations, product design, fulfillment, and customer service – sends signals that influence AI systems.

These aren’t just internal data points. They surface in online chatter that shapes how LLMs assess your brand’s relevance to customer queries.

Search engines rely on content match.
LLMs evaluate the entire customer journey, from shopping experience to product longevity, lifetime cost of ownership, and after-sales support.

That means even outdated technology or past operational glitches can lead an LLM to omit your brand or misrepresent it.

The chart below shows how negative signals from operations are picked up and learned by LLMs.

Sometimes, product design is the visibility blocker.

One of my clients – a global industry leader with a well-made, widely used product generating millions in sales – was flagged in an AI visibility audit.

An LLM described the product’s technology as “outdated” and concluded “the market has moved on.”

No company wants a customer to see that narrative, yet it is visible to everyone, including competitors.

LLMs act like a buyer’s advisor

Unlike search engines, LLMs aren’t just crawling content. They’re synthesizing signals across the operational lifecycle, including:

Product design and innovation.
Quality of materials and ingredients.
Cost of ownership ROI.
Shipping accuracy.
Ease of returns.
Product durability.
Pricing.
Use cases.
Buyer personas.
Support experience.

If operations sends even one negative signal the LLM deems important, your brand may be omitted from discovery or negatively portrayed in AI responses.

Below are a few examples from my audits:

Dig deeper: 7 ways to grow brand mentions, a key metric for AI Overviews visibility

These aren’t marketing gaps. They’re operational breakdowns.

CMOs can’t resolve them without COO involvement. Fixing them will take months, and in some cases, a year or more.

AI visibility roadblocks are buried in:

Fulfillment logs.
UX error rates.
Returns.
Even outdated technical specs or product design.

LLMs don’t just see what you say. They learn from what the world says about your performance.

That makes the COO a critical gatekeeper for brand visibility in AI.

The CMO needs operations metrics on their dashboard

Operational issues are early-warning signals for changes in AI visibility.

These metrics don’t directly drive visibility – but if left unaddressed, they often foreshadow visibility loss.

That’s why I recommend marketing teams track operational bellwether metrics – indicators of broader downstream impact.

In finance, FedEx shipping volume predicts consumer spending.

In AI visibility, metrics like shipping delays, support hold times, and other operational issues can forecast what LLMs will soon learn and reflect.

LLMs may not access your internal data, but issues may surface in complaints and commentary that shape AI perception.

CMOs need bellwether metrics to recognize when to pivot marketing tactics and avoid downstream visibility losses.

I had a mentor who called these crystal ball metrics, because they were his greatest indicator of what would happen in the future for his business.

The COO needs to monitor LLM perceptions over time

The COO needs visibility into how LLMs interpret real-world operations – not just internal performance metrics.

These systems pull from:

Public forums.
Reviews.
Industry publications.
Third-party comparisons.

Even flawless execution isn’t enough if LLMs detect innovation lag, outdated positioning, or recurring support issues.

That’s why COOs must monitor how AI platforms interpret their operations – and either course-correct or enable marketing to respond before those perceptions solidify.

What AI perception monitoring looks like in operations

Operations teams don’t need to become AI experts – but they do need to track how AI platforms reflect your brand.

This work can live in marketing, ops, or both. Here’s what that looks like in practice.

1. Track forum and online chatter

Track what’s being said about your brand in forums, reviews, Reddit threads, and social posts.

These external signals now influence AI visibility.

In the AI era, this can’t be left to marketing alone – COOs need to act when patterns emerge.

I predict AI visibility will pressure companies to operate at best-in-class levels, driving continuous improvement like never before.

In-house process analysts and change management consultants will become critical.

They will be tasked with responding quickly as patterns emerge in online chatter before LLMs solidify inaccurate or negative perceptions.

Dig deeper: Reddit: Your new online reputation challenge

2. Monitor AI platform responses

Regularly review what LLMs (ChatGPT, Bing Copilot, etc.) say about your company.

Watch for red flags like outdated descriptions, inaccuracies, or mentions of defects or support issues.

This requires training or a clear framework.

While tools can assist, much of the early work will be manual – reviewing AI responses directly to spot concerns.

Sentiment analysis can flag tone, but even positive narratives may be factually inaccurate.

3. Measure accuracy and consistency

Track how often AI responses get facts, brand statements, product specs, use cases, and messaging right versus wrong.

Inaccuracies often reflect how your information is surfaced.

The correct data may exist. But if it’s locked in sales-only PDFs, buried behind lead-gen forms, or embedded in interactive web components (like JavaScript tabs), LLMs may miss it entirely.

Visibility isn’t just about accuracy – it’s about accessibility.

4. Link ops events to AI narratives

Create a dictionary of key operational signals, then monitor them across internal data, public forums, reviews, and LLM outputs.

For example, track when a shipping delay first appears in ops metrics, then in online chatter, and finally in AI responses.

This connects specific faults to shifts in AI perception.

Over time, you’ll start to see how long it takes for LLMs to absorb brand signals and adjust their narratives.

With a consistent methodology, you’ll build an evidence-backed timeline for how long you have to address issues before they impact AI visibility.

My hunch is that larger companies in high-profile sectors will experience faster perception shifts because LLMs process their signals more frequently than those from niche players.

Dig deeper: Your brand in the age of generative search: How to show up and be cited

The strategic opportunity

AI visibility is a cross-functional challenge that demands shared ownership.

When operations and marketing align:

Issues get resolved faster.
Visibility improves.
AI tools reflect stronger brand narratives.

The organizations winning in the AI era are those that have cleared the brand signals hurdle.

Once operational signals are strong, marketing can amplify impact – if they adapt to how AI now drives discovery.

How AI overviews are reshaping Google search by Edna Chavira

Wed, 30 Jul 2025 14:45:00 GMT

Google’s AI Overviews are rapidly changing how users find information—and how SEOs need to think about visibility. According to new research from Semrush , these AI-generated summaries now appear in over 13% of desktop search queries in the U.S., up from just 6% in January 2025.

Drawing on 10M+ keywords and 200K+ clickstream queries, the report explores where AI Overviews are gaining traction, how they’re influencing zero-click behavior, and which industries and keyword types are most affected. Key takeaways include:

Informational queries dominate AI Overviews—putting top-of-funnel content at risk
Zero-click rates are high for AI Overviews, but aren’t consistently rising
High-trust categories like health, science, and law are seeing the fastest growth
Navigational and commercial queries are beginning to trigger Overviews too

The report also outlines which high-value keywords remain untouched—giving SEOs a roadmap for opportunity amid disruption.

Download the full report to explore the data and adapt your SEO strategy for the AI-driven SERP.

User-first E-E-A-T: What actually drives SEO and GEO

Wed, 30 Jul 2025 14:00:00 GMT

There’s a lot of confusion around what E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) actually is – and what it isn’t.

While often cited in SEO conversations, E-E-A-T is not a measurable score or a direct ranking factor.

Instead, it is best understood as a framework for building user trust through content quality, credibility, and relevance.

E-E-A-T is about the user, not checklists or scores

E-E-A-T is an SEO concept – not a measurable system used by search engines, generative engines, or AI models.

There are no E-E-A-T scores, checklists, or formal guidelines from Google or other engines because it’s not part of the core algorithm.

Instead, E-E-A-T represents a content framework that, when applied well, helps build user trust.

When users trust your content, they’re more likely to engage with it or share it.

Journalists and creators may link to it, which builds buzz that sends positive signals to search engines and LLMs.

In other words, it’s not the E-E-A-T concept itself that boosts visibility.

It’s the trust and authority your content builds with real users that leads to better performance in SEO and GEO.

If someone is telling you that you need to be reviewed by a medical professional or have a photo of yourself for E-E-A-T, they don’t understand the concept.

Anyone can edit themselves into a photo, and anyone can steal an image from someone else.

The “Medically reviewed by” tactic has been overplayed.

While it might build user trust, it may not help SEO and could even backfire if the reviewer lacks true expertise.

E-E-A-T should be about the user – not satisfying a made-up metric. It’s not something that can be measured with a score.

Here are a few ways we apply E-E-A-T with our clients, affiliates, and content partners to help them grow:

Citations.
Jump links.
“Reviewed by” and author bios.
Images.
Going further with content.

Citations

Linking out to another website may or may not carry direct SEO value, but it absolutely impacts the user’s experience.

When you make a claim, don’t link to a definition. That belongs in your article – or on your site if it’s part of your entity.

Instead, link to a study or credible source that directly supports the claim.

Here’s a made-up example:

“Vitamin C can help bring aliens to earth, save the environment, and give us world peace.”

Most sites would link the term Vitamin C to their own guide or to a third-party medical website that defines what Vitamin C is. This isn’t helpful.

An internal link to your own guide might benefit your site structure and highlight that page’s importance, but it doesn’t help the reader – they’re curious about the claim, not the definition.

The same goes for linking to a medical website that simply defines Vitamin C. It doesn’t support the claim, it just redirects the reader to basic information they weren’t looking for.

Instead, link to a peer-reviewed study that shows how Vitamin C attracts aliens to Earth (as outlandish as that sounds), because that backs the actual claim.

Cite sources that specifically state Vitamin C saves the environment and promotes world peace, and ensure they’re credible and clearly support what you’re asserting.

A site’s age, backlink count, or traffic doesn’t make it trustworthy. What matters is the user’s experience based on your recommendation.

Here’s what we look for when selecting citations or giving backlinks in our content:

The author is a real expert and practices in the field.
The site is fully focused on the topic, or has a clearly defined, dedicated section for it.
Older content is updated regularly as new findings emerge.
There are no links to spammy topics like porn, payday loans, pills, or other pay-to-play verticals.
Articles cannot be purchased (major media is the exception), and guest posts are only accepted under strict quality controls from certified experts. We spot this by checking menus, media kits, and contributor pages.
When you click away from the linked article, you land on other topically relevant content – not generic shopping lists or paid placements.
The page isn’t overloaded with ads, pop-ups, or interstitials.
The content is easy to read, understand, and absorb – no jargon overload or unnecessary complexity.

Dig deeper: How to get cited by AI: SEO insights from 8,000 AI citations

Jump links and qualifiers

Both long-form and short-form content can be authoritative, rank well, and build trust.

The goal is to give users the answer they’re looking for as quickly as possible and in the most effective format.

That might mean using:

Images.
Videos.
Bullet points.
Paragraphs.
Audio clips.
Any mix of media types.

It can help to include qualifiers at the top of the content, but don’t overdo it – especially with long-form pieces.

Qualifiers may include:

A short explanation of why you’re qualified to cover the topic.
First-hand experience if you’re not a certified expert.
Clarifications about what’s required to use a product, follow a process, or qualify for something.

These elements help build trust early and show the reader you’ve anticipated their concerns and done the homework for them.

That said, they take up space – so keep the user experience in mind and use them strategically.

When your content is long or has clearly separated sections, include jump links or a table of contents to help readers quickly navigate to the most relevant part.

For example, someone searching for disease symptoms may not care about treatment options or survival rates.

Just as someone browsing for comic book-themed gifs under $10 doesn’t need to scroll through higher-priced items first.

We also prioritize user-friendly headers.

Instead of using keyword-stuffed or robotic phrasing, we label sections using the terms our audience naturally uses.

This makes it easier for readers to skim and quickly find the information they need.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

‘Reviewed by’ and author bios

Stating that content is reviewed by a licensed or recognized expert can build user trust, but it can just as easily erode that trust if misused.

If the expert isn’t actually credible, or if the review feels performative, it may hurt trust and even work against your site algorithmically.

Authorship was once a hot topic in SEO, and this is where both reviewed by labels and author bios come back into play.

Include “reviewed by” tags and author bios where they’re essential, such as for medical or other YMYL (“your money or your life”) topics. You can also use them elsewhere when they add genuine value.

Authors can build loyal readerships and subscriber bases. And when contributors feel recognized and supported, they’re more likely to keep producing high-quality work.

Just make sure that both the author and the reviewer are actual experts.

Here’s what we require from clients when it comes to authorship and review credibility:

Authors do not write about topics they’re not certified in or don’t have firsthand experience with.
Either the author or reviewer – ideally both – must be known, published experts. That means:
- They’re published in academic or industry journals.
- They’ve been featured in the media as subject matter experts.
- They speak at relevant conferences or events.
- They operate a well-researched, topic-specific website.
- They’re licensed or certified in their field, and those credentials are current.
- They’re actively working or practicing in the space.

Author bios should focus on professional qualifications – not unrelated personal details.

If someone writes about dermatology, we don’t need to know they enjoy playing board games or love their dog (unless they’re writing about games or pets).

At most, include a brief hobby mention if they insist – but the priority should always be on showcasing their expertise.

Dig deeper: The myth of manufacturing author E-E-A-T

Images

Simply including an image in your article doesn’t prove you visited a location, baked a dessert, or completed a task – these can all be fabricated.

Focus on what helps the user, not what’s done just for SEO.

Include an image of yourself with the completed task near the top of the article or as the featured image, but for the rest of the content, focus on what benefits the user.

Here are a few examples.

Travel sites

Show the exact place to stand for a selfie or sunrise shot, using a specific marker in the ground so the person knows how to angle the camera and which direction to face.
Start with what the finished photo looks like, then share the best times of day because of natural light.
Finally, include an image of the area they should stand in, along with the camera height and angle, so they can recreate your shot.

Recipe and cooking sites

Include images of what steps look like when things are overdone or underdone, especially at tricky points.
Add consistent elements to your photos – like countertops or a place card – so it’s clear this is actually you cooking in your kitchen.

Operations sites (code, Excel, accounting, etc.)

Identify common typos by reviewing forums with formulas and calculations.
Provide an FAQ at the bottom of the page addressing each one and how to fix it.
Show what the formula, code, or equation should look like at each step – both correct and incorrect – and include what the output looks like for each.
Then explain why a specific character, space, or detail causes the incorrect result, so the user not only gets the fix but also understands the why.
An added bonus: this can attract long-tail searches from users who keep getting incorrect results.

Going further with content

Every sentence that makes a claim or expresses an opinion needs a “how” or a “why.”

This is one of our biggest wins for both SEO and AI Overviews.

When we see content that says something is good, we add the reason why it’s good – ideally in the same sentence.

Thin content:

“The top part of the castle at golden hour is perfect for a selfie” is missing the “why.”

Helpful content:

“The top part of the castle at golden hour is perfect for a selfie because you get naturally diffused light, and you can either capture a striking cloud-lit sky or angle down for a cityscape backdrop – making it a great shot year-round.”

Not every sentence needs this, but anytime you claim something is good, correct, bad, the best, better, or worse, support it with reasoning.

That’s what shows the user you know what you’re talking about – and gives them a reason to trust and act on your advice.

Another example:

“Take your selfie at golden hour in the morning 10 minutes after sunrise starts.”

A more helpful version:

“Take your selfie at golden hour in the morning 10 minutes after sunrise starts and face east for a backlighting effect – which makes it easier to lift shadows in editing – or west if you want direct light on your face and a brightly lit background, even if it requires more post-processing.”

Dig deeper: Want to beat AI Overviews? Produce unmistakably human content

It all comes back to the user

E-E-A-T is about giving the user everything they need in a trustworthy, actionable format – and cutting the fluff.

When done well, this type of content can naturally earn backlinks, citations, and mentions.

It can also appear in listicles from authoritative sources and be shared on social media.

All of these send signals that search engines, LLMs, and AI Overviews can use.

E-E-A-T is about the user experience – and when you get that right, better SEO and GEO often follow.

AI isn’t the enemy: How bloggers can thrive in a generative search world

Wed, 30 Jul 2025 13:00:00 GMT

If you’re a blogger feeling SEO fatigue in 2025, you aren’t alone.

Between the March and June core updates and noticeable SERP changes, many bloggers have found themselves pushed further down the search results or out of them altogether.

Most are still trying to make sense of what changed, only to be met with even more disruptive news:

AI Overviews are expanding .
Gemini is everywhere.
Traditional blue links keep getting replaced by summary boxes, People Also Ask modules, and rich result carousels.

It’s easy to feel like Google has moved on and left creators behind.

But here’s the new reality: the way people search, find, and consume content has changed.

What worked in 2021 doesn’t hold up in 2025. And no, AI isn’t going away. If anything, it’s accelerating.

AI Overviews are now appearing across more niches and query types. Gemini is being deeply integrated into Chrome and Android .

Content is being summarized and reused in ways that shape brand visibility and perceived authority, but may never generate a click for bloggers.

That might sound discouraging, but it shouldn’t be.

Bloggers who will succeed aren’t the ones who block AI bots or wait for traffic to magically come back. They’re the ones who:

Understand how AI sees content.
Start building in ways that let their content and brand show up where it matters.

This article isn’t about fighting AI. It’s about aligning with it.

Bloggers should no longer optimize for rankings. They need to optimize to be included, summarized, and reused in AI systems .

How can you do that? In this article, bloggers will learn the following;

We’ll break down the tools, tactics, and mindset shifts you need to make your site AI-ready.
Learn why playing offense now is the key to surviving and thriving in this new search era.

Why blocking AI isn’t a strategy (even if Cloudflare makes it easy)

It’s tempting. Traffic is down, and AI feels like the enemy.

Cloudflare offers a shiny new button that promises control: “Block AI bots.”

With one click, you can stop OpenAI, Gemini, Anthropic, and others from crawling your site.

But here’s the consequence of that action for bloggers:

When you block AI bots, you block your brand from appearing in tools your audience is already using, such as Gemini, ChatGPT, and Perplexity.
You’re entirely removing yourself from the AI results ecosystem.
In the new search experience, the bloggers who don’t block will be the ones quoted, summarized, and cited.

Cloudflare’s update, launched this month, is part of a growing trend to protect creator content from being reused without attribution.

But here’s the hard truth. Blocking AI doesn’t punish Google or LLMs. It punishes you.

Google has made it clear: AI Overviews aren’t going away.

They’re expanding into more verticals:

Recipes.
Travel.
Product reviews.
Tutorials.
And beyond.

And if your content can’t be crawled or reused by these systems, you won’t be part of the answer.

Raptive and other ad networks have already toyed with auto-blocking LLMs for their publishers via robots.txt.

But this approach removes bloggers from the systems shaping the future of discovery.

It’s a risky long-term bet, and that sidelines your content while competitors are picked up instead.

Let’s be honest: visibility is changing. Clicks are harder to earn.

But your goal as a blogger isn’t just traffic, it’s influence and brand presence.

And if AI can’t see you, neither can the people using it to find solutions, inspiration, or recommendations.

So if you’re using Cloudflare?

Don’t toggle that “block AI” switch.
Instead, optimize for inclusion, because this is the new shelf on which your content needs to sit.

Dig deeper: Cloudflare’s Pay Per Crawl: A turning point for SEO and GEO

Core updates are telling you what Google wants – are you listening?

The signs have been clear all year.

The March 2025 core update hit hard.

The June core update followed, reinforcing the same message: surface-level content and SEO checklists aren’t enough anymore.

If your site dropped during these updates, it likely wasn’t because of a single issue. It was because Google has shifted the goalposts:

Thin intros padded with fluff? Cut them.
Over-explained sections that don’t say anything new? Rewrite them.
Content built to rank not serve? Reconsider it.
FAQs that are generalized or lack context? Expand them.

These updates are designed to reward originality, clarity, and firsthand expertise.

That’s not speculation. Google confirmed this .

When your content is modular, trustworthy, and experience-driven, it’s not just better for readers. It’s also better for AI systems.

Why?

Because AI tools like Gemini and ChatGPT are pulling from sources that are scannable, structured, and credible.

When your posts are easy to break apart and summarize into independent chunks , you’re more likely to show up in snippets, boxes, and even full AI responses.

How do we do this?

Use H2s and tables of contents so your content is navigable.
Front-load value. Don’t bury answers under paragraphs of preamble.
Add bios, source context, and site-level trust signals.
Write like a person with a purpose, not like an article generator.
Emphasize unique insights. What makes YOUR content citable?

Ask yourself: Is my content so algorithmically attractive to Google they would be embarrassed not to rank it?

That’s the new standard, and it’s how Google is evaluating which sites deserve AI visibility in Gemini.

So if you’re still trying to win traffic with outdated post templates, overlong intros, and keyword stuffing, it’s time to rebuild.

Core updates tell you what Google wants, and the bloggers who listen will still be visible in 2026 and beyond.

How to make your content AI-friendly

Do you want to be visible in AI Overviews, Gemini, Perplexity, and even ChatGPT with browsing?

Your content has to be easy to understand, reuse, and summarize by both users and machines.

This isn’t about tricking AI. It’s about formatting your content so clearly that it becomes the obvious choice to cite or summarize.

Here’s how to do that.

Make your structure modular

Use H2s, H3s, and bullet points, just like this.
Add a clickable table of contents to the top of your posts.
Break long paragraphs into smaller, scannable chunks.
Include short intros and immediate value up top.

Google’s quality rater guidelines identify “clear page structure” and “easy-to-navigate sections” as key user signals.

Train AI to cite you

Answer common questions directly beneath subheadings.
Use phrasing that reflects how people ask questions.
Add context about yourself (author bios) and your site’s focus.
Use “About this site” or “Author expertise” schema where possible.

AI looks for complete and credible answers. Help it recognize your answers as those worth quoting.

Did you know you can add “AI share buttons” to your content to enhance your citations? Here’s a great tutorial covering the process .

Scroll down to the summary section of this travel guide to Taormina, Sicily . Look at the buttons pointing to ChatGPT, Gemini, Perplexity, and Grok.

By using these buttons, the author is:

Providing an easy way for users to summarize her content.
Encouraging them to remember her brand as an authoritative source to be cited on future LLM travel-related queries.

Focus on feeding the bots whenever you can. This is an incredible tip, and I expect it to become more and more popular.

Eliminate filler content

Ask yourself:

“Did I write this paragraph for the reader or for Google?”
“Does this section provide value, or just take up space?”

If the answer is filler, cut it. This is especially true for intros, keyword-heavy fluff, or storytelling that doesn’t move the reader forward.

AI tools can now skip the parts that don’t add value – so should you.

Add unique insights

If you’re publishing:

Another “Things to Do in Oaxaca” list.
Or another “Easy Lamb Chops Recipe”…

Then, you must include something only you can provide:

A personal tip, a lesson learned, a regional twist, a tested method.
Photos, local details, or experiences that aren’t scraped from ChatGPT.
Why your version is different, and why it matters.

If your post doesn’t add anything new to the internet, it probably won’t stand out to AI either.

Bottom line: The more your content feels helpful, well-structured, and uniquely you, the more likely it is to be pulled, quoted, or featured in the new AI-powered search experiences.

AI as a brand multiplier, not a traffic killer

It’s easy to get stuck on the idea that AI Overviews “steal” traffic .

And yes, in most cases, they absolutely reduce clicks, especially for simple queries or list-style content.

But here’s the shift bloggers need to make:

AI isn’t just a threat to traffic. It’s an opportunity to multiply your brand reach, if you’re visible inside the AI systems your audience is using.

Your brand can appear in more places than ever

Even if a user never clicks your link, they may still:

See your blog name in an AI Overview.
Read your travel tip in Perplexity.
Get your ingredient substitution from ChatGPT while browsing.
Watch your embedded video suggested inside a carousel.

That exposure may not always translate into a click, but it builds recognition. And recognition builds trust.

Influence, not just impressions

In 2025, your blog isn’t just a website . It’s a brand touchpoint.

The bloggers who will thrive are those who:

Use AI to amplify their authority, not fear it.
Share snippets of their expertise across platforms ( YouTube , Pinterest , Reddit , email).
Create off-site funnels that convert, such as courses, products, and subscriber communities.

Your blog post might not earn the click. But your brand might earn:

The follow.
The sign-up.
Or the sale.

Don’t hide from AI – teach it who you are

LLMs are trained on billions of documents, but they can’t read your mind. You have to teach them what your blog stands for.

How do you do this?

Be consistent in how you describe yourself.
Include branded terms in titles, headers, and content (especially your name).
Add structured data like author schema, organization markup, and product, recipe, and FAQ schema where applicable.

When AI systems understand your niche, tone, and credibility, they are more likely to pull from you again.

There has been a recent trend to remove the site name from page titles. This feels like questionable advice because the site name is a strong signal to LLMs as to where the content exists.

Just because Google doesn’t show the Site Name in search results doesn’t mean bloggers should be removing the site name from their page titles.

On the contrary, when our goal is to seed LLMs with brand mentions, this could hurt you, not help you.

Visibility in AI isn’t optional anymore

Your content is going to be used somewhere. The question is this: will it include your name, your brand, and your links or someone else’s?

Embracing AI means making sure your brand shows up in the places your readers are already turning to for help.

Brand mentions are the new “links.” The most successful creators must seed LLMs with these mentions going forward.

Dig deeper: Want to beat AI Overviews? Produce unmistakably human content

The blogger’s mindset shift: From resistance to resilience

Blogging in 2025 isn’t what most of us expected.

The platforms have changed. Google’s priorities have shifted. AI is summarizing content without sending traffic.

And tools like Cloudflare are making it easier than ever to throw your hands up and say, “Nope, I’m out.”

But that’s not a strategy. That’s surrender.

The bloggers who survive aren’t the ones who resist

They’re the ones who:

Rethink what visibility means.
Build for people-first, AI-friendly experiences.
Optimize for reuse, not just rankings.
Double down on brand, not just blog posts.

AI isn’t replacing bloggers. It’s revealing which ones were always worth quoting.

Treat your blog like a brand, not just a website

Ask yourself:

What do I want to be known for in Gemini, ChatGPT, or Perplexity?
What does my “brand voice” sound like in AI summaries?
Where do I send people after they find me in an AI box?

The bloggers who thrive next aren’t building “just in case I rank.”

They’re building on purpose, with authority, uniqueness, and portability across channels.

Focus on what you can control

You can’t force a click. You can’t always win the blue link. But you can:

Make your content scannable, clear, and worthy of inclusion.
Build topical authority through internal linking and consistent coverage.
Own your niche’s most important answers, before AI tries to.
Repurpose your posts into reels, pins, shorts, emails, or summaries that expand your reach.

This is the resilience mindset.

Stop fighting for circa-2021 Google and start showing up where users are actually spending time… in AI.

To prepare for the future, you must let go of the old playbook

The search landscape has changed. And it’s not going back.

Between core updates, AI Overviews, and shifting user behavior, the strategies that once worked (stuffing in keywords, padding content with fluff, chasing rankings through formulas) aren’t cutting it anymore.

But that doesn’t mean you’re out of options.

It means the rules have changed.

The bloggers who will grow are those who stop optimizing solely for Google and start building for inclusion, visibility, and lasting brand equity.

You don’t need to block AI to protect your content. You need to teach AI how to represent you well.

That means:

Creating structured, scannable, value-packed content.
Showing your expertise through real experience, not just word count.
Building a recognizable brand that surfaces across tools, platforms, and summaries.

This isn’t the end of blogging. But it is the end of blogging like it’s 2021.

So lean in.

Train the AI.
Build the brand.
Meet your audience where they’re actually searching.

The future belongs to creators who don’t just write for clicks, they write to be remembered.

Dig deeper: An AI-assisted content process that outperforms human-only copy

Unlocking Success: The Top Benefits of Professional Website Design and Management

Tue, 29 Jul 2025 03:07:47 GMT

In today's digital age, your website serves as the storefront of your business, often creating the first impression for potential clients. A well-designed and professionally managed website is not just a luxury; it's a necessity for any business striving to succeed online. At Hometown Beat , a leading digital marketing agency in Carrollton, GA, we believe that a strategically crafted website is a cornerstone of effective brand development and modernization. Let's delve into the multitude of benefits that professional website design and management bring to the table.

Elevated Brand Image

Your website is a reflection of your brand's identity. A professional design ensures that every element on your site—from color schemes to typography—aligns with your brand ethos. This results in a cohesive and aesthetically pleasing online presence that communicates trust and credibility. According to Forbes , a consistent brand image can increase revenue by up to 23%.

Enhanced User Engagement

User experience, or UX, is paramount in keeping visitors engaged on your website. A professionally designed site offers intuitive navigation, fast load times, and mobile responsiveness, ensuring a seamless experience across all devices. Consider the following components that contribute to effective UX:

Intuitive Navigation: Simple and easy-to-understand menus that guide users to the information they seek.
Responsive Design: A layout that adapts to varying screen sizes, providing an optimal viewing experience on mobile, tablet, and desktop devices.
Fast Loading Speed: Reduces bounce rates and keeps users engaged.

Boosted Search Engine Optimization (SEO)

SEO is crucial for increasing your website's visibility in search engine results. Professional website management ensures that your site is optimized for search algorithms, driving organic traffic and improving search rankings. Key SEO enhancements include:

Keyword Optimization: Strategic use of relevant keywords in content to improve visibility.
Meta Tags: Effective use of meta descriptions and title tags to enhance search visibility.
Quality Content: Regularly updated, high-quality content that engages users and encourages longer site visits.

Business Growth and ROI

A professionally designed and managed website serves as an effective tool for business growth. It not only attracts potential clients but also converts them into loyal customers. By leveraging advanced analytics, businesses can gain insights into user behavior, allowing them to tailor their strategies for enhanced performance. Furthermore, HubSpot reports that businesses with well-planned websites see a 55% increase in leads.

Strategic Conversion Optimization

Conversion rate optimization is a crucial part of website management that focuses on improving the percentage of visitors who complete desired actions. This may include filling out a contact form, making a purchase, or signing up for a newsletter. A/B testing and user feedback analysis are invaluable tools in this process, ensuring continuous improvement and adaptability to market demands.

Conclusion

In summary, investing in professional website design and management is an investment in your business's future. At Hometown Beat , we specialize in creating websites that not only look good but also perform exceptionally well. We understand the unique market rhythms of each business we serve, offering tailored solutions that foster growth and success. Contact us today for a free marketing audit and start your journey toward unlocking the full potential of your online presence.

SEO vs. GEO: What’s different? What’s the same?

Mon, 28 Jul 2025 14:00:00 GMT

Search is evolving – fast. AI tools now deliver direct answers, while traditional search engines still list links.

To stay visible, marketers need both search engine optimization ( SEO ) and generative engine optimization ( GEO ).

This article explains each approach, highlights their differences, and shows how they work together to maximize your reach.

What is SEO?

SEO enhances a website’s ranking on search engine results pages by optimizing content to align with search engine algorithms.

It hinges on using relevant keywords naturally, earning backlinks from trusted sites, and ensuring technical health, such as fast load times and mobile friendliness.

Historically, success has been measured by by metrics like click-through rates, keyword rankings, and time spent on page, all of which indicate how well your site satisfies user intent.

What is GEO?

GEO focuses on preparing content for AI-driven platforms that synthesise answers rather than list links.

It aims to make the material easy for AI to parse and cite by using structured headings, clear statements of fact, and authoritative references.

GEO success is measured by how frequently your content is cited in AI-generated responses, reflecting your visibility in conversational search.

SEO targets traditional search engines by optimizing for ranking signals, including keywords, backlinks, and site performance.

GEO, by contrast, targets AI platforms, optimizing for content structure, factual clarity, and citation potential.

SEO success is evident in higher traffic and engagement, and GEO success manifests as AI citations and inclusion in generative answers, where users read your content directly within a conversational interface.

While they target different platforms, SEO and GEO share foundational principles that guide high-quality content creation.

Dig deeper: How to implement generative engine optimization (GEO) strategies

What SEO and GEO have in common

SEO and GEO share a common goal of delivering content that truly meets user needs.

Both approaches start with understanding the questions your audience is asking and crafting answers that satisfy those queries.

Shared goal: Satisfy user intent

By focusing on user intent, you ensure that whether someone lands on your page from a search result or encounters your content in an AI-generated response, they find the information they came for.

Content quality is non-negotiable

Both search algorithms and AI models favor accurate, well-researched, and up-to-date material.

Investing time in fact-checking, citing reputable sources, and maintaining clarity boosts your rankings on traditional search engines and increases the chances that AI systems will cite your work.

Content quality builds credibility with human readers and with the AI algorithms that parse your pages.

Structure supports visibility

Clear headings, subheadings, and logical flow help search crawlers evaluate relevance and context while guiding AI engines to extract key facts accurately.

Well-organized content is easier for people to scan and for machines to parse.

Both SEO and GEO best practices emphasise concise paragraphs, occasional bullet points for lists, and summary boxes where appropriate.

Dig deeper: What is content readability and how to make your content easier to read

Ongoing optimization matters

Regular monitoring and refinement underpin both approaches. Search algorithms evolve and AI models continually retrain on fresh data.

Reviewing performance metrics – like click-through rates, search rankings, or AI citation frequency – helps identify when to:

Update content.
Add new insights.
Restructure sections.

This ongoing cycle of analysis and refinement keeps your content competitive across both traditional search engines and AI-driven platforms.

User experience is the ultimate test

User experience underpins all of this work.

Whether someone clicks a link to your site or reads an AI-generated summary that includes your content, the ultimate measure of success is how effectively you deliver value.

By prioritizing clear communication, accuracy, and ease of use you satisfy audiences and signal trustworthiness to both search engines and generative platforms.

Rather than choosing one over the other, the most effective strategy blends both approaches.

Integrating SEO and GEO

To combine SEO and GEO, start by reviewing your best-performing content and enhancing it with:

Clear subheadings.
Concise data points.
Expert quotes.

This satisfies AI extraction needs without harming your SEO foundation.

Broaden your research to include full questions and conversational phrases that both people and AI might use.

Finally, apply structured data markup to provide context that benefits all discovery methods.

Dig deeper: How to integrate GEO with SEO

Measuring success together

Track SEO performance through your analytics, monitoring rankings, traffic, and engagement.

For GEO, track the frequency of AI mentions and the share of voice in generated responses through your measurement dashboards.

By comparing trends such as changes in click-through rates alongside shifts in AI citation volume, you can see how adjustments affect both traditional and AI-driven visibility.

Practical takeaways

Unlike traditional search results, GEO outcomes are generated dynamically and can vary based on user context, prompting, and platform behavior.

Aim to create the right ecosystem and variables to increase your chances of being surfaced more frequently and accurately.

Start with clear headings

Use extractable headings that guide both readers and AI through your main points.

Ensure each section has a descriptive title that accurately reflects its content.

This simple step helps AI models pinpoint relevant facts and makes it easier for human visitors to scan your page.

Incorporate compelling data points

Include at least one data point or statistic in each key section to boost credibility and give AI answers concrete figures to cite.

Whether it is a percentage, a case study result, or a short survey finding, these numbers demonstrate authority and enrich your narrative.

Weave these figures into your text so they feel natural rather than tacked on.

Use authoritative references

Add references such as quotes from industry experts or links to reputable studies to reinforce trust.

Concise paragraphs that introduce the insight, give context, and explain why it matters will serve both readers and AI well.

Apply GEO-focused tweaks

Convert dense paragraphs into numbered steps or short sentences that focus on distinct facts.

Add a brief “Key takeaways” box at the end of each significant section to reinforce your main message and create a natural extraction point.

Conduct regular content audits

Every quarter, review your top-performing pages to:

Refresh figures.
Update expert quotes.
Refine headings.

As search algorithms evolve and AI models learn from fresh data, these audits ensure your content remains accurate, relevant, and structured for maximum visibility.

SEO vs. GEO

SEO and GEO serve the same purpose of making your content more visible, but operate in different arenas.

SEO drives clicks from traditional search results while GEO secures mentions inside AI-generated answers.

By blending both strategies and optimizing for ranking signals and AI-readiness, you ensure that users find you whether they click on links or read instant responses.

Start with your best pages, add structure and citations, and track both metrics to stay ahead in the rapidly evolving search landscape.

Your brand in the age of generative search: How to show up and be cited

Mon, 28 Jul 2025 12:00:00 GMT

We are experiencing an unparalleled seismic shift in digital discovery since Google was introduced to the public.

The rise of generative AI and large language models (LLMs) is fundamentally changing:

How users search.
How content is surfaced.
How businesses gain visibility.

This shift from discrete queries to continuous, AI-driven experiences is transforming the rules of engagement.

Welcome to the era of GEO – generative engine optimization.

This is the new battleground for visibility. It’s not about where you rank. It’s about whether the AI understands, trusts, and cites you.

This is not something that’s coming – it’s here today.

From search to synthesis: The big shift and why it matters

Search used to be simple. A user typed in a query, a list of links appeared, and the journey began.

SEO was also simple, with techniques like keyword stuffing and building backlinks being the main tools some SEO shops used to get attention.

However, AI-powered answer engines like Google’s AI Overviews and Gemini , Bing Copilot, ChatGPT , and Perplexity have replaced that model with synthesized responses.

AI anticipates follow-ups, assembles answers from multiple sources, and often eliminates clicks altogether.

In this new model, ranking is no longer the goal. Relevance, trust, and inclusion in AI outputs are the new measures of visibility.

Why this shift demands your immediate attention

This is a game-changer, the most significant change in the brief history of search
There is a much higher bar to entry because AI prioritizes contextual value over things like backlink quality and exact-match keywords
While SEOs have been discussing E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) for several years, it’s now non-negotiable for visibility
If AI answers the question without citing you, then you don’t exist in that moment

GEO vs. SEO: How the big shift is playing out

Traditional SEO focused on attracting clicks from a list of links in search results. GEO is about being cited by the AI itself. The difference is fundamental:

From clicks to citations: Search visibility was a “blue link.” Now, visibility is inclusion in the answer. If you’re not cited, you’re invisible.
From keywords to context: Search was mainly keyword-based. Now, AI understands meaning, not just exact keyword matches. GEO content is rich in context, incorporating entity optimization and how entities are connected to the content .
From traffic to influence: Influence builds brand equity, even when there’s no click.

This is no mere incremental update.

AI-based searches leverage natural language processing, semantic comprehension, and personalized direct answers.

The focus is more on searcher intent than ever, so the tools can help users get answers quickly.

Your GEO strategy: 7 essential items to survive the great shift

1. Cultivate deep topical authority

Build out deep, helpful content clusters around core themes. Become the go-to resource.

Ensure your content addresses the information needs of customers looking for your products and services, and don’t forget E-E-A-T.

2. Master schema markup

Use structured data aggressively and accurately. It’s a strong signal to Search Engines and AI.

3. Prioritize AI-friendly formats

Lean into summaries, tables, guides, and FAQs.

4. Embrace multimodal content

Use images (with alt text), video (with transcripts), and audio.

Real-time indexing using the IndexNow protocol is also extremely useful here.

5. Build brand authority beyond your website

AI-based search tools rely on information from trusted sources.

Focus on building your brand’s identity with an omnichannel approach, including digital PR, guest posts on reputable websites, and actively seeking reviews.

Your overall digital footprint matters.

6. Focus on semantic and entity-based optimization

Move beyond simple keyword matching. Identify key entities (people, places, products, and ideas) relevant to your niche. Schema tagging is also critical here.

7. Brand visibility everywhere for omnichannel discovery

Ensure presence and consistency in text content, images, and videos across web platforms, directories, social sites, and APIs.

Because paid search also incorporates AI, don’t let your SEO and paid search teams operate in silos.

Digging deeper: 4 foundations for GEO success

Foundation 1: Optimize your platform

GEO still incorporates some SEO best practices that include:

A clear site structure.
Fast downloading for a better user experience and spider crawling efficiency.
Avoidance of client-side rendered JavaScript for static or near-static content.

There is more to consider based on how these new generative search engines function.

A great user experience is key because user engagement data will become a critical signal for AI inclusion.

Fast download times also make spiders work more efficiently, which is helpful for bots to get information into their systems quickly.

Leveraging IndexNow can also help get content into Bing more quickly.

Their connection to OpenAI makes this a significant advantage, considering ChatGPT is currently the most-used LLM-based search .

Foundation 2: Digital asset optimization

AI-based searches are multi-modal, so your digital assets must be optimized for maximum visibility in generated answers.

Text content is still critical, but so are images and videos.

Regardless of format, all content must be consistent across all platforms to avoid mixed signals about your brand.

Text content must also be varied. Leverage:

FAQs.
Comparisons.
Definitions.
Use cases.

This allows for variations of the content that LLM-based tools for appearance in answers can easily ingest.

Content must also incorporate these techniques:

Signal intentionality.
Support retrieval-augmented generation (RAG).
Be created in various formats to improve chances of surfacing, regardless of how users wish to consume content (multimodal search).

Foundation 3: Structured data – The AI lifeline for discoverability

LLMs infer meaning well, but not perfectly. Structured data removes ambiguity and improves your chances of being cited.

Structured data means that the website content includes:

Semantic structure.
Logical information architecture.
And, most importantly, schema markup.

Google and Bing strongly encourage the use of schema markup.

For example, during the March 2025 Google Search Live event, Googlers reinforced that structured data is central by including the following on a slide during one of the presentations:

“Structured data is critical for modern search engines. Structured data is efficient, for computers easy to read, and very precise.”

Several studies have shown that ChatGPT is currently the most-used LLM-based tool .

The tight connection between Bing and OpenAI is another critical reason for leveraging schema tagging.

To help you understand how prepared your digital ecosystem is, audit for:

Visibility and consistency across channels (omnichannel audit, including text content, images, videos, and other formats).
Website technical SEO audit, including schema tagging.
Content audit to find information gaps and updated content formatting.

Foundation 4: Deep topical coverage

Gone are the days of optimizing for exact-match keywords.

Organic search has been moving to intent-first search results for several years, and AI-driven search takes intent-driven answers to a new level.

Creating content for GEO requires a blend of precision and human-centered design-style empathy:

Semantically rich : Incorporate related topics and meanings.
Factual and trustworthy : Use expert quotes, data, and citations.
Conversational tone : Match the natural style of AI interactions.

Because these new search tools incorporate an understanding of natural language and contextual understanding (including personalization), your sites need to be optimized to ensure a complete understanding of your content.

AI-first personalization means every user sees something different. Your content must address multiple versions of user intent.

For example, someone searching for “Las Vegas hotel pool” may want luxury, quiet, or party vibes. GEO content strategy digs deep into user data to understand applicable topics that fulfill searchers’ needs.

Measurement

Of course, the way we measure success must also change.

It’s no secret that clicks from search engines are going down , so our metrics must adapt. Where we used to measure SEO success through

Ranking reports.
Click-through rates.
Website traffic.

We now need to look at other metrics like:

Visibility in answers.
Citations.
Mentions.

Website traffic is still important, but with declining numbers, you need that traffic to work harder.

Tools like conversion rate optimization and A/B or multivariate testing are a critical part of the GEO toolbox.

The path forward: Be the answer

The AI-first world is here. Models are choosing sources. Answers are being built without your input, unless you optimize for it.

GEO is not about futureproofing. It’s about “present proofing.”

Brands that embrace GEO:

Get cited.
Gain influence.
Stay visible.

Those that don’t? They risk vanishing entirely.

Visibility now belongs to those who are understood, cited, and structured for the AI moment.

Be the answer or be left out.

A special thanks to Benu Aggarwal and Bill Hunt for their contributions to this article.

The Great Decoupling of search and the birth of the agentic web

Fri, 25 Jul 2025 13:00:00 GMT

This is the opening chapter of “The Last Click: How AI Agents Are Rewiring the Internet’s Attention Economy,” a series about how AI is changing the web.

The email arrived at 3:47 a.m., marked urgent. Sarah Chen, head of content at a mid-sized publisher, was watching 20 years of assumptions collapse in real time.

Her overnight analytics showed something impossible: search visibility had doubled in six weeks, yet traffic had fallen 30%.

The charts on her screen formed a pattern she’d never seen before: two lines that had moved in harmony since the company’s founding were now pulling apart like tectonic plates.

“It’s happening everywhere,” her lead analyst would tell her hours later, sliding a tablet across the conference table.

Forbes, NPR, Healthline, and even Wikipedia. Almost every major publisher showed the same divergence.

Impressions climbing. Clicks vanishing. The fundamental physics of the web had changed while they slept.

What Chen witnessed that March morning in 2025 was the culmination of a quiet revolution three years in the making.

The contract that built the modern web – visibility in exchange for visits, rankings in exchange for traffic – had been unilaterally rewritten.

Google had learned to satisfy its users so completely that sending them anywhere else became unnecessary for most searches.

I call this phenomenon the “Great Decoupling” – the historic severing of the link between being found and being visited.

It represents the most significant shift in the web’s economic foundations since the search engine’s invention.

Like most revolutions, it began with a simple idea that no one (not even its creators) thought much of at first: the release of ChatGPT and the creation of conversational chatbots.

The architecture of dependence

To comprehend the magnitude of what’s breaking, we must first understand what we built.

The modern web rests on an elegant three-way value exchange that emerged, without central planning, in the late 1990s.

Publishers invest in content creation.
Search engines index and organize that content.
Users navigate to publishers through search results.

Revenue flows – through advertising, subscriptions, and commerce – complete the circuit.

This wasn’t merely a technical arrangement. It was an economic constitution.

Entire industries crystallized around the reliable conversion of search rankings into visitor streams.

The metrics were dependable:

Achieve Position 1 for a valuable search term, receive approximately 28% of the clicks .
Fall to Position 10, collect less than 3%.

The mathematics of attention had rules, and fortunes were built on understanding them.

Google processes 5 trillion searches annually worldwide.

The company became not just a utility but the primary artery through which human attention flowed.

Publishers optimized every pixel for this reality. They:

Crafted headlines to match search queries.
Structured content to win featured snippets.
Built entire editorial strategies around ranking factors.

The relationship was symbiotic but unequal. Google held the power to redirect the world’s attention with algorithm updates that could devastate entire sectors overnight.

Yet publishers accepted this asymmetry because the bargain still functioned: create value, earn visibility, receive traffic.

The first cracks in this foundation appeared so gradually that few recognized them as structural threats.

The erosion begins

Google’s transformation from search engine to answer engine began with innocent conveniences.

In 2012, the Knowledge Graph started providing direct answers for factual queries.

What’s the capital of France? How tall is the Eiffel Tower?

Small boxes appeared atop search results, saving users a click for simple needs.

Publishers barely noticed. These were marginal queries, unlikely to drive meaningful traffic anyway.

The real content – analysis, guides, investigations, reviews – still required human insight that couldn’t be condensed into a box.

Featured snippets arrived in 2014, extracting the most relevant paragraph from a webpage and displaying it prominently above all results.

Publishers initially celebrated. Position zero, they called it, an opportunity to stand above even the first result. They didn’t yet understand they were training their replacement.

Mobile usage accelerated the shift toward self-contained answers. Smaller screens rewarded brevity. Voice assistants demanded singular responses.

Each innovation chipped away at the click-through imperative.

By 2019, Rand Fishkin’s research showed that 50.3% of Google searches ended without an outbound click . The web was being slowly enclosed.

Yet traffic still flowed. The ecosystem adapted.

Publishers learned to optimize for featured snippets while still capturing clicks through compelling meta descriptions.

The erosion was manageable, a tax on doing business in Google’s world rather than an existential threat.

Then, on November 30, 2022, OpenAI released ChatGPT to the public.

The catalyst arrives

The screenshots spread across X like wildfire.

Users were having conversations – real, nuanced, surprisingly intelligent conversations – with an AI that answered questions without showing a single hyperlink.

No ads. No People Also Ask boxes. No 10 blue links.

Just answers.

ChatGPT reached one million users in five days . By January, it was the fastest-growing consumer application in history.

The implications were immediately clear to anyone paying attention: if users could get comprehensive answers through conversation, why would they need traditional search?

Inside the Googleplex, the response was unprecedented. Sundar Pichai declared a “code red,” the first in the company’s history.

Engineers were pulled from other projects. Vacation policies were suspended.

The company that had defined online navigation for a generation suddenly faced a future where its core product might become obsolete.

The emergency was warranted. Internal modeling suggested three possible scenarios:

Search behavior remains unchanged, and Google maintains dominance.
Users split time between Google and conversational AI, fragmenting the market.
Conversational AI becomes the primary interface, reducing Google to a backend data provider.

The first scenario was already failing. ChatGPT’s explosive growth proved users wanted something different. The third scenario was existential.

Google chose to pursue the second while positioning for a fourth option: becoming the conversational AI provider itself.

The response that changed everything

Google’s counter-offensive began with Bard, announced hastily in February 2023.

The demo went catastrophically wrong – the AI gave an incorrect answer about the James Webb Space Telescope during the live presentation.

Google’s stock dropped $100 billion in value overnight. The mighty search giant looked vulnerable, perhaps for the first time.

But Google’s infrastructure advantages ran deep. By May 2023, at their annual I/O conference, they unveiled Search Generative Experience (SGE) .

Unlike ChatGPT’s separate interface, SGE embedded AI-generated summaries directly into search results.

Multi-paragraph answers appeared above the traditional links, complete with citations and follow-up questions.

The placement was strategic and revealing.

By positioning AI summaries at the top, Google acknowledged they were the superior user experience.
By keeping traditional results below, they maintained plausible continuity with the old model. Publishers could still theoretically receive traffic – if users scrolled past the comprehensive answer already provided.

Early access was limited, positioned as an experiment within Google Labs.

Publishers who gained preview access reported immediate traffic declines on affected queries. But the rollout was gradual enough to avoid panic. The boiling frog strategy had begun.

The quiet revolution accelerates

Through late 2023 and early 2024, Google refined its approach. The AI summaries (now renamed “ AI Overviews “) became more sophisticated.

They could synthesize information from multiple sources, compare products, provide step-by-step instructions, and even generate basic visualizations.

Each improvement made clicking through less necessary.

Why visit five websites to research a purchase when Google’s AI could provide a comprehensive comparison in seconds?
Why dig through a recipe blog’s life story when the AI extracted just the ingredients and instructions?

On May 14, 2024, Google dropped the experimental label. AI Overviews rolled out to all U.S. users by default. No opt-in required. The future had arrived.

The impact was immediate and unevenly distributed.

Many lost as much as half of their organic traffic. Informational publishers watched their lifeblood drain away.

Health websites that had invested millions in medically reviewed content saw Google’s AI summarize their articles without attribution beyond a tiny citation.

Recipe sites that monetized through advertising found their carefully crafted content reduced to bullet points above the fold.

June 2024 brought a subtle but crucial change: the return of pagination.

After years of infinite scroll, Google now required users to actively click “Next” to see results pushed below AI Overviews.

The friction was minimal, but the message was clear: the AI answer should be sufficient.

The breaking point

March 2025 marked the definitive break. Google launched Gemini 2.0 alongside “AI Mode” – a full conversational interface within search.

The same week, a core algorithm update dramatically expanded AI Overview coverage. Queries that had previously returned traditional results now triggered AI summaries.

The scope expanded from purely informational searches to commercial research, local queries, and even navigational searches.

The numbers were staggering. Ahrefs reported that 16% of all U.S. searches now displayed AI Overviews .

For informational queries – the bread and butter of content publishers – coverage approached 60%.

Click-through rates for the top organic position plummeted from 28% to less than 10% when an AI Overview appeared.

Sarah Chen’s 3 a.m. email was part of a flood.

Across the industry, analytics dashboards showed the same pattern:

Impressions skyrocketing as Google cited more content in AI summaries.
Clicks evaporating as users found those summaries sufficient.

The crocodile mouth – that vivid image of diverging lines – became the industry’s shared nightmare.

But the most disturbing revelation was still to come.

The inversion

In June 2025, Patrick Stox from Ahrefs published data that defied conventional logic.

Analyzing their own content, they discovered that the correlation between impressions and clicks had turned negative .

Pages that gained visibility through AI Overview citations actually received fewer clicks than similar pages without that exposure.

The mechanism was perverse but predictable.

When Google’s AI summarized content comprehensively, users had no reason to visit the source.

But Google counted showing the citation as an “impression,” inflating visibility metrics while destroying their meaningful connection to traffic.

This was an inversion of the fundamental assumption underpinning web publishing.

Being found by Google was no longer a predictor of being visited. In some cases, it predicted the opposite.

The forces that made it inevitable

Why did Google consciously break the value exchange that built its empire? Three interlocking forces made the Great Decoupling inevitable.

Technological capability

The breakthrough beyond that AI could answer questions was that it could do so cheaply enough for billions of queries.

GPT-4’s release in March 2023 proved that large language models had crossed a quality threshold.

Google’s Gemini models, trained on decades of search data, could match and eventually exceed that quality.

Inference costs plummeted from dollars to fractions of a cent per query. The economics of AI answers became superior to the economics of ten blue links.

Competitive dynamics

Google faced a strategic trilemma. Maintaining traditional search meant ceding ground to ChatGPT and its inevitable successors.

Adding AI features while preserving click-through rates would create an inferior product that satisfied no one.

Fully embracing AI answers meant cannibalizing their own referral traffic but maintaining control of the user experience.

The choice, viewed from Mountain View, was obvious.

Better to disrupt yourself than be disrupted.

Better to keep users within your ecosystem, even if it meant fundamentally altering that ecosystem’s relationships.

User behavior

The dirty secret was that users had been ready for this shift for years.

Decades of UI improvements had trained them to expect instant gratification.

Featured snippets taught them to look for answers at the top of the page.
Voice assistants proved they’d accept a single synthesized response.
Mobile usage made comprehensive answers more valuable than lists of links to tap through.

When AI Overviews appeared, users didn’t revolt – they adapted instantly.

Engagement metrics showed they preferred the new experience by every measure: shorter time to answer, fewer search refinements, and improved satisfaction scores. The market had spoken.

The new economics

The numbers tell a story of transformation, not just disruption:

Zero-click searches reached 65% by mid-2025, up from 50% just three years earlier.
Google’s search volume hit record highs even as outbound clicks reached record lows.
400 million people used ChatGPT weekly , each session representing searches that never touched Google.
Publishers reported traffic declines of 20-60% despite maintaining or improving search rankings.
Google’s search advertising revenue, paradoxically, continued growing as ads integrated into AI answers.

The mathematics of attention had new rules. Impressions became a vanity metric.

Rankings mattered only insofar as they influenced AI citation.

The click-through rate – the fundamental constant of SEO – became a variable trending toward zero.

Living in the aftermath

Publishers scrambled to adapt, pursuing divergent strategies:

The Optimizers doubled down on AI visibility, restructuring content to maximize citation likelihood. They:

Added schema markup.
Wrote in easily extractable formats.
Positioned themselves as authoritative sources for AI training.

Their bet: that brand exposure in AI answers would create value through other channels.

The Builders created what AI couldn’t summarize:

Interactive tools.
Proprietary databases.
Community platforms.
Personalized experiences.

They sought to make their destinations irreplaceable rather than their content.

The Exodus abandoned search dependency entirely. They built direct relationships through:

Newsletters.
Podcasts.
Owned platforms.

They viewed the Great Decoupling as liberation from Google’s algorithmic whims.

Meanwhile, Google walked a tightrope. They needed publisher content to generate quality answers, but no longer needed to reciprocate with traffic.

New licensing deals emerged – payment for content usage rather than traffic exchange. But these deals favored large publishers, accelerating consolidation.

The question of sustainability

The Great Decoupling raises a fundamental question: Can an information ecosystem sustain itself when value creation and value capture are severed?

The optimistic view suggests a new equilibrium will emerge.

Quality will concentrate among publishers who provide genuine value beyond summarization.
AI companies will be forced to share revenue to maintain content pipelines.
Users will benefit from both better answers and deeper sources.

The pessimistic view sees a tragedy of the commons.

Why invest in original research if it will be instantly summarized without compensation?
Why build comprehensive resources if they’ll be reduced to bullet points?

The web could hollow out, becoming an echo chamber of AI-generated content citing other AI-generated content.

The realistic view acknowledges both possibilities.

Some sectors will thrive in the new paradigm – those providing services, communities, and experiences that resist summarization.

Others will wither – particularly those built on advertising-supported information arbitrage.

The end of the beginning

I named this phenomenon the Great Decoupling not to eulogize the web that was, but to recognize the web that’s becoming.

We stand at an inflection point as significant as the beginning of search itself.

The rules that governed two decades of digital publishing are changing, and new ones are being written in real time today.

This is more than a technology story.

It’s a story about knowledge, power, and the changing nature of human information consumption.

It’s about what happens when the intermediaries become the destinations, when the map becomes the territory, when the index becomes the library.

Sarah Chen’s dashboard that March morning showed more than diverging metrics. It revealed a fundamental shift in how humans interact with the web.

The Great Decoupling arrived, marking the start of the agentic web. The only question now is what we build in its wake.

This article is released as part of a companion set of assets:

GSC Guardian Chrome Extension Version 2.9 with the In-Bulk Annotations Import Update.
The Great Decoupling: Origins + Timeline of Events Live Dataset .
GSC Guardian CSV Builder .
A downloadable spreadsheet kept live with the events that are changing the internet into the agentic web.

The next chapter will examine the birth of the agentic web.

Building a machine-readable brand: Strategic visibility across all search modalities by Semrush Enterprise

Fri, 25 Jul 2025 11:00:00 GMT

AI is now your silent brand manager.

Marketing leaders face a new imperative: engineer a brand identity that machines can read, context they can understand, and presence they can discover across every customer journey.

Your brand must maintain consistency across human and artificial intelligence ecosystems. This playbook distills the chaos into clear, actionable steps. Each section is a pragmatic directive.

1. Decode customer search behavior

Machine intelligence influences brand visibility across a wide range of customer touchpoints. Customers now search using images they snap, show their world via video while talking to interfaces like ChatGPT to find what they need. This shift affects every marketing leader, regardless of search expertise background.

Multi- = Multiple, many
Modal = Modes or modalities (ways of doing something)

Multimodal search is a term describing systems like ChatGPT (and many other LLMs) that process multiple input types simultaneously.

TL;DR: text, images, voice, and video inputs processed together rather than separately.

This shift makes multimodal search strategy essential for all marketing leaders, regardless of background.

Understand how your audiences use images, text, and voice and video to search for things online. What combos matter most for your business?

From snapshots to solutions: How customers search today

Visual search is growing , according to Liz Reid, head of Google Search:

“Lens queries are now one of the fastest growing query types on Search, and younger users (ages 18-24) are engaging most with Lens.”

While executives recognize the theoretical importance of visual search, understanding real-world customer patterns determines strategic resource allocation.

The following behavioral mapping demonstrates how customers integrate visual search into purchase decisions.

Key actions

Identify modalities: Understand how your audience uses images, text, voice, and video to search. Do not wait for Google or ChatGPT to provide this data. Deploy targeted questionnaires to survey existing customers. Pull 100 recent support tickets or DMs. Sort by: Messages with attached photos and messages mentioning “I saw this on…”
User journey mapping: Track how users start, switch, and end their journeys across modalities. For instance, a shopper might snap a photo of a product, ask a voice assistant for reviews, and then switch to text search for pricing.
Presence audit: Assess where your brand appears (or doesn’t) across these journeys. Use analytics to spot gaps and opportunities for visibility in AI-curated results. Track LLM mentions across models.

2. Products and packaging are now landing pages

Packaging and products must be considered with an optimization mindset meant for the web.

Your product and packaging are your landing page. They need to be understandable and contextualizable by AI. If a machine can’t read it, you’re invisible at the crucial moment of consumer consideration.

Tips to optimize your content for multimodal discovery

Use clear, sans-serif fonts and high contrast between text and background on your packaging.
Analyze where LLMs struggled to “read” or contextualize your packaging.
A great practical test is to see how your color combination looks in grayscale.
Make sure your content answers common queries and customer prompts.

3. Factor for sentiment

Create content that has the tone and emotional context for your intended audience. Establish a baseline for text, videos, and images.

What is the current emotional tone of your brand’s imagery? Does the emotional tone of your brand imagery match the intended feeling of the product?

4. Build a visual Knowledge Graph for your brand

A multimodal AI doesn’t just see your product; it sees your product and everything else you placed next to it. These adjacent objects help machines infer your price point, target customer, and their context.

Successful brands curate their image. If your product or service caters to a specific lifestyle, you must deliberately curate the visual knowledge graph of each photo or video your brand puts out.

5. Foster cross-functional team collaboration

Multimodal AI is not going anywhere. It is a trend that fosters new uses of technology and new customer behaviors.

As a marketing leader, your goal is to align marketing, SEO, content, design, and engineering teams around multimodal search goals. This involves establishing new workflows for asset creation, tagging, and QA.

Train teams in multimodal SEO and AI basics to ensure everyone “speaks the same language.”

Next steps

Define owns what: roles for content, SEO, design, and data teams.
Training and upskilling: what every team needs to know about multimodal search.
Workflow templates for asset creation, review, and optimization.

6. Track what matters: Measure, evaluate, and iterate

Define new success metrics using both quantitative and qualitative frameworks. Monitor search platforms, user behaviors and emerging trends.

Building a feedback loop between innovation and measurable business results is key.

Tangible next steps

Track citations, not keywords. Monitor how often and where your brand is mentioned across LLM models to get a better view of your digital footprint.
Analyze the tone and sentiment to understand audience perception, on social media but also within LLMs.
Check credibility and contextual placement: break down where and how your brand is mentioned.

7. Stay trustworthy: Embed ethics, accessibility, and brand safety

When the AI can see why your information is reliable, it’s more likely to retrieve your content and use it in its synthesized answer.

AI doesn’t just accept multimodal inputs from the user; it actively seeks out and synthesizes multimodal content to build its answers.

Feed large language models proper brand fuel by ensuring all multimodal content is accessible (alt text, captions, subtitles, readable chunks), ethically sourced, and compliant with privacy and copyright standards.

The medium is the message

Marshall McLuhan famously said: “the medium is the message.” LLMs, as the medium, are shaping your brand narrative. AI is now a brand ambassador, like some Hollywood types or famous social media influencers.

When AI drives search, even the most recognizable brands must ensure their visibility aligns with their value.

In a world where users search via vision, images, voice, and text, and with what they experience around them in real time, brands that thrive will design every asset, process, and team around discoverability.

Author: Myriam Jessier, Technical Marketing Expert

How AI Is Reshaping SEO: Insights from Search Engine Land and Fractl by Edna Chavira

Thu, 24 Jul 2025 21:36:00 GMT

AI is fundamentally changing how people search, how content is surfaced, and how brands stay visible. In this exclusive webinar, access new research from Search Engine Land and Fractl, revealing how generative AI is reshaping SEO, content, and PR in 2025.

Join Kelsey Libert and Kristin Tynski, co-founders of Fractl, alongside Danny Goodwin, Managing Editor at Search Engine Land, as they unpack findings from a survey of hundreds of marketers and explore how leading brands are adapting. In How AI is Reshaping SEO: Challenges, Opportunities, and Brand Strategies for 2025 , you’ll discover:

How AI is influencing search behavior across demographics
What marketers are doing now to evolve SEO, PR, and content workflows
Key risks and ethical standards around AI-generated content
What LLM training set data reveals about domain authority and brand visibility

Whether you’re building content workflows or rethinking your entire SEO strategy, this session offers expert insights and practical next steps to stay ahead. Save your spot here.

A smarter Reddit strategy for organic and AI search visibility

Thu, 24 Jul 2025 14:00:00 GMT

Reddit doesn’t need to feel like a black box.

When approached the right way, it’s a goldmine for influence, engagement, and search visibility.

With over 1.21 billion monthly users and a growing presence across Google SERPs, Reddit has become a serious force in the search everywhere optimization landscape.

If it’s not on your radar yet, you’re likely missing a major opportunity.

Here’s why Reddit deserves a place in your marketing strategy – and how to show up in a way that builds trust, not backlash.

Why Reddit deserves your attention

Reddit shows up in Google searches more than ever

Google loves Reddit. In 2024, Reddit’s presence in search results shot up by about 191% .

Whether someone Googles “best laptop 2025,” “is vuori worth the price” or “how to get my baby to sleep through the night,” Reddit threads rank higher than many well-optimized pages targeting the same terms.

Studies show Reddit is the second most common site cited by ChatGPT

Tools like ChatGPT and other generative AI models are increasingly pulling insights from Reddit.

Reddit offers authentic, user-generated content that reflects real opinions, experiences, and advice.

If your brand is showing up in these conversations, or better yet, contributing to them, then it boosts your chances of being referenced in AI responses.

That’s another layer of visibility you shouldn’t ignore in today’s search landscape.

Redditors are actively researching products

People use Reddit to solve problems and make purchasing decisions.

From /r/Parenting/ to r/PersonalFinance, Redditors are sharing reviews, asking questions, and influencing others on which brands and products to choose from.

That’s huge for the consideration phase of today’s complicated customer journey.

How to win on Reddit and not get banned

It’s about value, not sales

Redditors sniff out inauthentic marketing quickly. If you roll in with corporate lingo and hard sells, you’re finished. Successful brands on Reddit show up with helpful answers, not ads.

That means posting under transparent, branded usernames like BrandName_Official or BrandName_Becky.

Like any good content strategy, it’s all about helping, contributing, and building trust.

Subreddit culture is not negotiable

Every subreddit has its own vibe, rules, and expectations.

Some warmly welcome brands, while others will ban you for a single off-base post.

You have to earn your place by showing up respectfully and always adding value.

Dig deeper: Reddit for SMBs: A hidden SEO and engagement goldmine

A crawl, walk, run approach is essential

A surefire way to waste your time with Reddit is to jump in headfirst right away.

Redditors value authenticity and community trust, and that must be earned gradually.

Jumping in too quickly with promotional content risks backlash, downvotes, and bans.

Crawl (2-3+ months)

Start slow. Create your profiles and start by monitoring conversations.

Evaluate post formats, community rules, moderation strictness, and attitude toward brands.

Make sure you really take the time to understand Reddit’s culture and community tone.

In this phase, you should also take your time to learn the language of your audience.

Once you’ve done that, upvote, comment, and blend in. Don’t post anything branded quite yet. The crawl phase is all about building karma and familiarity.

Walk phase (1-2+ months)

Now, you can really start engaging.

Answer relevant questions where your product fits.
Include expert insights.
Maybe even create threads based on FAQs you see popping up.

At this stage, you should aim to have a few branded profiles with a clear engagement plan rooted in SEO research.

Look for high-ranking Reddit threads around your core keywords.

Run

Launch your own branded subreddit.

Document your processes and start finding ways to cross-train, increase efficiency, and get the most out of our Reddit.

Build content pillar plans to ensure you’re really maximizing. Train and involve your support team.

Think of Reddit as an always-on focus group, reputation manager, and content testing lab all rolled into one.

Top tips for Reddit activation

Be transparent : Always disclose who you are and if you’re affiliated with a brand.
Follow the rules : Each subreddit is different. Don’t wing it.
Prioritize value : Solve problems. Don’t pitch.
Use SEO to guide your strategy : Root your strategy in SEO by leveraging tools like Semrush , Ubersuggest, or Ahrefs.

Reddit is no longer optional for enterprise brands – it’s essential.

As the platform continues to dominate Google search results and feed generative AI with real user insights, brands that ignore it risk falling behind.

But success on Reddit isn’t built from quick wins or copy and paste strategies. It’s about patience, transparency, and a focus on genuine value.

Treat Reddit as a long-term play and it’ll be a platform that can fuel organic visibility, trust, and influence across the entire search ecosystem.

Start small, stay consistent, and earn your place. Your future SEO strategy will thank you!

In GEO, brand mentions do what links alone can’t

Thu, 24 Jul 2025 13:00:00 GMT

The SEO landscape is suddenly flooded with mentions of GEO .

Short for generative engine optimization, it’s quickly becoming the industry’s biggest buzzword – one that’s sparked heated debate, frustrated much of the SEO community, and yet continues to gain traction.

Backed by Silicon Valley VC hype and easy to grasp in theory, GEO might be the most significant shift in SEO in a decade.

But it’s not just a rebrand. GEO is fundamentally different, and understanding that difference is critical.

This article breaks down how brand mentions and links work within generative engine optimization and how to approach them strategically.

The function of links in GEO

Links still matter in GEO, but perhaps not as much as in traditional SEO.

Links still play a crucial role in the web’s ecology, providing search engines with valuable information about your website and brand.

However, when it comes to generative engine optimization, you may need fewer links than in traditional SEO, as the long-tail and highly specific search queries used by generative engines are often less competitive.

But you should still build links. People are still using search engines, and LLMs leverage search engines, so you can’t throw SEO and links away.

Dig deeper: Survey says: AI is changing search, but links still matter to SEOs

Why brand mentions matter in GEO

Brand mentions are critical in GEO because natural language processing (NLP) and machine learning models rely on them to reduce ambiguity and increase certainty in their results.

Mutual information around a topic, entity, or subject helps NLP systems to better understand content.

Think of the word “president.” Sure, the systems know what a president is, but adding “Trump” afterwards gives the systems more certainty around which president you’re talking about.

In an oversimplified way, that’s what mutual information is.

As a brand gets mentioned in relation to the products, services, or industry it operates in, it becomes an authoritative source of information on those topics.

Search engines and generative engines use brand mentions as a signal of authority and relevance, much like links have been used in traditional SEO.

The large databases and constantly refreshed data that power generative engines mean brand mentions play an even more crucial role than in standard search engine optimization.

These mentions provide the mutual information and context needed for the language models to confidently answer specific, long-tail queries.

In GEO, building a strong presence through brand mentions should be a top priority, alongside (but potentially more important than) traditional link building efforts.

The goal is to establish your brand as an authoritative source on the topics and queries relevant to your business.

So, how do you do this?

Dig deeper: How to harness the power of brand mentions across the search universe

Think marketing communications, not links

When it comes to building brand mentions, the focus should be on marketing communications, not just link building.

To gain the best results for GEO, you need to become hyper-specific in your approach to generating brand mentions.

The goal is to supply the language models powering generative engines like ChatGPT , Gemini , and Perplexity with the relevant mutual information and context they need.

So, how do you do this?

There are several key strategies for effectively communicating information about your brand online.

Targeted media outreach and public relations efforts to get your brand mentioned in relevant industry publications and blogs.
Actively participating in online communities, forums, and social media discussions related to your products, services, or industry.
Creating and promoting high-quality, informative content that positions your brand as an authority on key topics.
Leverage influencer marketing and brand ambassador programs to increase your brand’s visibility.

The key is to think beyond just links.

Focus on building a robust presence through diverse marketing communications that provide the mutual information and context generative engines need.

Targeted media outreach and public relations

Targeted media outreach and public relations efforts are crucial for generating the high-quality, contextual brand mentions that are so valuable for GEO.

Digital PR should now focus on securing brand placements and mentions in relevant industry publications and media outlets.

The goal is to get your brand referenced in the right context, providing the mutual information the language models powering generative engines need.

To do this effectively:

Identify the key industry publications, blogs, podcasts, and other media channels where your target customers and prospects are consuming content. Tools such as SparkToro will allow you to see what publications your audience reads.
Develop a targeted media outreach strategy to pitch story ideas, comment on trending topics, and position your brand as an authority.
Leverage your company’s subject matter experts, thought leaders, and spokespeople to secure guest posts, interviews, and other branded content opportunities.
Monitor media coverage and engagement to identify additional opportunities for brand mentions and expand your reach.

The focus should be on building a strong presence through high-quality, contextual brand mentions – not just links.

This will help ensure your brand is visible and recognized as an authority by the language models powering generative engines.

Dig deeper: Why PR is becoming more essential for AI search visibility

Bonus tip: Watch the publications making LLM deals

A bonus tip here is to look out for publications making LLM deals. This is happening more and more.

ChatGPT, in particular, is making deals with publishers to obtain their content and use it in its systems. As a result, they’re more likely to surface that content in their answers.

Dig deeper: OpenAI’s growing list of partnerships

Actively participating in online communities, forums, and social media discussions

Back in 2016, I engaged in a strategy where we would have freelancers join relevant Reddit groups, Facebook communities, and other online forums related to our clients’ industries.

The goal was for them to actively participate, build trust over 4-5 weeks, and then start sharing our clients’ content.

The freelancers would join these communities and actively participate, engaging with posts and discussions in a natural, authentic way.

After building up trust over several weeks, they would then share our clients’ relevant content, often introducing it by saying something like “Hey, I just found this and wanted to get your thoughts.”

This approach might still work today, for a few key reasons:

It generates genuine public responses and feedback on the content, both positive and negative.
It helps build mutual information and mentions around our clients’ brands within these online communities.
As language models and generative engines increasingly leverage social media signals, this type of organic community engagement can become a valuable part of GEO strategy.

Brands need to be cautious when engaging in online communities, as overt promotional tactics can backfire.

There are also options to partner directly with community moderators and admins to sponsor discussions or host Q&A sessions that feature your brand transparently.

Creating high-quality, informative content to share organically through relevant channels can also be an effective approach.

The core idea is to focus on building genuine relationships and providing mutual information, rather than relying on covert tactics.

This type of authentic community engagement will be increasingly important for generative engine optimization in the years to come.

Dig deeper: The rise of forums: Why Google prefers them and how to adapt

Creating and promoting high-quality, informative content

If you follow me online, you’ll know that I’m against creating content that chases keywords.

Creating content that people wouldn’t know how to search for is key.

Expected content : This is content you expect to find if you search.
Unexpected content: Refers to information that people can’t search for because they are unaware of its existence. I suggest focusing on this.

Creating and promoting high-quality, informative content matters more than ever for GEO.

Not only can it help increase your brand’s visibility and credibility, but it can also provide the language models with new information and insights they haven’t encountered before.

his content doesn’t need to follow an “ultimate guide” format to be valuable.

Instead, focus on identifying unique angles, data-driven insights, or emerging trends that your audience will find genuinely helpful and informative.

The key is to create content that provides value beyond just keyword-driven SEO.

Share content such as:

Original research.
Expert analysis.
Forward-looking perspectives.

This positions your brand as a trusted authority and earns valuable mentions and citations from both users and language models.

Don’t be afraid to tackle topics that haven’t been extensively covered. Focus on:

Emerging needs.
Unaddressed pain points.
Areas of uncertainty in your industry.

This kind of content is more likely to resonate with your audience and the language models powering GEO.

Leveraging influencer marketing

Instagram recently announced that it would allow the web to index all its content, which matters for GEO.

Indexed content from Instagram will now show up in search results.

This means leveraging the power of influencer marketing can be a valuable tactic for GEO.

By creating micro-influencer programs, you can seed mentions of your brand, products, and industry across a wide range of niche audiences.

While big-name influencers can still have their place in social media marketing, the focus for GEO should be on maximizing the number of authentic, contextual brand mentions from micro-influencers with passionate, engaged followings.

These micro-influencers have more than social media presence. Many also have their own:

Blogs.
Newsletters.
YouTube channels.
Other web properties.

Partnering with them can introduce your brand to new audiences and increase your overall web presence and citation network.

The key benefit of influencer marketing for GEO is the ability to generate a high volume of contextual, authoritative brand mentions across a diverse set of web properties and audiences.

Dig deeper: Why creator-led content marketing is the new standard in search

Mass adoption increases ambiguity

It’s worth revisiting a core principle of natural language processing: machines dislike uncertainty and work hard to reduce ambiguity.

That remains true in GEO – but there’s a twist.

As more brands adopt the same tactics, such as generating large volumes of brand mentions, they risk introducing more ambiguity, not less.

Language models can begin to struggle with signal noise, making it harder to differentiate true authority.

This dynamic is not unlike the early days of PageRank, when popularity tactics eventually triggered the need for more sophisticated filtering systems.

The takeaway: GEO tactics won’t stay static.

What works today may lose effectiveness tomorrow as language models evolve to cut through generic, overused strategies and focus on deeper signals of trust and relevance.

To stay ahead, you’ll need to remain agile – closely watching how generative engines respond to scale, repetition, and spam.

Evolve with them, and you’ll continue to earn visibility where it matters.

The AI-driven reputation repair toolkit: What’s working in 2025

Wed, 23 Jul 2025 13:00:00 GMT

Artificial intelligence is reshaping how brands engage with consumers and redefining how they recover from reputational damage.

As online conversations move faster and misinformation spreads more easily, companies are turning to AI to manage their reputations quickly, effectively, and at scale.

About 70% of professionals view AI as critical to their organization, per a Harvard Business Review study.

This article tackles the most effective AI-powered tools and platforms revolutionizing reputation repair.

From content suppression to proactive sentiment shaping and real-time crisis response, we’ll explore what’s working – and why.

The high stakes of online reputation in 2025

Consumers, investors, and partners make snap decisions based on online impressions.

A single negative news cycle, viral review, or misleading blog post can damage years of brand equity.

Reputation repair is no longer just about PR spin; it’s about technological agility, proactive monitoring, and real-time engagement.

What’s changed in 2025?

Three things:

AI tools have been upgraded, becoming exponentially more accurate and context-aware.
Platforms now favor user-generated content in search rankings, making suppression harder.
Regulatory pressure has forced platforms to improve transparency, but not always takedown processes.

The result is that companies must now build robust, AI-enhanced systems to defend and restore their reputations dynamically.

So, how are they going about this process?

Dig deeper: AI and online reputation: How to stay in control

Core pillars of AI-driven reputation repair

1. Content suppression and de-indexing tools

While legal takedowns (via DMCA, defamation claims, or privacy laws) remain part of the reputation repair toolkit, AI-based suppression focuses on algorithmic dilution.

That means pushing negative content down in search results through strategic, high-authority positive content placement.

What’s working

Leveraging LLMs to semantically map keywords related to negative press and build competing content clusters.
Using AI-driven backlink scoring to automatically distribute reputation-boosting content across authoritative domains.

Examples in practice

Tools like Semrush and Ahrefs help create efficient backlink opportunities, automate tasks, and improve customer outreach. They are commonly used in the reputation repair industry.
SurferSEO and ChatGPT are useful for mapping keywords related to positive or negative press. Pair ChatGPT with tools like SurferSEO or Semrush for the highest-quality results.

2. Automated positive content generation

AI now enables scalable, human-sounding content that aligns with brand values.

From executive thought leadership pieces to customer success stories, platforms like Jasper AI, Copy.ai, and custom LLM pipelines are fueling content creation at unprecedented volume and quality. (As of Q3 2024, Jasper AI is trusted by 20% of Fortune 500 companies for content creation.)

What’s working

Editorial-style prompts are fine-tuned to match brand voice.
Automated cross-channel distribution that schedules and posts to LinkedIn, Medium, YouTube, and other emerging content platforms based on audience relevance.

Examples in practice

Persona-trained GPT pipelines that generate press releases, blog posts, and Q&A articles tailored to Google’s E-E-A-T (experience, expertise, authoritativeness, trustworthiness) guidelines.
Video synthesis tools (e.g., Synthesia) produce brand-authentic testimonial videos in multiple languages. These help suppress negative multimedia content on platforms such as YouTube and TikTok.

3. Real-time sentiment monitoring and crisis response

Speed is everything in a digital crisis.

Advanced AI sentiment engines now monitor traditional media, forums, and social spaces like X, Reddit , Discord, and private forums.

What’s working

Proactive alerting and drafting systems are reducing human decision lag in early crisis stages. They’re also reducing human error across various industries. A 2023 study showed significantly lower rates of error from AI (6.8%) than from humans (11.3%).
Emotion-aware tone modeling can help match public sentiment in responses, avoiding tone-deaf replies.

Examples in practice

AI cloud platforms are monitoring heavy streams of data in real time. By gathering data from social media and various media outlets, these platforms inform organizations about developing crises.
Several tools are already being used to detect and enhance emotions for marketing and advertising purposes. For instance, IBM Watson’s Tone Analyzer and Hume AI are becoming adept at understanding human emotions to improve communications and interaction between machines and humans.

Dig deeper: Reddit: Your new online reputation challenge

Under-the-radar breakthroughs

A few innovations currently making waves:

AI-powered review management : Tools like Rep AI gather reviews from across the internet (Yelp, G2, Google, Amazon, etc.), generate tailored response options, and flag high-risk reviews for human intervention.
Synthetic persona audits : Brands are now using AI avatars with distinct demographics to test how their reputation appears across platforms and regions. This practice helps reveal filter bubble distortions or content gaps.
AI-powered legal drafting : Tools like Harvey.ai and Lexion now help draft cease-and-desist letters or EU “Right to be Forgotten” requests based on real-time content scans.

Challenges and ethical considerations

With great power comes great responsibility. In 2025, the ethical debate around AI-generated reputation repair is heating up. Key concerns include:

Deepfake backlash : When synthetic videos or personas are used without transparency, it can further erode trust amongst consumers. (An estimated 500,000 deepfakes were shared on social media in 2023, according to Deep Media .)
Disinformation suppression vs. censorship : Tools that “de-optimize” content must walk a fine line between ethical suppression and manipulation.
Hallucinations : LLMs still generate inaccurate content at times. If left unchecked, this risks reputational blowback.

Best practice: Human-in-the-loop governance remains essential. AI is not an end-all and be-all; it should support, not replace, human judgment in reputation recovery.

What the future holds

AI reputation repair is moving toward full reputation intelligence platforms. These will combine:

Predictive modeling of reputation risks.
Full-funnel content management (from idea to distribution).
Automated legal, SEO , and PR integration.

Within the next couple of years, expect AI agents to coordinate brand rescue autonomously, reacting faster than any human team ever could.

Dig deeper: Why PR is becoming more essential for AI search visibility

Final thoughts

In 2025, AI-driven reputation repair is no longer optional – it’s foundational.

The brands thriving in the face of reputational threats are those that combine technological sophistication with human oversight and ethical transparency.

Success lies in being proactive, responsive, and adaptable, leveraging the best of AI while never losing sight of human values.

With the right toolkit, companies can recover from crises, emerging stronger and even more credible than before.

Inside Google’s secret search systems: 1,200 experiments, AI agents, and entities

Tue, 22 Jul 2025 15:00:00 GMT

Over the past few months, we’ve conducted a sprawling investigation into Google’s inner workings.

It has led to major discoveries – some of which we’re disclosing here.

While we can’t reveal everything, the insights below offer a clearer view of how Google generates and ranks its results.

What ~1,200 experiments reveal about Google’s inner workings

We obtained a list of nearly 1,200 Google experiments, over 800 of which were active as of June 2025.

This dataset confirms that many components revealed in the 2024 leaks – Mustang, Twiddlers, QRewrite, Tangram, QUS, and others – remain central to the system.

At the same time, it surfaces a wave of new and intriguing codenames, from Harmony and Thor to more evocative labels like Whisper, Moonstone, and Solar.

Among the most notable are DeepNow, a successor to Google Now with its counterpart NowBoost, and SuperGlue, which may replace Glue – NavBoost ’s equivalent for universal search.

Unlike most websites, which undergo major overhauls every three to five years, Google evolves continuously.

There’s no big-bang “new version” – just a steady flow of micro‑changes that move from experiment to launch to full integration.

This explains the experiment list’s layered nature: months-old tests appear alongside brand-new trials, some already in their 15th iteration (e.g., MagiCotRev15Launch).

This incremental approach reduces risk – failed experiments impact only a small number of users – while enabling an innovation pace traditional redesigns can’t match.

The range of domains covered is striking:

AI (including multiple Magi and AIM – or AI Mode – variants).
Shopping (with over 50 dedicated experiments).
Verticals like sports, finance, weather, travel, and more.

A clear pattern emerges – each vertical is assigned its own “domain,” such as ShoppingOverlappingDomain, TravelOverlappingDomain, or SportsOverlappingDomain.

These overlapping domains point to a sophisticated architecture where each product team operates within its own experimental space, enabling parallel testing without conflict.

See the full list of experiments here .

Entities everywhere

Entities play a pivotal role across Google’s entire ecosystem – a point explored in depth during a talk titled “ Entities Everywhere ,” delivered earlier this year in Marseille by Damien Andell and Sylvain Deauré of 1492.vision.

The presentation examined how the Knowledge Graph underpins services from Search to Discover, YouTube, Maps, and beyond.

The Knowledge Graph: Google’s central nervous system

Their research shows that the Knowledge Graph is far more than the side-panel assistant most users see.

It functions as the central nervous system of Google’s ecosystem – powering Search, Discover, YouTube, Maps, Assistant, Gemini, and AI Overviews .

Google treats data reliability as a core priority.

At the heart of the Knowledge Graph is Livegraph, which assigns a confidence weight to every triple it encounters before determining whether to admit it.

This obsessive focus on verification is reflected in a layered namespace hierarchy:

kc: Data from highly validated corpora (e.g., official ages, government records).
ss: Web-extracted “webfacts,” along with a few ok: shortfacts (less reliable but richer in coverage).
hw: Information manually curated by humans.

This classification is far from cosmetic – it directly influences the confidence assigned to each fact and governs how that fact is used across Google’s services.

Ghost entities and real‑time adaptation

Among the most fascinating discoveries are so-called ghost entities – unanchored items that float in a buffer zone of the Knowledge Graph.

Unlike fully validated entities with stable MIDs, these temporary structures allow Google to react in near real-time to emerging events.

While conventional LLMs remain fixed to their training snapshots, Google can:

Dynamically generate new entities.
Validate them progressively.
Surface them in results as needed.

Supporting this system are SAFT and WebRef, which – as revealed in the 2024 leaks – operate continuously to extract, classify, and link entities, helping Google build a comprehensive semantic representation of the web.

SEO implications: Become a validated entity

For SEO professionals, the takeaway is clear: your brand needs to exist as a validated entity within Google’s ever-expanding Knowledge Graph.

The 2024 leak revealed that Google vectors entire sites, calculating thematic-coherence signals – such as siteFocusScore and NSR – that penalize scattered or unfocused content.

Chrome data continually feeds into the Knowledge Graph, identifying visited entities, updating trust signals, and tracking emerging trends.

In this new reality, visibility depends less on content volume and more on whether your site represents an entity that is triangulated by multiple sources and deeply embedded in a coherent thematic graph.

You can learn more in “ Entities Everywhere: The Knowledge Graph, the Invisible Architecture of the Google Empire ” by 1492.vision.

Inside Google’s AI Mode: 90 projects and a constellation of agents

A recent discovery provided access to what appears to be an internal Google debug menu – visible only on-corp or via VPN.

While Tom Critchlow had surfaced an earlier version in March, this newer build, dated May 28, 2025, reveals nearly 90 projects in development – an increase of over 40 from the previous list.

A constellation of ultra‑specialized agents

What stands out immediately is Google’s multi-agent strategy. Instead of building a single all-purpose assistant, the company is developing a constellation of ultra-specialized agents:

MedExplainer for health.
Travel Agent and Flight Deals for trips.
Neural Chef, Food Analyzer, and Smart Recipe for cooking.
News Digest and Daily Brief for news.
Shopping AI Studio for commerce.
And so on.

Project Magi: The backbone of AI Mode

Many of these experiments fall under Project Magi, Google’s internal name for AI Mode, with more than 50 active tests. The rollout appears highly structured:

MagiModelLayerDomain : The core infrastructure.
MagitV2p5Launch : Aligns with Gemini 2.5.
SuperglueMagiAlignment : Mirrors the Glue system that tracks user interactions.

Perhaps most striking is MagitCotRev15Launch, already on its 15th iteration.

It implements a Chain-of-Thought technique, in which the AI reasons through five stages:

Reflect → Research → Read → Synthesize → Polish.

AIM (AI  Mode) and the new UI

The AIM project focuses on user interfaces with multiple entry points:

AimLhsOverlay : An AI sidebar.
SbnAimEntrypoints : Repurposing the “I’m feeling lucky” button as an AI gateway.
Even the Google logo itself becomes interactive.

Meanwhile, Stateful Journey and Context Bridge confirm the LLM revolution – Google is moving from isolated queries to full conversational sessions.

Link to the full list: https://i-l-i.com/google-ai-mode-debug-menu.html

SEO takeaways

Hyper‑specialization is crucial-content must match expert‑level agents.
Multi‑modality is no longer optional; text, images, video, and structured data all feed these agents.
Personalization reaches unprecedented depth, driven by session‑level context rather than single queries.

You can learn more in “ AI at the Heart of Google’s Strategy: Behind the Scenes Revealed ” by RESONEO.

The profiling engine: Smile, you’re being embedded!

Our investigation reveals a hidden layer of Google’s infrastructure – one that transforms every digital interaction into a mathematical embedding: a vector that encodes the essence of your online identity.

At the center of this profiling system is Nephesh, Google’s universal user-embedding foundation.

Nephesh generates vector representations of your preferences and behaviors across all Google products.

As the 2024 leaks showed, these embeddings:

Feed signals that assess whether you fit a “typical” or “atypical” profile.
Estimate how likely you are to engage with specific content – based on the alignment between your interests and the vectorized features of that content.

Picasso and VanGogh: Dual embeddings for Google Discover

For Discover, Google deploys a two‑part embedding system named (with pseudonyms) Picasso and VanGogh:

Picasso : Your long‑term memory, patiently analyzing months of interactions to build a persistent profile. It uses two time windows: STAT (recent interests) and LTAT (long‑term passions).
VanGogh : Runs on‑device, capturing real‑time signals-device state, latest queries, even how far you scroll.

These twin systems coordinate to balance your immediate needs with your deeper interests.

A constellation of specialized embeddings

Beyond Picasso‑VanGogh, Google maintains a constellation of specialized embeddings:

Vertical embeddings (i.e., podcasts, video, shopping, travel).
Temporal embeddings (real‑time, short‑term, permanent).
Contextual embeddings that adapt to situational cues.

Google’s HULK system takes behavioral analysis to the extreme.

It detects whether you’re IN_VEHICLE, ON_BICYCLE, ON_STAIRS, IN_ELEVATOR, or even SLEEPING – using these signals to interpret and anticipate user context in real time.

It also identifies frequently visited places – such as SEMANTIC_HOME and SEMANTIC_WORK – and uses that data to predict future destinations and personalize results accordingly.

You can learn more in “ Smile, you’re being embedded! ” by 1492.vision.

Query understanding: Query expansion and real‑time scoring revealed

Another notable breakthrough concerns Google’s query‑expansion engine and a mysterious real‑time scoring layer.

Through a method we’ll keep confidential , we captured how your queries are transformed:

For instance, in “cycling tour france,” “cycling tour” instantly becomes the consolidated bigram “cyclingtour” and fans out to “bicycle,” “bike,” and “trips.”
Special markers, such as iv;p and iv;d , appear.
- iv;p for in‑verbatim exact matches.
- iv;d for linguistic derivations.

Geographic intelligence

For a query like “nail salon fort lauderdale 17th street,” the system:

Maps geo‑categories ( geo:ypcat:manicuring ) and zone codes ( geo;88d850000000000 ).
Expands address variations.
Translates certain terms on the fly when your location suggests local intent-even if that’s not your default language.

These findings confirm that the 2024‑leaked architecture.

GWS → Superroot → Query Understanding Service (QUS) → QBST is still live.
Ongoing experiments such as GwsLensMultimodalUnderstandingInQusUpstream and QusPreFollowM1InQResS run upstream.

Real‑time term scoring

The same leak exposes a scoring grid where each term gets 0–10 points per URL:

Stop‑words are ignored.
Title terms earn bonuses.
Named entities consistently hit maximum scores.
Crucially, scoring is pairwise. The same term can receive different scores for the same URL depending on query context.

This aligns with Google’s documented Salient Terms process – context‑weighted metrics such as virtualTf, idf, and salience refine the lexicon.

These lexical scores don’t decide final ranking.

NavBoost, freshness, and other factors dominate-but they illuminate how queries are interpreted and weighted in real time.

You can learn more in “ Uncovering Google’s Query Expansion System and a Mysterious Scoring Layer ” by RESONEO.

The information presented comes exclusively from publicly available sources obtained without bypassing access controls or intrusion. It is published for informational purposes only.

Boost Your Brand: Top Social Media Advertising Techniques for Carrollton Businesses

Tue, 22 Jul 2025 03:16:56 GMT

In today's digital age, social media has become an indispensable tool for businesses aiming to enhance their online presence. For Carrollton businesses, utilizing effective social media advertising techniques can be the key to capturing the local market's attention. At Hometown Beat , we understand the unique dynamics of Carrollton's vibrant market, offering tailored solutions that help businesses thrive in the digital landscape.

Understanding Your Audience

Before diving into social media advertising, it's crucial to identify and understand your target audience. Knowing their demographics, interests, and online behavior allows you to create content that resonates. Tools like Facebook Audience Insights provide valuable data to help refine your audience targeting strategies.

Building Compelling Content

Content is king in the realm of social media advertising. It's essential to create content that not only captures attention but also engages and retains your audience. Here are some tips to enhance your content:

Visual Appeal: Use high-quality images and videos that relate to your brand.

Storytelling: Craft stories that connect emotionally with your audience.

Consistency: Maintain a consistent voice and style across all posts.

Interactive Content: Encourage engagement with polls, quizzes, and live sessions.

Utilizing Platform-Specific Strategies

Different social media platforms offer unique advertising opportunities. Tailoring your strategy to fit each platform's strengths can significantly boost your campaign's effectiveness.

Facebook Advertising

Facebook remains a powerful platform for reaching a broad audience. Utilize its advanced targeting options to zero in on your desired market segment. Experiment with carousel ads and video ads to showcase your products or services creatively.

Instagram Advertising

Instagram's visually-driven platform is ideal for businesses with striking visuals. Utilize Instagram Stories and Reels to connect with younger audiences. Engaging influencers can also amplify your reach and credibility.

Leveraging Data and Analytics

Data-driven decision-making is the cornerstone of successful social media advertising. Regularly analyze metrics like engagement rates, click-through rates, and conversion rates to assess your campaigns' performance. Use tools like Google Analytics to gather insights and refine your strategies accordingly.

A/B Testing

Testing different ad variations can reveal what resonates best with your audience. Experiment with various creatives, headlines, and calls to action (CTAs) to optimize your ads for maximum impact.

Localizing Your Advertising Efforts

For Carrollton businesses, tailoring your social media advertising to reflect local culture and events can foster a stronger connection with the community. Highlighting local stories or partnering with local influencers can enhance your brand's relatability and appeal.

At Hometown Beat , we specialize in crafting nuanced social media strategies that cater to the unique rhythm of each business we serve. Whether you're new to social media advertising or looking to elevate your current efforts, our free marketing audit can provide the insights needed to maximize your digital potential.

By leveraging these cutting-edge techniques, Carrollton businesses can not only boost their brand awareness but also drive meaningful engagement and sales. Embrace the power of social media advertising today and watch your business flourish in the digital age.

An AI-assisted content process that outperforms human-only copy

Mon, 21 Jul 2025 14:00:00 GMT

This article was co-authored by Chelsey Church .

When AI-generated content became more accessible, questions quickly followed:

Is this the future of content marketing?
Can AI match the quality of a tenured writer?
Will it impact long-term performance?
Can it be implemented at scale?

We needed to find out.

By building a process that balances speed and structure without sacrificing quality, we’ve helped clients produce foundational content at a lower cost, in greater volume, and with faster turnaround – all while improving performance.

This approach leverages the strengths of AI.

Still, it’s important to note that AI-assisted content creation is an involved and nuanced effort, which we’ll break down in more detail below.

Six months in, the results are promising: clients who adopted this AI-assisted process saw 36% year-over-year growth, compared to 11% for those relying solely on human-generated content.

While not conclusive across every vertical, it’s a strong signal that, when applied thoughtfully, AI-assisted content can drive measurable results.

The data: How we got there

We reached this conclusion by tagging each client in our executive dashboard based on their budget bracket and whether they were receiving AI-assisted or human-only copy.

After grouping clients by similar budgets and content creation styles, we compared their year-over-year performance data.

A few important context notes about the dataset:

These are generally smaller accounts for newer businesses, as this is the segment that needs to produce content quickly and in quantity without sacrificing quality.
The content was less blog-focused and more service- and product-focused.
Because AI Overviews caused fluctuations in click metrics this year, growth numbers were lower than expected in both segments.

We’re not suggesting that AI content is universally better than human content.

What matters is understanding why – and when – our AI-assisted approach delivered stronger results.

In reviewing the data, we identified specific scenarios where AI-assisted content was especially effective:

Businesses without an existing online footprint that need to produce content faster and in greater quantities than human writing can allow.
Smaller and/or newer businesses with more runway to grow than mature, established companies.
Service-based, rather than ecommerce, businesses.

The process: Building content that still feels human

AI was always meant to be a collaborative tool, not a shortcut.

That’s why our process never involves dropping a single prompt into ChatGPT and copying and pasting the output.

That approach doesn’t deliver strong results, and we’ve even worked with businesses trying to recover after being deindexed by Google for using it.

Instead, our methodology centers on human intervention.

We start with strategic prompts, let AI handle the heavy lifting, then refine the output, polishing it to remove robotic language , improve performance, and ensure it aligns with the brand.

Here’s what that process looks like.

1. Train the AI to think like the client

Before generating any content, we define the style and tone.

This starts with creating customized GPTs for each client, training the model to recognize the client’s voice and brand language without needing constant reminders.

Alternatively, voice can be manually primed throughout the workflow if necessary.

In both cases, success comes from providing clear expectations and samples of existing client content.

This can take a few iterations, but the payoff is always worth it: Content that aligns perfectly with the brand and naturally fits the website.

2. Start with strategy

Every piece of content begins with an idea, but getting from concept to structure can be a hurdle.

An immediate advantage our writers have found by using AI is its ability to break through writer’s block.

We establish a foundational structure by prompting ChatGPT to generate a five-point outline based on a blog title or webpage topic.

This streamlines the writing process and ensures clarity, consistency, and a faster path to execution.

Dig deeper: The art of AI-enhanced content: 8 ways to keep human creativity front and center

3. Become a prompting expert

Get strategic when prompting, otherwise you’re wasting AI potential. You need to think about:

Who you want to talk to (demographic/persona).
Your constraints (business offerings/compliance).
What you are trying to accomplish (user intent).

Asking these questions early helps generate responses that are stronger, more customized, and aligned with your goals.

Prompt engineering involves:

Understanding the goals of your target audience.
Making changes based on the review of AI generator outputs.
Learning as you go and making improvements over time.

Strong output begins with stronger input.

A clear, detailed prompting strategy is essential to creating useful and adaptable AI-generated content.

Marketers need greater insights into conversational ways to prime the tool to get results that align with their end goal.

4. Generate your content in chunks

The golden rule for creating AI-assisted copy that performs well: We don’t ask the tool to write a 1,000-word blog in a single sentence prompt.

Instead, we guide ChatGPT through each section based on the content outline, moving through every paragraph one prompt at a time.

This is when you give general guidelines, word count parameters, and keyword suggestions.

Each section gets its own prompt. For example:

Write me a 100-word introduction on (the outline). Consider the target audience and keep the bottom line of the article in mind.
Write me a 150-word paragraph that focuses on (topic from outline). Sprinkle (keywords) throughout and remember to follow (style and tone).
Write me a 100-word conclusion, tying up loose ends on the sections curated above.

This allows us to be consistent and tight throughout the content.

5. Polish and optimize

Once we have all sections completed, we holistically polish the content. We review each section and read the work from top to bottom. This is the heaviest human intervention:

Remove awkward wording.
Fix strange transitions.
Replace repetitive phrasing.
Reformat and optimize for search.
Check for consistent style and tone.

AI-generated content almost always reads stale, robotic, and monotonous without human refinement .

The editorial step transforms a piece of content that signals “this was written by AI” into content that feels carefully curated and brand-aligned.

6. Fact check, edit, and complete

With structure in place and voice refined, the final editing phase begins.

This includes:

Fact-checking for accuracy.
Verifying that any sourced links are credible and current.
Incorporating internal links where relevant.

These final touches reinforce brand authority while strengthening SEO and enhancing the overall user experience.

Dig deeper: 7 reasons why your AI content sucks (and how to fix it)

Use AI, but don’t skip the strategy

The real opportunity with AI lies in how it’s used – not as a replacement for content creation but rather as a productivity tool.

It speeds up the foundational work, fuses ideas together, and gives writers time to focus on what really deserves their brain power: strategy, style and tone, and credibility.

When you combine the efficiency of AI with an experienced human writer, you can still create content that:

Performs well.
Reads naturally.
Meets the quality standards our clients expect.

If you go into it assuming AI can do it all, you may run into trouble.

11 actionable tips for the Google AI Mode era

Mon, 21 Jul 2025 12:00:00 GMT

Optimizing for Google AI Mode (and AI search in general) isn’t just business as usual for SEO professionals.

But it also doesn’t necessitate a deep technical understanding of vector space or dense retrieval models.

Instead, it calls for recognizing and addressing the evolution of both Google Search and user behavior.

This means adapting existing SEO tactics (yes, SEO is still not dead) with updated strategies, fresh applications, evolving approaches, and new ways to measure success.

While these tips will help prepare your strategy for the future, remember the immediate goal is to win today .

Currently, most people aren’t using AI Mode, and AI search still accounts for less than 1% of most brands’ traffic from what I’ve seen from client data.

However, it is crucial to strategically prepare for tomorrow, as AI will undoubtedly expand its role and continue to bring significant new opportunities.

If you want to win today while also preparing for tomorrow, implement these 11 tips.

1. Cultivate deep topical authority

Strive to become a go-to expert in your space by creating a wealth of insightful content that thoroughly explores your main topics and subtopics.

Think of your goal as being the most comprehensive and reliable resource available.

Ensure your pages link to one another naturally, guiding users and AI through your content like a clear, structured map.

Your goal is to have Gemini find your brand for as many topically related queries as possible and to make your message as relevant as possible.

When writing your content, focus on genuinely explaining concepts and their relationships, not just sprinkling in keywords.

The aim is to make your expertise unmistakably clear and valuable to both people and AI systems.

Dig deeper: How AI Mode and AI Overviews work based on patents and why we need new strategic focus on SEO

2. Build your brand through search everywhere optimization

In 2025, much of search is beyond our direct control.

That makes it vital to focus on what you can influence: your brand.

Build a strong brand presence through diverse channels and invest in digital PR to expand your reach.

In addition to building strong topical authority on your site, you also need a search everywhere optimization strategy to make sure your content is discoverable across all the platforms where AI models, like Gemini, collect information.

The ultimate goal is for AI to identify your brand as the most relevant and valuable resource at every stage of its reasoning, effectively answering both explicit and implied user questions.

3. Be user-driven

AI Mode is user-first, so success in this new chapter will require user-centric approaches.

AI Mode answers will be heavily personalized, integrating user behavior and preferences from other Google sources (e.g., Gmail).

AI Mode answers will be contextualized based on user embeddings, which are like digital fingerprints based on things like:

Search and click history.
Content engagement history.
Location.
Demographics.

Google’s longstanding recommendation to do what’s best for your users has never been more critical.

Dig deeper: The next wave of search: AI Mode, deep research and beyond

4. Create unique content

Standing out has never been more crucial. Simply replicating existing content from competitors won’t work.

You need to create something truly unique and valuable, leaning into the existing E-E-A-T framework . This means:

Leveraging proprietary or custom data.
Integrating exclusive quotes.
Showcasing contextualized subject matter expertise to deliver content that genuinely hasn’t been done a dozen times before.

5. Think beyond rankings and clicks

Success metrics in modern-day search are changing.

Google hasn’t shared data backing up their claim that clicks in an AI Overview SERP are coming from “more engaged audiences” that are “more likely to spend more time onsite.”

Still, the recommendation to “understand the full value of your visits” is valid, nonetheless.

Make sure you focus on lower funnel KPIs and how those are trending.

Rather than just traffic volume, this means evaluating metrics like:

Transactions.
Leads generated.
Customer lifetime value.

Understanding the qualitative impact of AI visits will be key to understanding and proving ROI.

Dig deeper: 12 new KPIs for the generative AI search era

6. Audit your content for relevancy and freshness

Relevance is everything in this next era of search.

Continuously audit and refresh your website content for stronger relevance and new insights.

Strategically retire outdated or off-topic content to optimize topical authority and overall site value.

This ensures your content provides the most current and comprehensive answers, aligning with how AI prioritizes information.

By maintaining a highly relevant and fresh content library, you signal expertise and trustworthiness to both users and AI.

7. Think outside the ‘search volume’ box

AI mode captures intent that the user didn’t explicitly express.

Gemini moves beyond the initial prompt and determines user intent either:
- Directly from the user’s explicit query.
- By inferring and synthesizing it from their history and context.

While we aren’t in a position to completely eliminate search volume yet, conversational AI search will eventually render keyword search volumes less relevant and less accurate.

Instead of focusing solely on keywords and search volumes, take the time to ensure:

Your content is semantically rich.
Explores related concepts and diverse user intents.
Provides a deep and authoritative understanding.

Dig deeper: Chunks, passages and micro-answer engine optimization wins in Google AI Mode

8. Start thinking about agent optimization

It will take time, but connecting with audiences via AI agents will be crucial as users will ultimately rely on agents rather than websites for their needs.

Agents will not only synthesize and personalize answers for people, but they’ll complete actions, too.

Grabbing tickets to a Padres game.
Signing up for a new yoga studio.
Purchasing a new set of earbuds.

Agents will become personal assistants, and marketers need to start thinking about how they optimize content and experiences for agents.

9. Move beyond text-based content

AI Mode’s ability to synthesize information extends far beyond just text, actively incorporating imagery, audio, and video into its responses.

To maximize visibility and relevance, strategically diversify your content portfolio.

This means:

Optimizing images with descriptive alt text and contextually relevant captions.
Creating engaging video content with clear transcripts.
Even exploring audio formats like podcasts, if you haven’t already.

Ensuring these assets are discoverable and clearly aligned with related topics will be crucial, as AI aims to provide comprehensive, multi-faceted answers.

If your content strategy remains solely text-focused, you risk being overlooked in this richer, more dynamic information landscape.

Dig deeper: How multimodal discovery is redefining SEO in the AI era

10. Invest in CRO and UX

With AI search reducing organic site traffic, every visitor becomes that much more valuable.

CRO focuses on refining your website to make it easier for users to take desired actions, such as:

Completing a purchase.
Submitting a form.

An optimized user experience ensures your site is intuitive, engaging, and easy to navigate.

Make sure you have clear calls to action, fast load times, and an overall frustration-free journey, so those who do visit can easily convert.

Dig deeper: SEO in an AI-powered world: What changed in just a year

11. Put poor collaboration to bed

Winning in 2025 and beyond means there’s zero tolerance when it comes to working in silos.

Knowledge sharing and collaboration is the only path forward.

SEO, content, social media, CRO, user experience, engineering, digital PR, brand, product, and data and analytics will all have to work together to:

Win AI visibility.
Keep up with the shifts.
React to what’s working or not.

AI search is booming, but SEO is still not dead

Fri, 18 Jul 2025 13:00:00 GMT

The explosive rise of ChatGPT and other generative AI tools has fundamentally changed how we search for information online.

These tools offer a faster, more streamlined way to access knowledge and content, often outperforming the traditional search engines we’ve come to know and rely on for decades.

Large language models (LLMs) aren’t without flaws. But their ability to support rapid learning and problem-solving through conversational search has made a significant impact.

They’ve also opened the door to new use cases – like coding help, language learning, tutoring, and even offering a sense of companionship.

Together, these capabilities are reshaping how many people approach searching, learning, and finding information.

For many users, LLMs increasingly feel like a more effective starting point than traditional search engines – especially when the task calls for clarity, context, or a conversational experience.

When LLMs become the starting point, what happens to SEO?

A recent study by Dr. Kevin Matthe Caramancion confirms this shift. Users generally prefer LLMs over traditional search engines for tasks requiring:

Nuanced understanding.
Explanation.
Or personalized, conversational responses.

Search engines remain favored for straightforward, fact-based queries.

This change in search behavior has triggered a wave of media hype and a sense of urgency across the digital marketing industry.

The idea that LLMs could eventually replace Google – or kill traditional search and, with it, SEO altogether – has fueled a media frenzy and a speculative gold rush, especially among those looking for an edge in AI search visibility or an opportunity to capitalize on these new trends.

New AI search tools and startups are emerging daily, fueled by the trendy narrative that traditional SEO is now outdated, and that only novel, shiny approaches like GEO (generative engine optimization) or AEO (answer engine optimization) can save the day.

Learning to optimize for AI search is unquestionably important for business owners, marketers, and SEO professionals.

However, the narrative that this new service offering somehow implies that SEO is no longer important is some of the most inaccurate and irresponsible framing I’ve seen in my 15-plus years in the SEO industry.

Optimizing for AI search is essential. It requires new and evolved skills, tracking and reporting, and updated knowledge, but all of this does not imply that SEO is dead.

Dig deeper: As Google shifts to AI search, legacy SEO faces a new reality

Always question where your marketing advice comes from – especially with the rise of AI

To start, it’s important to question the source of some of these big claims.

Ironically, many of the months-old, faceless entities peddling grandiose solutions to AI search appear to be AI-generated themselves.

They use AI logos and AI-automated marketing campaigns to offer big promises, but with little evidence that proves the “company” actually has any real experience earning visibility in AI answers.

Hundreds of self-proclaimed “GEO experts” have suddenly materialized, each professing to possess the secret key to unlocking visibility in a marketing domain that has existed for less than a couple of years.

They claim to be experts at optimizing technology that evolves and improves daily, through reinforcement training, model updates, and new features and integrations.

This trend isn’t surprising, given the broader, pervasive hype around AI.

That hype actively encourages the creation of AI solutions designed to replace traditional businesses, displace teams, and automate their workflows.

These sales pitches can bamboozle companies eager to jump on the AI bandwagon, without fully grasping the implications or the risks of what’s being sold.

We are, undeniably, in a cycle propelled more by hype and urgency than by genuine substance.

Many are capitalizing on the fear, uncertainty, and doubt that have taken the marketing world by storm with the rise of AI.

This alarmist messaging has been especially effective at the executive level.

Leadership teams are rightfully trying to understand how AI search will impact their brands and where their content fits into this new landscape.

This raises more fundamental and existential questions for existing SEO teams.

If users begin to favor direct answers from LLMs over browsing search results, what does that mean for traditional search engine optimization?

The reality is a bit more nuanced than the alarmist messaging would have you believe.

Yes, AI search is creating a massive shift in our industry. And yes, optimizing for answer engines (AEO) requires a new set of tools, skills, and considerations.

But none of this means SEO is dead – or that the approaches SEOs have relied on for decades are suddenly irrelevant.

The opposite is true: SEO teams approaching their work the right way are at a major advantage for encouraging content and brands to appear in AI search results.

Few professionals are more qualified to help lead clients into the future of AI search than those with experience earning visibility in search engines.

Dig deeper: How AI is reshaping SEO: Challenges, opportunities, and brand strategies for 2025

Why SEO teams are the most qualified to help companies with AI search

As we learn more about how AI search works and how LLMs retrieve and synthesize information, it becomes clear that many of the same tactics that generate AI search visibility turn out to be common SEO approaches, only now wearing a shiny new “AI” hat.

If anyone is uniquely skilled and seasoned in navigating daily changes, it is unquestionably SEO professionals.

Here are some of the most commonly cited tactics I’ve seen recommended as magic bullets for AI search visibility, and why they are really just standard SEO approaches that have been updated for AI search:

Focusing on conversational, long-tail searches generated during the query fan-out process

Sure, many new tools and methods exist to either directly collect and/or emulate the new query fan-out process used by LLMs, particularly Google’s AI Mode.

Some of these tools even pull the keywords directly from Google Chrome Dev Tools during ChatGPT searches, which provide incredible insights.

And it’s true that query fan-out uses new technology to generate relevant questions based on intent, personalization, localization, and more, as explained in great detail in this article by Mike King.

But fundamentally, SEOs have been doing this type of query research for years.

Didn’t we all obsess over conversational search with the rise of voice search around 2015?
Didn’t we start to include Speakable markup in our conversational content for this same reason, around 2018?
Haven’t we used long-tail keyword research generation tools, like Answer the Public around 2016, or Keyword Shitter and AlsoAsked in 2019?

The process, tools, technical considerations, and methodologies evolve (like everything in SEO).

Still, the fundamental approach to researching, conceptualizing, and optimizing for fan-out queries has been core to the work of SEOs for decades.

Multi-modal search

One important aspect of AI search is that LLMs can process and understand information from images, videos, and audio, not just text, meaning that AI search is “multi-modal.”

But, in the words of Barry Schwartz, this is certainly “not new.”

For years, repurposing content into different formats and earning visibility and awareness through different modes of discovery have been essential SEO approaches.

Ross Simmonds presented on this exact topic at MozCon 2019, followed by an excellent Whiteboard Friday episode .

SEO has been nudging us toward:

Optimizing images.
Creating video transcripts.
Ensuring audio content is accessible.

Think about how we’ve long optimized images for Google Image Search and Google Lens – or how YouTube videos have consistently appeared in standard search results.

Google began indexing podcast content in 2019, turning podcasts into a valuable source of organic discovery.

Many might argue that this work falls more into “social media” territory than SEO.

The truth is, SEO and social teams should have been working together all along.

Our agency’s social media team uses social listening tools to help inform our SEO department about:

What social users are saying about our clients.
What topics are trending across different social platforms.

Our SEO team also provides our social team with insights on:

Which content generates the most engagement across social platforms.
What topics are trending in search.
Using social media to support and amplify our content for better visibility in Search and Discover.

We both use tools like BuzzSumo and SparkToro to understand where our audiences spend most of their time and which content resonates most with them.

Digital PR

Suddenly, “ brand mentions ” and “entity authority” are buzzwords for AI search, given that LLMs prioritize information from credible, well-referenced sources.

This sounds remarkably like – digital PR .

Looking at the most heavily cited domains and pages for nearly any brand in AI search reveals the importance of digital PR in earning AI visibility.

For example, the following screenshot from Profound shows the most frequently cited domains across seven large language models for the brand “Canva.”

For a company like Canva, securing positive brand mentions on trusted third-party review sites and directories is clearly vital for AI search visibility.

Yet, a version of this truth has been fundamental to SEO at least since the inception of PageRank .

Links, mentions, co-citations, and overall brand perception have always been table stakes for healthy SEO performance.

Some SEO teams focus more heavily on digital PR and link building than others, with some partnering directly with external PR teams or agencies.

Earning links, mentions, and visibility on trustworthy third-party websites has been a core part of the SEO conversation for decades.

It’s also where the well-known – if sometimes cringeworthy – subcategory of “ off-page ” SEO first originated.

Content optimization

This one’s almost comical.

Some of the “new” advice I’ve seen for LLM-friendly content?

Write clearly.
Directly answer questions.
Use FAQs and Q&As.
Use structured headings.
Break content into readable chunks and passages.
Focus on entities.
Provide scannable bullet points.
Cite your sources.

Sound familiar?

Well, yeah. It’s an updated version of the same advice SEOs have given for effective on-page optimization for decades.

The content that performs best in AI Overviews or chatbot responses is often precisely the content that was already optimized for:

This recent commentary from SEO expert Dawn Anderson is fitting:

Cindy Krum presented about “ Fraggles ” at MozCon in 2019 – a term she coined by combining “fragment” and “handle.”

Her theory, which predated AI search by years, explained how Google was already indexing and leveraging “fragments” or “chunks” of content from pages and using “handles” to link directly to those specific sections.

This idea of breaking down long pages into smaller, indexable units (passages, anyone?) to better serve user intent in formats like featured snippets is, in many ways, the foundation of how AI models now synthesize answers.

The idea that LLMs “passage-rank” or extract precise answers isn’t entirely groundbreaking or unique to generative AI.

It’s a scaled-up, evolved, and more sophisticated version of the content segmentation and retrieval Google was already pursuing years ago.

Google even confirms this in “ Your RAGs powered by Google Search technology, Part 2 .” It explains how modern Retrieval-Augmented Generation (RAG) systems, like Vertex AI Search, use technologies originally developed for Google Search – specifically, breaking documents into smaller, indexable segments (passages) and re-ranking them based on relevance.

This process, involving deep re-ranking and smart content extraction, ensures LLMs receive only the most relevant, concise passages, closely mirroring how Google Search has historically extracted featured snippets and indexed passages to serve precise user intents.

Note: None of this is meant to discredit or diminish the incredible work that technical SEOs like Mike King, Dan Petrovic, Andrea Volpini, Dan Hinkley, Ryan Jones, Kevin Indig, Gianluca Fiorelli, and others are doing to unpack how LLMs actually work.

Their efforts to explore topics like passage indexing, chunking, NLP, query fan-out, Knowledge Graphs, and other core AI concepts are invaluable.

Their deep dives – through patent analysis, real-world testing, and detailed breakdowns of how AI systems interpret and synthesize information – are crucial contributions that continue to push the entire industry forward.

Cross-platform visibility

One important aspect of AI search is that large language models appear to heavily reference information found across:

Multiple platforms.
Data sources.
Forums.
User-generated content sites.

AI search doesn’t just draw from one source.

LLMs pull in information from all over the web, including:

Forums.
Social media posts.
Cloud docs.
Random Q&A sites.

Tools like ClickUp’s Connected Search or Microsoft’s Azure AI search are good examples.

They scan across apps like Google Drive, Dropbox, and internal company docs to find exactly what is needed to answer the user’s query, no matter how it was phrased or where it was saved.

AI-powered systems are also getting better at handling messy, mixed types of data (like PDF reports, Reddit threads, and blog posts), thanks to advances in natural language processing.

LLM-powered tools act more like smart aggregators, pulling answers from across platforms instead of relying on any single site or search engine.

While this evolution has created some new implications for SEO and AI search professionals, it’s not too dissimilar from the work we’ve already done to help our clients appear in all the places their audience might discover them.

For example:

Ecommerce SEOs have worked on optimizations for sites like Amazon, eBay, Etsy, and Walmart for years, not to mention product feed and Merchant Center optimizations.
News-focused SEOs have optimized Google Publisher Center, RSS, and “follow” feeds, and other news aggregator optimizations for decades.
Many SEOs have also driven brand awareness, traffic, and sales through TikTok, YouTube, Instagram Reels, and other video sites for many years.

Monitoring and earning mentions, links, and a positive reputation on Reddit and other niche forums has been a core component of SEO for at least 15 years. (Earning links on Reddit and other forums was actually a big part of how I spent my first year doing SEO professionally, in 2010.)

Let’s not forget:

The long-standing work in app store optimization ( ASO ) for both the Apple App Store and Google Play Store, aimed at keeping mobile apps discoverable.
The ongoing efforts to rank in Google Images and in dedicated local directories like Yelp and TripAdvisor.

Focus on local search and NAP consistency

Large language models – particularly, Google’s AI Mode – heavily cite and reference local business information pulled from Google Maps and other business directories.

While AI search and the shift toward Google’s AI Mode may make local SEO more important than ever, many of its core tactics have been essential for decades, such as:

Ensuring NAP consistency.
Optimizing Google Business Profiles.
Managing reviews and images.
Other local SEO practices.

Directory submissions, anyone? It might feel like ancient SEO history, but somehow, it’s still just as relevant.

Use structured data

Many AI search-focused guidance recommends using clear and robust structured data to help LLMs better understand your content.

While the jury is still out on the extent to which LLMs actively use structured data to understand content, there are strong arguments for its future importance, like this article by Mark van Berkel of Schema App.

That said, incorporating clear and robust structured data is by no means a new recommendation for SEO professionals.

Using it – especially in ways that help generate rich results and boost click-through rates – has been an important part of the SEO process for years.

Still, there’s ongoing debate about how much structured data really matters for LLM and AI visibility.

Structured data expert Jono Alderson said:

“It’s not clear if, or how, today’s (or future) AI tools use structured data – whether that’s schema.org, or something else entirely. But what we do know is that as they slurp up our HTML markup, they try to ‘make sense’ of it.”
“So if that markup contains conveniently structured, easily tokenizable, reinforcing descriptions of what we (as marketers and business owners) believe to be the most important attributes and features of that page – in a way which also benefits other systems – then that feels like a good investment to me.”

Jarno van Driel , also a structured data expert, explained:

“The role of on-page structured data hasn’t changed with the arrival of LLMs. It’s still very much about what it has always been about (for Search) and that is, it’s a means for search engines (mostly Google though) to enrich their results in a cost-effective way. But beyond that, on-page structured data really doesn’t achieve all that much.”
“The presence of markup doesn’t achieve anything unless specific functionality has been created for it, so in practice this means there’s little sense in going beyond what Google mentions in its structured data documentation, whereas many seem to be convinced we should use all of schema.org to help the machines understand content.”

Dig deeper: SEO in an AI-powered world: What changed in just a year

Google’s own communications about AI search

Google itself has weighed in with new guidance for appearing in AI search .

While the article mentions some of the new features and inner workings of AI search, its technical and content guidance echoes nearly all the same SEO best practices Google has shared for years.

It’s almost as if the foundations of solid information architecture and valuable content remain the same, regardless of the technological interface.

I don’t think I’m the only SEO professional who read that article and felt that its guidance looked and felt nearly identical to what Google has been saying for years.

AEO is absolutely important, but it’s incremental to good SEO, not a replacement

Now, you might be wondering whether new services like AEO are worth the investment. The answer is a resounding yes.

The main reason is that AI search demands tracking visibility, impressions, perception, and competitive share of voice across new AI-driven platforms, beyond just traditional search engines.

AEO is essential for understanding:

How often a brand is mentioned or cited.
What LLM responses are saying about overall brand perception, sentiment around products and services, and positioning within the competitive landscape.

That’s why it’s even more important to work with an AEO provider who can deliver these insights and reporting across multiple LLMs simultaneously, including ChatGPT, Google AI Overviews and AI Mode, and Perplexity.

AEO services should also provide detailed, actionable recommendations on how to use these insights to boost visibility across relevant AI search platforms.

New tools like Profound, Peec AI, Otterly, WAIKAY, and ZipTie can support this process, along with updated and evolving features within major SEO platforms like Semrush and Ahrefs.

AEO requires that SEO professionals continue learning new skills, such as:

AI and machine learning literacy.
Prompt engineering .
Vector embeddings and cosine similarity analysis.
NLP and semantic search.
Workflow automation.

AI search hasn’t made a dent in Google Search – yet

Despite all the hype around AI search, each new study suggests it has yet to make a meaningful dent in Google’s dominance.

Google processed an astounding 14 billion searches per day in 2024, per Rand Fishkin of SparkToro . This is 373 times more than the estimated daily “search-like” prompts on ChatGPT.

Even if all of ChatGPT’s 1 billion daily messages were search-related, its market share would still be under 1%, while Google holds over 93.57%.

Google’s search volume actually grew by more than 21% in 2024 compared to 2023, based on data from Datos.

While some users may be turning to large language models like ChatGPT to start their queries, it’s clear that Google hasn’t been abandoned, at least not yet.

This perspective is echoed in Glenn Gabe’s recent analysis , which found that AI search was driving less than 1% of traffic to most websites as of June 2025, with many seeing numbers below 0.5%.

Gabe cautions against an overemphasis on AI search at the expense of traditional Google SEO.

Neglecting core search quality can lead to negative consequences from Google’s broad core updates.

For example, churning out lots of low-quality content in an attempt to gain AI search visibility can cause a site to see declines in visibility with future core updates.

And because Google’s AI Mode and AI Overviews draw from Google’s own search index (see the below image from Gabe’s article), core updates can impact a site’s visibility across all search features, including AI search platforms.

It’s not just Google’s AI products that rely on search results.

Search results play a major role in how other large language models, like ChatGPT, Perplexity , and Claude, generate answers for search-enabled queries.

Dig deeper: How to stay grounded (and inspired) as AI changes search and SEO

AI search draws from traditional search signals and may even leverage Google’s search results

One of the most impactful ways AI search builds on existing SEO performance is through retrieval-augmented generation.

RAG enables language models to incorporate real-time, external information, rather than relying solely on pre-trained data, which can quickly become outdated.

When an LLM identifies that current web content could improve a response, it acts as a “retriever,” pulling relevant information from sources like Google or Bing search results.

It’s long been clear that Google uses its own search results for RAG.

However, there’s growing speculation that ChatGPT may now be doing the same, pulling from Google results, not just Bing’s (which OpenAI has previously confirmed in its documentation ).

Alexis Rylko ‘s recent article speculates that OpenAI may have quietly switched from pulling in Bing’s results to strongly leveraging Google’s own search results in its responses.

Some of Alexis’ findings mirror my own discovery from four months ago – specifically, ChatGPT citing URLs that include Google’s unique ?srsltid parameter.

Similarly, my colleague Johnny Herge recently found examples of ChatGPT citing Google Maps URLs in its recommendations for local businesses.

This convergence of AI search and traditional SEO is perhaps the most compelling argument for SEO’s essential role in the age of AI.

If LLMs are actively retrieving and synthesizing information from existing search results, particularly Google’s search results (the search engine most SEOs have focused on for 20+ years), then the content that ranks prominently in traditional Google Search becomes the de facto source material for the LLMs that rely on it.

This also reinforces my belief that many of the newer spammy techniques aimed at gaining AI search visibility are dangerous.

Tactics like adding prompt information using white text on a white background may offer a temporary boost, but they’re essentially recycled 20-year-old SEO spam strategies.

Just because something works in AI search today doesn’t mean it will continue to as LLMs evolve and release improved models.

We are in an AI-driven hype cycle, and it’s not the first hype cycle in SEO history

It’s important to pay attention to how AI is impacting search and how users are increasingly using it.

That said, anyone working in SEO for more than a decade will recognize this current “AI apocalypse” narrative as a bit of an SEO throwback, echoing countless past declarations of SEO’s impending doom.

( Spoiler alert : SEO wasn’t doomed; it’s more alive now than ever.)

Below are some other examples of technological developments that created significant hype about their existential threat to search over the years.

Google+ and Facebook (early 2010s)

The early 2010s brought an industry-wide obsession with “social signals,” fueled by the rise of platforms like Google+ (launched in 2011) and the dominance of Facebook.

Many believed that social engagement would soon render SEO irrelevant.

Despite the hype, Google+ ultimately failed to gain significant traction.

While social media remains vital for branding, its direct impact on organic search usage was consistently shown to be minimal, never fully replacing traditional SEO.

Mobile-first indexing (mid-2010s, officially rolled out 2018)

Mobile-first indexing created a panicked frenzy to adapt to Google’s shift to prioritizing mobile versions of websites for ranking, with dire warnings of being “left behind” or “invisible” if not immediately compliant.

The buzz began years before the official rollout in 2018.

SEO teams evolved their services to ensure client websites were compliant with mobile-first indexing and retained visibility in an increasingly mobile world.

It ended up being much more gradual than expected, as many major brands took years to comply with the new guidelines, without losing much visibility in the meantime.

In retrospect, mobile-first indexing represented a needed evolution that solidified best practices rather than creating a sudden, life-or-death ranking factor.

The real impact was much more nuanced and gradual than what was originally expected.

Voice search (mid-to-late 2010s)

This era of SEO was marked by the widespread belief that spoken queries, amplified by voice search devices like Amazon Echo (2014) and Google Home (2016), would soon replace typed searches altogether.

Many predicted that keyword research would become obsolete and content strategies would need to shift entirely to suit conversational AI.

Voice search, however, ended up being a bit of a dud, even evolving into a recurring inside joke within the SEO community.

There was little evidence that consumers actually replaced typed searches with voice, as typed queries continued to grow exponentially.

And while the popular statistic claimed that by 2020, 50% of all searches would be done by voice, that prediction still hasn’t come to pass.

Page speed and Core Web Vitals (late 2010s to early 2020s)

The intense focus on technical performance metrics, particularly with the introduction of Core Web Vitals in 2020-2021, sometimes to the exclusion of content quality and user experience, drove enormous fear of algorithmic penalties.

Core Web Vitals, while important, were later felt to be a bit overblown, especially as they came to be known as a “ tiebreaker ” signal, not the earth-shattering ranking factor many feared would tank the rankings of non-compliant websites.

TikTok and video (early 2020s)

The pronouncements that short-form video, largely popularized by TikTok, would render traditional text-based search irrelevant caused panic at Google and caused many to suggest that TikTok posed an existential threat to Google’s search business.

But not only did Google respond with its own short-form video product, YouTube Shorts. TikTok’s rise also hasn’t significantly disrupted Google’s dominance in search.

Dig deeper: 6 easy ways to adapt your SEO strategy for stronger AI visibility

The unshakeable core of SEO: Sorry, but SEO is still not dead (yet)

Another year, another declaration that SEO is dead. But the truth reveals another story.

It’s clear that AI search is a powerful, evolving force that should not be ignored.

The rise of LLMs demands new skills, new metrics, and at the very least, an insatiable desire to continue learning and experimenting.

Services like AEO are genuinely important for understanding brand visibility and perception within these new AI platforms.

And yes, for businesses, being at the forefront of optimizing for these emergent platforms is not only advisable but essential.

However, the narrative of traditional SEO’s demise is not just premature; it’s profoundly inaccurate.

As we’ve explored, the very mechanics of AI search – from retrieval-augmented generation pulling from existing search results to its reliance on established signals like clear content optimization, E-E-A-T , structured data, and cross-platform authority – underscore the enduring relevance of traditional SEO approaches.

Also, the content that performs best in AI Overviews or receives citations from ChatGPT is, more often than not, the same high-quality, well-optimized content that excels in traditional Google Search.

This isn’t an existential threat; it’s an evolution.

And if any profession is uniquely skilled and seasoned in navigating daily evolution, it is unquestionably SEO professionals.

For decades, we’ve adapted to algorithm updates , new search features, shifting user behavior, evolving spam and content policies, and a steady stream of so-called “SEO killers” – none of which have actually succeeded.

SEOs have learned to optimize for mobile, voice, video, images, local searches, and app stores, mastering a myriad of platforms beyond just websites.

AI search is simply the next frontier for SEO professionals to conquer, much like the many previous search iterations before it.

LLM perception match: The hurdle before fanout and why it matters

Wed, 16 Jul 2025 14:00:00 GMT

Before an LLM matches your brand to a query, it builds a persistent perception of who you are, what you offer, and how well you fit the user’s need.

If you’re not perceived as the right match, your brand is quietly filtered out – before fanout, before relevance, before you even enter the race.

This is what I call LLM perception match (LPM) – the new eligibility filter for AI visibility. And it’s already happening inside ChatGPT and, likely, other LLMs.

If your perceived fit doesn’t align with the query’s intent, your content, links, and authority will not matter, and you will not be considered.

What is LLM perception match?

LLM perception match is how language models determine whether your brand even qualifies to be considered for a recommendation before relevance or content matching happens.

They form this judgment from everything they can crawl:

Your site.
Reviews
Forums.
Analyst reports.
Competitor comparisons.
And more.

This perception is persistent and synthesized. If it doesn’t align with the user’s intent, persona, or expectations, your brand is excluded before fanout ever begins.

Simply put, LLM perception match is the gatekeeper. Without it, content quality and SEO don’t matter – you won’t even be in the running.

In every AI visibility audit I’ve done, one pattern is clear: LLM perception match decides whether your brand is in the conversation or invisible.

Dig deeper: AI visibility: An execution problem in the making

LLM perception match vs. fanout

After ingesting and synthesizing everything it can find about your brand, products, services, and technology, an LLM forms a perception of:

Who you are.
What you offer.
Who you’re a fit for.

This is your LLM perception match. The LLM determines whether your brand is a match based on what it perceives.

In contrast, fanout is a technique where a single user query is expanded into multiple related subqueries to gather a broader range of information and deliver a more comprehensive answer.

The marketing goal is to be relevant to as many of these subqueries as possible.

In my AI visibility audits, I’m seeing that if the LLM’s perception of your brand doesn’t align with what it determines the user needs, your brand is filtered out, no matter how well optimized your content is.

Perception trumps relevance. Without a strong LLM perception match, your brand won’t even be considered, even if your content is technically relevant.

This is the critical shift in AI visibility.

LLM perception match acts as the eligibility filter before relevance matching in fanout. If there’s no perception match, you won’t be considered.

Notes :

Whether LPM filtering happens before or as part of fanout doesn’t change the reality. A poor LLM perception match will block your brand from being considered for recommendation.
Google introduced the concept of query fan-out for Gemini, but it’s not unique to Google. Other LLMs are likely doing the same.

Why LLM perception match matters for B2B

Companies with complex B2B sales cycles – million-dollar machines, six-figure software, and high-stakes services – are especially exposed.

These purchases require extensive due diligence, and AI systems can quickly consolidate all the research that took months to do before.

Thus, LLMs can quietly shape early consideration long before a prospect ever talks to your sales team.

ChatGPT , in particular, now functions like a personal procurement advisor.

It instantly generates comparison tables on pricing, buyer remorse, implementation complexity, and feature differences – information that used to require multiple vendor calls and your own conclusions is now curated and consolidated in seconds.

To every CEO, CMO, and COO’s horror, buyers can now ask LLMs detailed questions about your product and technology, only to be told exactly why it may not be the right choice.

Few executives will be happy about what LLMs really have to say. (You’ll see examples below.)

Dig deeper: Optimizing LLMs for B2B SEO: An overview

Visibility gaps may be an operational problem

In many cases, brands and SEO teams will mistakenly assume they have a “relevance” or “fanout” issue when they aren’t being recommended. They’ll be chasing their tail in these cases.

In reality, the LLM may simply not perceive your brand as a good fit. That’s LLM perception match at work – determining whether you’re eligible for recommendation before content relevance even comes into play.

Here’s the kicker – perception is about your company as a whole, not just the content on your pages.

We’ve seen a wide range of factors negatively affect LLM perception match, including:

Difficult return policies.
Technology perceived as outdated.
Website UX (e.g., images too close together).
Low-quality materials in product construction.
Clunky or confusing software interfaces.
Formerly innovative tech, now perceived as lagging.

These factors extend beyond what any SEO team can control.

This is why LLM perception match is the first hurdle in AI visibility.

Ignoring it will quietly erode discoverability and pipeline, especially for B2B brands where trust and fit drive sales cycles.

Fixing it can take many months, and in many situations it will take years.

Let’s look at a few examples from my audits that will take months to years to address.

Examples from AI visibility audits

Here’s what I’ve seen in real AI visibility audits across different industries.

Example 1: Once a technology leader, but the field moved on

ChatGPT described a client’s technology as “once a leader” but highlighted that the field had “moved on.”

Example 2: Integration friction

ChatGPT noted this product worked well within its own ecosystem but the LLM perception is that it creates headaches when connecting to other platforms.

For buyers with hybrid tech stacks, this LLM perception will cause the product to be filtered out early, or given a lower recommendation with caveats, despite strong SEO rankings and AI Overview recognition.

Example 3: Return policy friction

ChatGPT and other LLMs described a retailer’s return policies as restrictive and inconsistently enforced, triggering a low LLM perception match.

They were completely out of the running because the LLM’s bias was to recommend companies with positive customer experiences.

Example 4: Transaction friction

LLMs flagged transaction challenges – shipping delays, unclear returns, disputes – leading them to steer buyers toward in-store options, despite strong SEO rankings.

ChatGPT actually said, “If you still want to proceed… Order in-store if possible — you’ll likely experience fewer surprises.”

Example 5: Innovation leader, but harder to adopt

A client was seen as innovative, but competitors were more appealing for “broader compatibility” and “more intuitive interfaces.”

The LLM would recommend competitors for queries, prioritizing ease of use and broader compatibility with their diverse tech stack.

Example 6: Perceived as overkill for entry buyers

ChatGPT noted this client’s suite felt like “overkill” for organizations wanting to get started with a particular technology, framing it as too complex for practical needs.

This perception limited consideration in early-stage buyer queries despite strong product capabilities.

ChatGPT is your test bed

ChatGPT is your clearest test bed for understanding LLM perceptions of your brand. It surfaces issues candidly, often in ways that catch CEOs, COOs, and CMOs off guard.

The key is to be thorough and methodical, testing across:

Entities – structured concepts about your products, services, and customer use cases, not just keywords.
All relevant LLMs.

The first step in AI visibility is building out these entities, which is the AI-era equivalent of keyword research.

Note: You’ll notice I call out COOs in this article. That’s intentional.

Many of the AI visibility issues I see trace back to operational breakdowns that cause LLMs to form the wrong – or weak – perceptions of your brand. COOs need to hear this.

Where LLM perceptions are headed

Expect the LLM perception match to expand across Gemini, Claude, Perplexity, and Copilot.

If they haven’t surfaced these perceptions yet, it’s likely due to legal caution or ad model priorities.

In my view, ChatGPT’s unfiltered candor is a preview of where LLMs are headed.

If not all, the ones that do move this direction will become the models users trust most for the research phase of the buyer journey.

What it takes to manage LLM perceptions

AI visibility issues often stem from years of inconsistent positioning across distributors, press, analysts, user comments, and legacy content.

As we have seen, the SEO team’s content optimizations alone can’t override entrenched misperceptions in LLM systems.

Managing how LLMs perceive your brand will require:

Operational changes (returns, fulfillment, product design, support).
Narrative changes and consistency across owned and unowned digital properties.
Updating your brand footprint where LLMs crawl – not just your site.
Monitoring how LLMs describe your brand and products.
Internal ownership to maintain and adapt LPM as markets evolve.

Notice the word “manage.”

LLM perception match management isn’t an SEO tweak – it’s an organizational competency most brands have never had to build until now.

Dig deeper: Decoding LLMs: How to be visible in generative AI search results

Why you need to audit LLM perceptions now

Most brands have no idea what LLMs actually believe about them. My clients have been stunned about the things our audits uncover.

The negatives blocking your visibility often aren’t the ones you expect, and they will cause missed opportunities before your funnel even begins.

This is why systematically auditing your LLM perception match is critical to staying discoverable in the AI era.

What’s at stake if you ignore LLM perception match

Your AI visibility is at stake if you ignore LLM perception match.

Worse, if you wait to act, you will be spinning your wheels to figure out why you’re not recommended when LLMs are the start of product discovery.

For B2B brands with complex sales cycles, the risk is even higher.

Missed leads (especially for fringe personas and use cases), shrinking pipelines, and competitors capturing early consideration will quietly happen, and it could take 6-24+ months to recover.

Bottom line

The question isn’t whether LLM perception match affects your brand.

It’s whether you’re ready to fix it before your customers – and competitors – see the gaps. Now or later, you will be addressing LLM perception match.

B2B brands with complex sales cycles must operationalize LPM management now or risk losing pipeline to competitors who show up in AI discovery with positive, matching perceptions where it matters most.

FLUQs: Answer the hidden questions or vanish in AI search by Citation Labs

Wed, 16 Jul 2025 11:00:00 GMT

ChatGPT, Gemini, Perplexity: these are the new operating environments. Your content must be invokable inside them, or no one will see it.

At SMX Advanced , I broke down how to build an AI visibility engine: a system for making your net-new facts reusable by humans and agents across synthesis-first platforms.

It goes beyond publishing to show how teams can deploy structured content that survives LLM compression and shows up for buyers during their purchasing decisions.

It’s what we’re building with clients and inside XOFU, our LLM visibility GPT .

Here’s how it works.

Find the FLUQs (Friction-Inducing Latent Unasked Questions)

Friction-Inducing Latent Unasked Questions are the unasked questions your audience doesn’t know about. But if left unanswered, they can derail the entire buying process.

Costing you existing and future customers.

FLUQs live in the gap between what’s known and what’s required, often right where AI hallucinates or buyers hesitate.

That’s the zone we’re scanning now.

We explored this with a client that’s a prominent competitor in the online education space . They had the standard FAQs: tuition, payment plans, and eligibility.

But we hypothesized that there were numerous unknown unknowns that, when discovered, could negatively impact new students. We believed this would negatively impact existing and future enrollments.

Mid-career students going back to school weren’t asking:

Who watches the kids while I study for the next 18 months?
Who takes on extra shifts at work?
How do I discuss schedule flexibility with my boss?

These aren’t theoretical questions. They’re real decision-blockers that don’t reveal themselves until later in the buying cycle or after the purchase.

And they’re invisible to traditional SEO.

There’s no search volume for “How do I renegotiate domestic labor before grad school?”

That doesn’t mean it’s irrelevant. It means the system doesn’t recognize it yet. You have to surface it.

These are the FLUQs. And by solving them, you give your audience foresight, build trust, and strengthen their buying decision.

That’s the yield.

You’re saving them cognitive, emotional, reputational, and time costs, particularly in last-minute crisis response. And you’re helping them succeed before the failure point shows up.

At least, this was our hypothesis before we ran the survey.

Where FLUQs hide (and how to extract them)

You go where the problems live.

Customer service logs, Reddit threads, support tickets, on-site reviews, even your existing FAQs, you dig anywhere friction shows up and gets repeated.

You also need to examine how AI responds to your ICP’s prompts:

What’s being overgeneralized?
Where are the hallucinations happening?

(This is difficult to do without a framework, which is what we’re building out with XOFU .)

You have to be hungry for the information gaps.

That’s your job now.

You aren’t optimizing content for keywords anymore. This ain’t Kansas. We’re in Milwaukee at a cheese curd museum, mad that we didn’t bring a tote bag to carry 5 pounds of samples.

You’re scanning for information your audience needs but doesn’t know they’re missing

If you’re not finding that, you’re not building visibility. You’re just hoping someone stumbles into your blog post before the LLM does.

And the chances of that happening are growing smaller every day.

There are four questions we ask to identify FLUQs:

What’s not being asked by your ICP that directly impacts their success?
Whose voice or stake is missing across reviews, forums, and existing content?
Which prompts trigger the model to hallucinate or flatten nuance?
What’s missing in the AI-cited resources that show up for your ICP’s bottom-funnel queries?

That last one’s big.

Often, you can pull citations from ChatGPT for your category right now. That becomes your link building list .

That’s where you knock.

Bring those publishers new facts and information.

Get cited.

Maybe you pay. Maybe you guest post.

Whatever it takes, you show up where your ICP’s prompts pull citations .

This is what link building looks like now . We’re beyond PageRank. We’re trying to gain visibility in the synthesis layer.

And if you’re not on the list, you ain’t in the conversation.

Prove FLUQs matter with facts (FRFYs)

Once you’ve spotted a FLUQ, your next move is to test it. Don’t just assume it’s real because it sounds plausible.

Turn it into a fact.

That’s where FRFYs come in: FLUQ Resolution Foresight Yield.

When you resolve a FLUQ, you’re filling a gap and giving your audience foresight. You’re sparing them cognitive, emotional, reputational, and temporal costs.

Especially during a last-minute crisis response.

You’re saving their butts in the future by giving them clarity now.

For our client in online education, we had a hypothesis: prospective students believe that getting admitted means their stakeholders (their partners, bosses, coworkers) will automatically support them. We didn’t know if that was true. So we tested it.

We surveyed 500 students .

We conducted one-on-one interviews with an additional 24 participants. And we found that students who pre-negotiated with their stakeholders had measurably better success rates.

Now we have a fact. A net-new fact.

This is a knowledge fragment that survives synthesis. Something a model can cite. Something a prospective student or AI assistant can reuse.

We’re way beyond the SEO approach of generating summaries and trying to rank. We have to mint new information that’s grounded in data.

That’s what makes it reusable (not just plausible).

Without that, you’re sharing obvious insights and guesses. LLMs may pull that, but they often won’t cite it. So your brand stays invisible.

Structure knowledge that survives AI compression

Now that you’ve got a net-new fact, the question is: how do you make it reusable?

You structure it with EchoBlocks.

You turn it into a fragment that survives compression, synthesis, and being yanked into a Gemini answer box without context. That means you stop thinking in paragraphs and start thinking in what we call EchoBlocks .

EchoBlocks are formats designed for reuse. They’re traceable. They’re concise. They carry causal logic. And they help you know whether the model actually used your information.

My favorite is the causal triplet. Subject, predicate, object.

For example:

Subject: Mid-career students
Predicate: Often disengage
Object: Without pre-enrollment stakeholder negotiation

Then you wrap it in a known format: an FAQ, a checklist, a guide.

This needs to be something LLMs can parse and reuse. The goal is survivability, not elegance. That’s when it becomes usable – when it can show up inside someone else’s system.

Structure is what transforms facts into signals.

Without it, your facts vanish.

Where to publish so AI reuses your content

We think about three surface types: controlled, collaborative, and emergent:

Controlled means you own it. Your glossary. Help docs. Product pages. Anywhere you can add a triplet, a checklist, or a causal chain. That’s where you emit. Structure matters.
Collaborative is where you publish with someone else. Co-branded reports. Guest posts. Even Reddit or LinkedIn, if your ICP is there. You can still structure and EchoBlock it.
Emergent is where it gets harder. It’s ChatGPT. Gemini. Perplexity. You’re showing up in somebody else’s system. These aren’t websites. These are operating environments. Agentic layers.

And your content (brand) has to survive synthesis.

That means your fragment – whatever it is – has to be callable. It has to make sense in someone else’s planner and query.

If your content can’t survive compression, it’s less likely to be reused or cited, and that’s where visibility disappears.

That’s why we EchoBlock and create triplets.

The focus is on getting your content reused in LLMs.

Note : Tracking reuse is challenging as tools and tech are new. But we’re building this out with XOFU. You can drop your URL into the tool and analyze your reuse.

Test if your content survives AI: 5 steps

Do this right now:

1. Find a high-traffic page.

Start with a page that already draws attention. This is your testing ground.

2. Scan for friction-inducing fact gaps.

Use the FLUQs-finder prompting sequence to locate missing but mission-critical facts:

Your proposed prompt structure is deeply practitioner-aware and already aligned with SL11.0 and SL07 protocol logic. Here’s a synthesis-driven refinement for role-coherence and FLUQ-sensitivity:

Refined prompts with emission-ready framing

Input type 1: Known materials

Prompt:
“Given this [FAQ / page], and my ICP is <insert ICP>, what are the latent practitioner-relevant questions they are unlikely to know to ask — but that critically determine their ability to succeed with our solution? Can you group them by role, phase of use, or symbolic misunderstanding?”

Input type 2: Ambient signal

Prompt:
“My ICP is <insert ICP>. Based on this customer review set / forum thread, what FLUQs are likely present? What misunderstandings, fears, or misaligned expectations are they carrying into their attempt to succeed — that our product must account for, even if never voiced?”
Optional add-on:
“Flag any FLUQs likely to generate symbolic drift, role misfires, or narrative friction if not resolved early.”

Drop it into this PARSE GPT .

Sources include:

Reviews and forum threads.
Customer service logs.
Sales and implementation team conversations.

3. Locate and answer one unasked but high-stakes question

Focus on what your ICP doesn’t know they need to ask, especially if it blocks success.

4. Format your answer as a causal triplet, FAQ, or checklist

These structures improve survivability and reuse inside LLM environments.

5. Publish and monitor what fragments get picked up

Watch for reuse in RAG pipelines, overview summaries, or agentic workflows.

The day Google quietly buried SEO

We were in Room B43, just off the main stage at Google I/O.

A small group of us – mostly long-time SEOs – had just watched the keynote where Google rolled out AI Mode (it’s “replacement” for AI Overviews). We were invited to a closed-door session with Danny Sullivan and a search engineer.

It was a weird moment. You could feel it. The tension. The panic behind the questions.

“If I rank #1, why am I still showing up on page 2?”
“What’s the point of optimizing if I just get synthesized into oblivion?”
“Where are my 10 blue links?”

Nobody said that last one out loud, but it hung in the air.

Google’s answer?

Make non-commoditized content . Give us new data. Ground AI Mode in fact.

No mention of attribution. No guarantees of traffic. No way to know if your insights were even being used. Just… keep publishing. Hope for a citation. Expect nothing back.

That was the moment I knew the old playbook was done.

Synthesis is the new front page.

If your content can’t survive that layer, it’s invisible.

Appendix

1. Content Metabolic Efficiency Index (useful content theory)

How AI is reshaping SEO and what’s next by Edna Chavira

Tue, 15 Jul 2025 21:13:00 GMT

AI is fundamentally changing the way people search—and how brands show up.

In How AI is Reshaping SEO: Challenges, Opportunities, and Brand Strategies for 2025 , Search Engine Land and Fractl will share new findings from a fresh survey of hundreds of marketers, revealing how teams are integrating AI across SEO, content, PR, and social—and where the biggest opportunities and risks lie.

You’ll learn:

How consumer search habits are shifting across key demographics
How marketers are using AI in real workflows—and what’s working so far
What brands are doing to balance automation with authenticity and trust
What it means to be included in LLM training sets—and how to boost your AI visibility
How to navigate emerging standards for ethical AI use and content disclosure

If you’re rethinking your SEO and content strategy for 2025, this session is a must. Save your spot here.

7 ways to grow brand mentions, a key metric for AI Overviews visibility

Tue, 15 Jul 2025 14:13:00 GMT

Brand mentions have always mattered, but they’ve become mission-critical for organic visibility in the AI era.

Brand building should now take priority over keyword-based strategies as your top priority in organic discovery.

Why? Because brand mentions from trusted sources are the top factor for visibility in AI Overviews, per a recent Ahrefs study .

There are basic block-and-tackle ways and more complex, strategic ways to build your portfolio of brand mentions.

Along with a recommendation to get on your horse and get moving, I’ll break down the most important SEO initiatives driving growth in brand mentions for our clients.

1. Build your local SEO presence

Presumably, anyone reading this has long ago claimed and optimized their Google Business Profile, whether or not you have a brick-and-mortar presence.

(Although I do find that franchises can forget to set up a profile for each location, consider this your reminder to confirm you’ve claimed them all.)

Otherwise, there are plenty of local platforms to include in your local SEO campaigns, such as:

Local directories.
Chamber of Commerce sites.
Malls or marketplaces with store listings.

All of these are examples of properties where you can build your local portfolio.

Dig deeper: AI and local search: The new rules of visibility and ROI in 2025

2. Claim your turf on high-authority review sites

Most industries have some kind of review site that serves as a trusted resource and buying guide. Think Capterra for SaaS or Angi for home-service professionals.

Research those review sites for your vertical, and proactively claim and optimize your listings.

If you don’t, someone may start one for you – it happens all the time on Yelp, for instance.

3. Leverage potential third-party entry points

Many businesses have ICPs within specific types of organizations.

For instance, let’s say you specialize in throwing the best children’s birthday parties ever.

A brand mention on a local Make-A-Wish chapter would be an incredible source of awareness and bookings.

Consider all the kinds of organizations that would be providing a service to their end users by listing your business.

Then, explore your options for getting listed on their websites.

4. Research and submit to niche directories

Particularly for service-based and B2B SaaS organizations, there are perfectly relevant niche directories that would be happy to include your site.

If you’re a roofer, for instance, don’t settle for a link on Angi or the BBB.

Do your research and submit your site to sites like https://www.roofing-directory.com for inclusion.

Note : Make sure part of your research can determine whether a site is legit, since AI Overviews and other AI search platforms will likely either ignore or ding you for mentions on junk sites.

5. Build your partner network

One of the most effective and under-leveraged marketing strategies is finding and nurturing great partnerships, and this is true for SEO.

Think about all the organizations adjacent to your industry that can offer your services as a value-add (and vice versa) without representing any kind of competition.

These partnerships could be companies that:

Do more or less what you do, but for a different audience segment (i.e., retail vs. B2B office supplies).
Do something different but complementary to what you do (e.g., online event logistics for a company that offers catered in-person experiences).
Do something completely separate from what you do, but for an ICP that could benefit from your product or service (i.e., skating rink rentals, for a consortium of private hockey coaches).

This step requires creativity, but the potential benefits (not just in brand mentions but also in referral business) are substantial.

6. Get strategically aggressive with digital PR

Digital PR is a kind of bootstrapped PR initiative you can run without investing in PR providers (more on them in a second).

The idea is to get placed in third-party publications, whether by getting your experts quoted or linking to a piece of your owned content.

You have several levers for building digital PR momentum, including:

Media sourcing platforms like HARO and Qwoted, which are built to connect journalists with expert sources for quotes, commentary, and insights.
Your owned media – such as your company’s blog and leadership social accounts – to publish strong POVs that can attract attention from journalists covering relevant topics.
Targeted outreach to publications by researching ideal outlets and proactively offering something of tangible value to open a conversation and build relationships.

I’ve seen brands get real traction this way, but it has its limits.

Your brand may have contacts at industry outlets, but trusted sources with huge, more general user bases (e.g., The Wall Street Journal) are generally outside of a marketing team’s scope and almost never respond to cold outreach.

Dig deeper: Why PR is becoming more essential for AI search visibility

7. Invest in a PR partner

You may have been putting this off because it’s a significant investment, but the impact of PR-fueled brand mentions now carries extra weight.

PR partners have always been effective for generating backlinks and awareness, and their contacts at top-tier business publications are more valuable than ever for organic visibility.

Unless you have an in-house PR resource who can drop an email to the New York Times or set up a lunch briefing with Forbes, a whole tier of potential brand mentions will remain out of your reach.

We know this limits the clout you’ll appear to have when AI Overviews factors in which brands to cite for relevant queries.

Boost AI search visibility by growing brand mentions

Brand mentions have always played a meaningful role in SEO – not as spammy backlinks, but as authoritative citations that support E-E-A-T .

Their outsized influence in AI Overviews is more than just a bonus; it’s a strong signal that brand strength is becoming a defining factor in organic discovery, now and in the near future.

Dig deeper: Want to beat AI Overviews? Produce unmistakably human content

How to make ecommerce product pages work in an AI-first world

Tue, 15 Jul 2025 12:00:00 GMT

Most ecommerce product pages weren’t built for how people shop today.

They were written for humans scanning specs – not for AI tools filtering, summarizing, and recommending products behind the scenes.

But AI is now your new sales rep. And unless your product detail pages offer the right kind of context, they’ll be skipped entirely.

This article tackles:

How AI changes product discovery.
Why context matters more than keywords.
What to include in your PDPs to stay visible – and persuasive.

From sales rep to (AI) rep

Over the past 20 years, we’ve watched the buying process shift dramatically – from physical stores and printed catalogs to Amazon and ecommerce brands of every size.

Back then, you’d speak with a sales rep who asked:

What you were looking for.
What you planned to do with it.
Whether you had a preference for certain materials or brands.

If you were lucky, they already knew a bit about you and might hand you Levi’s jeans the moment you walked in.

Then came the ecommerce era, with product pages listing features, facts, and specs.

Now, with AI, we’ve come full circle.

Once again, we’re “talking” to something – only this time, it’s a chatbot or LLM:

Asking qualifying questions.
Offering personalized options.
Often knowing far more about us than any human ever could.

Whether you’re doing SEO for a Shopify or WooCommerce site, running an ecommerce brand, or selling on Amazon, thinking of AI as your new sales rep is a helpful mindset shift.

It’s no longer just about what your product is – it’s about how well the machines understand who it’s for and why it matters.

Who will be reading your product descriptions?

With the growing adoption of AI tools, more intermediary apps and platforms are emerging to handle a wide range of use cases:

Conversational assistants guide users through product selection by asking qualifying questions.
AI recommends products based on purchase history and browsing behavior.
It analyzes reviews to highlight key features or concerns.
It adjusts pricing dynamically based on real-time demand and competitor data.

What we see across platforms is the same process:

Information > AI > Revised output

AI pulls information from the web, processes it, and provides a refined answer or suggestion.

The more users rely on these tools, the fewer decisions are made without AI’s involvement.

Take Amazon, for example. It now summarizes product reviews so users don’t have to read hundreds of them.

In many industries, fewer and fewer customers will read your product descriptions directly.

Instead, they’ll accept filtered, pre-selected information as a starting point.

Dig deeper: How to leverage cosine similarity for ecommerce SEO

A role play to understand probability

Imagine you’re selling cars at Toyota. When someone walks into the dealership, you can’t recommend a car without knowing the context.

But once you learn she’s a married mother of two who sometimes drives her father to the hospital and enjoys hiking, the choice becomes much easier.

Here’s how Claude would approach a general query:

It’s all about narrowing down the context to formulate a better question.

Context is king

The more context AI has, the better it can form internal questions, run additional queries, and refine results, similar to how Google expands a query behind the scenes .

This added context improves the quality of answers or generated content.

A simple transactional search on Perplexity, for example, triggers nine different sources to build a broader response with related follow-ups.

While it’s clear context matters, many ecommerce sites still rely on thin, manufacturer-copied product descriptions.

There are better ways to provide context, like using advanced schema markup. For example, the product type’s usageInfo or audience fields

Platforms like Shopify surface basic schema based on product attributes, but aren’t ready for deeper markup out of the box.

Join the dots on your product detail page

To appear in AI-driven search results or chats, your product pages must connect:

What you’re selling.
Why someone wants to buy it.
What they need it for.

Just like a Toyota sales rep might instantly think of a RAV4 or Land Cruiser when a customer mentions hiking, mountains, and off-road trips, your product pages should reflect use cases and intent.

When writing a product description, imagine a conversation with the buyer:

What would you ask them?
What might they say?
What would they need to know?

This exercise helps uncover what to include in your product descriptions, feature lists, FAQs, and more.

A quick role play with your AI tool of choice can be a great source of inspiration.

Product description makeover: From generic to context-rich

Here’s a simple example of a typical product description – before, and after enriching it with additional context:

Before:

“Waterproof hiking boots. Gore-Tex construction. Available in sizes 7-12. Black, brown, tan colors.”

After:

“Waterproof hiking boots engineered for serious outdoor enthusiasts who tackle challenging terrain in unpredictable weather. The Gore-Tex construction keeps feet dry during stream crossings and sudden downpours, while the aggressive tread pattern provides confidence on loose rock and muddy trails. Ideal for multi-day backpacking trips, day hikes in mountainous regions, and anyone who refuses to let weather dictate their outdoor adventures. Temperature rated for conditions down to -20°C.”

Key difference: The optimized version answers “who is this for?” and “what problems does it solve?” – not just what it is.

Dig deeper: How to use generative AI to create product descriptions from your reviews

Expanded context across collection pages

Collection (or category) pages have long been a key part of ecommerce SEO.

For broader searches, AI tools often link directly to category pages, especially when the products are similar (e.g., sunglasses).

That makes context on collection pages critical, not just on the page itself, but also through content from internal and external pages that link to it.

Two ways to surface the context AI needs

One of the most effective strategies over the past 15+ years has been interviewing customer service and sales teams.

Their insights into top questions, common concerns, and specific use cases have proven invaluable.

For large ecommerce sites, though, this isn’t always scalable.

When working with 70,000 SKUs, you can’t optimize every description individually. Instead, you can build scalable systems to gather insights and boost visibility.

1. Manually gathering context

If you’re researching stand-up paddle boards, search Google for something like site:reddit.com “SUP board” to surface real user discussions.

Export results with a Chrome extension, then analyze them using your AI tool of choice.

2. Automation and AI

Modern automation tools, paired with scraping and LLMs, can surface deep customer insights at scale.

This lets you build a continuously updating context database to support PDP and collection page content.

You can pull data from sources like Reddit , Quora , Amazon, or niche Facebook groups and process it within minutes.

Tool stack:

Apify for Reddit/Quora scraping.
OpenAI API for context analysis.
Airtable for context database.
N8n for workflow automation.

Implementation process

Data collection: Set up n8n to regularly scrape target threads or posts using Apify (e.g., Reddit discussions on your product category).
AI analysis: Run the scraped content through a prompt like:
- Analyze these discussions about [product category]. Extract:
  - Common use cases and scenarios.
  - Frequent pain points mentioned.
  - Specific conditions/environments discussed.
  - Demographic indicators.
Context database: Store results in Airtable or Google Sheets with fields like:
- Use case scenario.
- Target demographic.
- Problem solved.
- Seasonal relevance.
- Confidence score.
Auto-refreshing: Use n8n to automate regular updates and trigger notifications when new insights are available – so you can quickly apply them across product and collection pages.

Start improving your product detail pages today by adding a simple sentence to each description beginning with:

“Ideal for…”
“Perfect when…”

Just this small addition can help your product attract more of the right buyers.

Write product pages that AI recommends – and customers buy

Customer behavior is shifting fast.

Depending on your audience, potential buyers may now spend more time in ChatGPT or Google’s AI Mode than on traditional search.

AI tools aim to match user prompts with their conversation history and known preferences to suitable products.

Supplying rich context on your product and collection pages helps AI connect your offer to a user’s specific use case.

Start with your top 20 products. Identify:

The problems they solve.
Where they excel.
Who they’re for.
When they’re ideal.

This context-driven approach sets your products up for discovery in an AI-powered world.

Dig deeper: Retailers: Google is becoming your new category page

How to keep your SEO skills sharp in an AI-first world

Mon, 14 Jul 2025 13:00:00 GMT

Discovery looks different now. AI is influencing how people find answers, products, and brands – and that shift impacts SEO .

To keep your skills relevant in an increasingly AI-driven world, you need to blend traditional SEO expertise with AI-centered competencies.

The overlap between SEO and GEO skills

SEOs are taking on more GEO responsibilities, thanks to overlapping goals, user behaviors, and skill sets.

Goals

When working in SEO and GEO, you’re likely optimizing for discoverability – that is, making sure your brand appears as an answer or result when users are searching for relevant information, products, or services.

This means that both SEO and GEO focus on improving visibility, increasing relevance, and aligning with user intent.

User behavior

There are clear similarities in user behavior across both types of discovery engines.

In both cases, users are actively looking for something – whether it’s where to buy shoes online, how to compare brands, or which sites are reputable for making a purchase.

They may also be searching for answers to a specific question or trying to get a general overview of a topic.

While there are differences between search engines and LLM platforms, both serve users who are in an active “search” phase, unlike more passive touchpoints such as ads or email marketing.

Skills that overlap

Because the goals and user behavior in LLMs and traditional search engines are similar, there are skills that transfer well between the two.

User intent analysis

In both GEO and SEO, it’s important to understand what your potential audience is using these platforms for.

This means identifying the inputs users provide and the outcomes they hope to achieve. In traditional SEO, we do this through keyword research.

With LLMs, we focus more on prompt topics. While the inputs may differ, the methods for identifying them are similar.

In SEO, we use keyword research tools to find the terms people are searching for, then test those terms to see what results appear and what kind of intent the search engine assigns to each query.

With GEO, the process is much the same. We use software to identify the types of prompts users are entering that return our brand – or our competitors’ – in the results.

We then examine the responses across different LLMs and look for opportunities to improve our online presence so our brand surfaces more effectively.

Content strategy development

LLMs and search engines both prioritize content that is reliable, comprehensive, and accurate when selecting their sources.

Whether you’re creating on-site content to attract organic search traffic or developing off-site content to strengthen your brand’s presence in LLMs, the strategy behind that content is essential.

Fortunately, this is a skill you’ve likely been refining throughout your SEO career.

Technical SEO skills

Just like search engines, LLMs need access to your website’s content.

The technical setup of your site plays a critical role in making that content accessible and understandable.

The same technical SEO skills used to help search bots crawl and interpret your content will continue to be valuable for GEO.

Digital PR skills

Now more than ever, building your brand through digital PR is essential.

Beyond backlinks, how your brand is talked about – and how consistent that messaging is across sources – will matter for GEO.

The skills that made your outreach campaigns successful in traditional SEO will remain vital in this new landscape.

Identifying skills that help with using AI in your SEO workflows

As AI becomes more widely adopted, it’s important to stay current on how it can enhance your SEO workflows.

Automation and efficiency

In the past, SEO automation often required extensive Python skills.

Now, with the rise of AI, many automations are just a few prompts away.

Even if you can “ vibe code ” your way to building something useful, it’s still essential to understand the limitations and risks of relying on AI for efficiency gains.

The key skill to build here is knowing how to stress-test your automations and understand how they interact with proprietary data.

For example, if you create an automation to streamline your keyword research but don’t test it thoroughly, you might miss critical issues, like it omitting 50% of your keyword data.

Or, if you feed SEO data into an LLM to generate a monthly report, you could inadvertently violate your company’s data security policies.

Prompt engineering

It’s essential to understand how LLMs interpret your inputs and determine the content and format of their outputs.

Prompt engineering is a valuable skill because it helps you structure prompts in ways that produce the level of detail and accuracy you need.

Tool proficiency

Understanding the capabilities and limitations of LLMs is key to using them effectively in your workflows.

Knowing what an LLM can (and can’t) do will save you time by preventing you from chasing outputs it simply can’t deliver.

It will also help you recognize hallucinations and avoid misinterpretations.

Dig deeper: Advanced AI prompt engineering strategies for SEO

GEO-specific skills

Some GEO skills aren’t directly transferable from SEO.

To confidently work across both disciplines, it’s important to understand these GEO-specific capabilities and actively develop them.

Understanding how LLMs work

The mechanics behind LLMs differ significantly from those of search engines, both in:

How they gather data for their knowledge bases.
How they identify new information and generate responses.

These systems function differently, and understanding those differences is critical to optimizing for both.

Understanding user behavior in LLMs

User interactions with LLMs aren’t identical to how they use search engines.

Inputs in LLMs tend to be more conversational and specific, and users may have saved history or added context that shapes the query.

This can make each interaction highly unique, even when users are ultimately seeking similar answers.

It’s critical to understand how this impacts prompt analysis and how that process differs from traditional keyword research.

Broad content design

Because of how users interact with LLMs, they may move through the traditional buying journey much faster than through search.

A user might go from describing a problem to discovering solutions, comparing providers, and making a decision – all within minutes, entirely within the LLM.

This means your content strategy must account for every stage of the buying journey – awareness, comparison, purchase, and advocacy – both on and off your site.

That includes making sure your reviews are detailed, comparison articles are accurate, and pricing is current both on your site and across third-party sources.

LLMs draw from many sources, so the risk of misinformation is higher than in traditional SEO, where a brand’s site typically ranks for its own terms.

Consistency of messaging

A key difference lies in how messaging is handled across non-brand websites.

In SEO, the focus has been on avoiding duplication to show unique value.

With LLMs, however, consistency matters more.

While exact duplication isn’t necessary, your core brand message must appear consistently across multiple trusted citation sources so LLMs can accurately reflect it.

Dig deeper: How to integrate GEO with SEO

Staying current in a rapidly evolving industry

Keeping up with the pace of change in SEO and GEO can feel overwhelming, but developing a few intentional habits can make a big difference.

Habitual learning

Build learning into your routine.

That could mean reading one article a day from a trusted source or listening to an SEO/GEO-focused podcast once a week.

Regularly setting aside time to upskill helps ensure you stay ahead of changes without burning out.

Distill information

Right now, simply scrolling through LinkedIn can feel like drinking from a firehose.

Everyone has a hot take on GEO, and there’s no shortage of conflicting advice and debate.

Instead of trying to keep up with everything, curate your inputs. Find a few trusted sources that distill the week’s key conversations.

That way, you’ll engage with vetted, relevant insights during your dedicated learning time without getting buried in noise.

Experimentation mindset

The future of SEO and GEO won’t be static.

The landscape will continue to evolve, and staying effective means staying curious.

SEO has always involved testing strategies to see what works best for a specific site or industry, and that spirit of experimentation remains essential.

Keep an open mind. What works today may not work five years from now, and that’s OK.

Community engagement

Staying sharp also means staying connected.

Go beyond passively scrolling through LinkedIn and engage with the SEO community more intentionally.

Join groups like Women in Tech SEO, attend conferences, and participate in discussions with practitioners you respect.

Building relationships with other SEOs helps you tap into real-world insights and learn which tactics are actually working.

Auditing of workflows

LLM-powered automations continue to open new opportunities for efficiency, but that doesn’t mean your workflows are “done.”

Regularly auditing your processes helps ensure they remain effective, even after AI integration.

There may be more streamlined approaches or new tools better suited for GEO-specific needs.

Staying agile will help you continue to optimize as technology evolves.

Adapting your SEO playbook for the age of LLMs

Think of AI as a collaborator, not a threat. Focus on learning how to use it effectively and keep experimenting.

GEO is a fast-moving offshoot of SEO, and while it represents a significant shift, SEO has never stood still.

You’ve adapted to every change so far.

This shift simply asks for a bit more time, curiosity, and commitment – but it’s still within reach.

Geoptie: Free GEO tools every SEO professional needs in 2025 by Tor.app

Mon, 14 Jul 2025 11:00:00 GMT

The digital marketing landscape is experiencing a seismic shift. While traditional search engine optimization focused primarily on Google’s algorithms, a new frontier has emerged: Generative Engine Optimization (GEO) .

As AI-powered answer engines like ChatGPT, Claude, Gemini, and Perplexity reshape how users discover information, SEO professionals must adapt their strategies to remain competitive.

The challenge? Most SEO tools weren’t built for the AI era.

That’s where Geoptie steps in, offering a comprehensive suite of free GEO tools designed specifically for optimizing content for generative search engines.

Drawing from years of hands-on SEO experience, they’ve created four powerful free GEO tools that solve the AI optimization puzzle. No guesswork, no theoretical frameworks – just practical solutions that work.

Understanding generative engine optimization (GEO)

Think beyond SEO rankings. GEO focuses on becoming the source AI engines trust and cite.

When someone asks ChatGPT about your industry topic, your content should be the foundation of that response. When Claude needs expert information in your field, your website should be the primary reference.

This shift demands new strategies, new metrics, and new tools built specifically for the AI era.

Built by winners of the SEO game

Success leaves clues, and the Geoptie team has left plenty. Before tackling AI search optimization, founder Siyar Isik and his experienced team proved their SEO mastery by building Transkriptor into a market-leading SaaS platform. They didn’t just participate in the SEO game – they dominated it

This track record translates directly into better GEO tools. While competitors offer features based on speculation, Geoptie delivers solutions rooted in proven optimization strategies.

The team knows which metrics actually matter, which tactics drive results, and which approaches waste time. That practical wisdom powers every tool they create.

1. Free GEO audit: Your complete AI visibility health check

The Free GEO Audit tool serves as your comprehensive diagnostic platform for AI search readiness. This powerful analyzer from Geoptie examines your website across six critical dimensions that determine how well your content performs in generative search environments.

Key features:

Citation readiness : Evaluates how easily AI systems can extract and attribute information from your content. Proper citation structure is crucial since AI engines need clear, attributable sources.
Answer alignment : Measures how well your content directly answers common user queries. AI systems prioritize content that provides clear, concise answers to specific questions.
Knowledge Graph optimization : Assesses your content’s structured data and entity relationships, helping AI systems understand the context and connections within your information.
Content authority : Analyzes signals that establish your content as a trusted, authoritative source in your field – a critical factor for AI recommendation algorithms.
Technical optimization : Reviews technical elements that affect AI crawling and understanding, including schema markup, page structure, and accessibility.
Competitive positioning : Compares your GEO performance against competitors to identify opportunities and gaps in your AI optimization strategy.

Why SEO professionals will love it:

This audit tool eliminates guesswork by providing actionable insights specific to AI search optimization. Instead of adapting traditional SEO metrics, you get targeted recommendations for the AI-first world.

2. GEO Content Checker: Elevate your content for AI discovery

Content remains king, but the rules have changed. The GEO Content Checker from Geoptie evaluates your existing content across six specialized dimensions designed for optimal AI search performance. Leveraging the team’s extensive SEO experience from Transkriptor, this tool provides insights that traditional content analysis simply can’t match.

The 6 dimensions of GEO content excellence:

Content structure : Analyzes how well your content is organized for AI parsing, including proper heading hierarchy, logical flow, and scannable formatting.
Factual density : Measures the concentration of verifiable facts and data points within your content – AI systems favor information-rich, substantiated content.
Semantic clarity : Evaluates how clearly your content communicates concepts and relationships, crucial for AI understanding and contextual relevance.
Answer completeness : Assesses whether your content provides comprehensive answers to user queries, reducing the need for AI systems to seek additional sources.
Authority signals : Identifies elements that establish credibility and expertise, including citations, author credentials, and supporting evidence.
Competitive differentiation : Analyzes what makes your content unique compared to other sources on the same topic, helping AI systems recognize your distinctive value.

Professional impact:

For SEO specialists managing multiple clients or large content portfolios, this tool streamlines the content optimization process by providing specific, actionable feedback tailored to AI search requirements.

3. GEO Keyword Finder: Discover the language of AI search

Traditional keyword research tools focus on search volume and competition metrics that don’t apply to AI-powered search.

The GEO Keyword Finder from Geoptie revolutionizes keyword discovery by identifying terms and phrases that perform exceptionally well across generative search engines. Built on the solid foundation of SEO expertise that made Transkriptor successful, this tool represents a breakthrough in AI-focused keyword research.

What makes it different:

This tool analyzes how ChatGPT, Claude, Gemini, and other AI platforms process and respond to different query types. Instead of focusing solely on search volume, it identifies:

Conversational query patterns that users employ when interacting with AI systems.
Long-tail opportunities specific to AI-generated responses.
Semantic variations that AI systems associate with your core topics.
Intent-based keywords that trigger detailed AI responses in your niche.

Strategic advantage:

By targeting AI-optimized keywords, your content becomes more likely to be referenced, cited, and recommended by generative search engines – creating a new channel for organic traffic and brand visibility.

4. GEO Rank Tracker: Monitor your AI search visibility

Understanding your performance across AI search platforms requires specialized tracking.

The GEO Rank Tracker from Geoptie monitors your website’s rankings across multiple AI search engines, providing location-based and keyword-specific insights that you won’t find anywhere else.

Comprehensive AI platform coverage:

ChatGPT : Track mentions and citations in OpenAI’s responses.
Claude : Monitor visibility in Anthropic’s AI assistant.
Gemini : Analyze presence in Google’s AI search integration.
Perplexity : Track performance in AI-powered answer engine.
Bing Chat : Monitor Microsoft’s AI search integration.

Advanced tracking features:

Location-based monitoring : Since AI responses can vary by geographic location, track your performance across different markets and regions.
Keyword-specific insights : Monitor how your content performs for specific queries and topics across different AI platforms.
Competitive intelligence : Compare your AI search visibility against competitors to identify opportunities and threats.
Trend analysis : Track performance changes over time to measure the impact of your GEO efforts.

Getting started: Your free GEO toolkit awaits

The transition to AI search is not a distant future concern – it’s happening now. Every day that passes without GEO is a missed opportunity to establish authority in the AI-driven search landscape.

These four free tools from Geoptie.com provide everything you need to begin your GEO journey:

Start with the Free GEO Audit to understand your AI search readiness.
Use the GEO Content Checker to optimize your most important pages.
Leverage the GEO Keyword Finder to discover new AI optimization opportunities.
Implement the GEO Rank Tracker to monitor your progress across AI platforms.

The competitive advantage of early adoption

SEO professionals who master GEO now will have a significant advantage as AI search becomes increasingly mainstream. These free tools remove the barrier to entry, allowing you to experiment, learn, and refine your approach without financial risk.

The question isn’t whether AI search will impact your SEO strategy – it’s whether you’ll be prepared when it does. With Geoptie’s free GEO tools, you can start that preparation today.

Ready to evolve your SEO strategy?

The convergence of AI and search represents the most significant shift since the mobile revolution. Don’t let your SEO strategy become obsolete while competitors gain ground in AI search visibility.

Access these powerful GEO tools today at Geoptie.com and join the ranks of forward-thinking SEO professionals who are already optimizing for the AI-first future.

Built by the experienced team behind Transkriptor , Geoptie represents the evolution of SEO tools for the generative AI era. Your clients, your rankings, and your career will thank you. Ready to get started? Visit Geoptie.com today and unlock your free GEO toolkit – because in the age of AI search, optimization isn’t just about being found; it’s about being chosen. Experience the difference that years of proven SEO expertise can make in your AI optimization strategy.

What generative search means for brand visibility (and what CMOs are doing about it) by Edna Chavira

Thu, 10 Jul 2025 20:17:00 GMT

As AI-driven platforms increasingly shape how brands are represented online, marketing leaders face a new imperative: ensuring their narrative remains accurate, consistent, and strategic.

In this webinar , technical marketing expert Myriam Jessier and Gerald Murphy , Evangelist at Semrush Enterprise, will introduce the Brand Control Quadrant —a practical framework for understanding and managing how large language models (LLMs) interpret and present your brand.

You’ll learn how to:

Audit how AI currently describes your brand—and spot misalignments early
Respond quickly when generative tools get your brand wrong
Feed the right brand content to influence what AI “remembers”
Uncover hidden signals that shape how AI presents your business

This session is designed for CMOs, and for search and marketing leaders navigating the new realities of generative AI and brand management. Sign up here.

Cloudflare’s Pay Per Crawl: A turning point for SEO and GEO

Thu, 10 Jul 2025 14:00:00 GMT

The content economy is in trouble .

AI crawlers read websites, summarize information, and offer answers without sending users to the original source.

The result?

A dramatic drop in traffic from AI models, declining ad revenue, and a growing threat to the business model that has powered the web for decades.

Now, Cloudflare is stepping in with a new approach.

Just two weeks ago, CEO Matthew Prince said in an interview with Axios :

“AI is going to fundamentally change the business model of the web. The business model of the web for the last 15 years has been search. One way or another, search drives everything that happens online.

Last week, Cloudflare introduced a possible solution: Pay Per Crawl , a first-of-its-kind monetization layer that lets publishers charge AI bots (like OpenAI’s GPTBot or Anthropic’s ClaudeBot) for accessing their content.

It’s not a complete redesign of how the web works, but rather a new layer built on top of existing web infrastructure.

The system uses the rarely implemented HTTP 402 “Payment Required” response code to block or charge bots that attempt to crawl a site.

So, how does it work, and what does it mean for the future of generative engine optimization (GEO)?

Let’s break it down.

The unspoken deal is breaking – for the first time

It’s worth stepping back for a moment to understand the deeper context behind Cloudflare’s move, as Prince highlighted last week.

Nearly 30 years ago, two Stanford grad students started working on a research project called “Backrub.”

That project became Google. But it also became something bigger: the foundation of the web’s business model.

The unspoken deal between Google and content creators was simple:

Let us copy your content to show it in search results, and in return, we’ll send you traffic.
You, as the creator, could monetize that traffic by running ads, selling subscriptions, or just enjoying the satisfaction of being read.

Google built the entire ecosystem that supports that deal:

Google Search generated the traffic.
Google bought DoubleClick and launched AdSense to help you earn from it.
Google acquired Urchin to create Google Analytics so you could measure everything.

It worked.

For decades, this agreement was what kept the open web alive.

But now, that deal is starting to fall apart.

For the first time, Google’s search market share is declining , and what’s replacing them?

AI.

Prince put it plainly:

Ten years ago, Google crawled two pages for every visitor it sent to a publisher.

Six months ago, the ratios were:

Google: 6:1
OpenAI: 250:1
Anthropic: 6,000:1

And today:

Google: 18:1
OpenAI: 1,500:1
Anthropic: 60,000:1

The trend is clear: AI reads, learns, and answers, but rarely sends users back.

The mutual value exchange that once powered the web is quietly collapsing, and content creators are being left out of the equation.

How Cloudflare plans to change the game

When you think about it, the solution is actually pretty smart.

Instead of forcing website owners to choose between two extremes – either block AI crawlers completely or give them full access for free, Cloudflare introduces a third option: charge them.

Here’s how it works.

Every time an AI crawler requests content, the server can respond with an HTTP 402 Payment Required, including the price for access.
- If the crawler agrees to pay, it sends a follow-up request with a payment header, and the server returns the content with a 200 OK response.
- If it doesn’t, no access is granted.

Within the Cloudflare dashboard, site owners will be able to set one of three access rules for each individual AI crawler:

Allow: Grant full access (HTTP 200).
Charge: Require payment per request (HTTP 402 with price info).
Block: Deny access (HTTP 403), but with a hint that payment could be negotiated in the future.

It’s like introducing an API between content and generative models, where information isn’t just free-floating knowledge; it’s now a monetizable digital asset.

And let’s be clear: this isn’t some garage-stage startup trying to shake up the web.

Cloudflare powers the CDN infrastructure for over 20% of all websites, including media companies, content platforms, governments, and major brands, according to company data, .

Sites like Reddit, Medium, Shopify, Udemy, and The Guardian all run through Cloudflare’s network.

That means one simple thing: if Cloudflare decides to limit access, it can do so at a massive scale.

But scale is only part of the picture.

Cloudflare also knows exactly which bots are visiting these sites and how often .

Bytespider, the AI crawler operated by ByteDance (parent company of TikTok), is the most active, visiting over 40.4% of all Cloudflare-protected domains.
Next up: GPTBot from OpenAI, at 35.5%, followed by ClaudeBot from Anthropic at 11.2%.
Interestingly, GPTBot, despite being the most “mainstream,” is also one of the most frequently blocked bots across the network.

Cloudflare notes that many publishers aren’t even aware that AI crawlers are visiting their sites so aggressively. The scale of this hidden scraping is likely far larger than most realize.

How will Pay Per Crawl impact SEO?

First, let’s be clear: Cloudflare will not block BingBot, Googlebot, or any traditional search engine crawlers by default.

However, it does block AI crawlers like GPTBot or ClaudeBot by default, particularly for new websites joining the platform.

Site owners can override this setting and decide which bots to allow, block, or charge using Cloudflare’s new access rules.

The update focuses specifically on AI models, crawlers from companies like OpenAI and Anthropic that gather content to train or power large language models (LLMs), rather than to serve real-time search results.

That said, for the first time, Cloudflare is also giving publishers the option to block or charge traditional search engine crawlers, a level of control that simply didn’t exist before.

Will publishers actually do that?

Probably not, at least not most of them.

Blocking Google or Bing still means risking a huge slice of valuable organic traffic, and for many, that’s too high a price.

And while The Wall Street Journal recently reported a noticeable drop in publisher traffic due to AI-driven search experiences, Google is still responsible for a significant share of most publishers’ traffic today, often 30-60% or more, depending on the site.

But that’s starting to change.

A growing number of major publishers have joined Cloudflare’s Pay Per Crawl initiative, including:

TIME.
The Atlantic.
ADWEEK.
BuzzFeed.
Fortune.
Quora.
Stack Overflow.
Webflow.

This publication, which first reported the story , is among them.

So while SEO is still too important for most to walk away from, for the first time, the power to say “no,” or at least “pay up,” is officially on the table.

Dig deeper: 3 reasons not to block GPTBot from crawling your site

How will Pay Per Crawl impact GEO?

Absolutely, and more deeply than you might think.

To understand why, let’s use ChatGPT as an example. As many know, the premium version of ChatGPT performs real-time searches using Bing.

But behind the scenes, there are actually two separate processes at work:

Model training (Training data)

Before the model can search anything, it first needs to be trained. This is when LLMs learn how language works and build their knowledge of the world, using massive datasets that include text, code, images, and information pulled from across the public web.
That content is gathered by dedicated AI crawlers like GPTBot (OpenAI), AnthropicBot (Anthropic), and similar bots operated by Google and Meta. You can read more about OpenAI’s data collection process here .

Real-time information retrieval

After the model is trained, it can pull fresh data from the web via external tools, for example, ChatGPT’s Bing integration.

At first glance, you might think, “If my site isn’t blocking Bing, ChatGPT will still see it when users search, so what’s the big deal?”

But here’s the key distinction:

Training is what builds the model’s foundational understanding of the world, its sense of language, topics, context, and domain expertise.
Real-time search is just a way to supplement that understanding, not replace it.

And that’s where Pay Per Crawl starts to matter.

As more websites restrict access to AI crawlers, the training process becomes limited, and that creates three major effects.

Decline in answer quality (even with real-time search)

Even if the model can “see” your site via Bing or Brave, it won’t know how to interpret or prioritize your content if it wasn’t exposed to similar material during training.

Built-in bias toward known sources

Models tend to favor sources they’ve seen during training; they “trust” them more.
If your content wasn’t part of that process, you might be overlooked or less visible in AI-generated answers, simply because the model doesn’t recognize your tone, format, or authority.

Long-term impact on GEO

As more websites opt into Pay Per Crawl and limit access, the overall training data available to AI models becomes smaller.
Sites that weren’t visible during training won’t become part of the model’s “mental map,” even if they’re technically accessible during real-time search.

Bottom line

Real-time search may still function, but without a strong foundation, the model won’t know what to look for, how to interpret it, or why your content matters.

GEO isn’t just about visibility; it’s about whether the model knows you, trusts you, and understands you. And all of that starts with training.

The new marketing war isn’t for clicks – it’s for memory

Thu, 10 Jul 2025 12:00:00 GMT

Marketing used to be a game of visibility.

You researched the market, spotted the gaps, built the content, ran the ads. You showed up, people saw you, some of them clicked-and, crucially, you could see it happening .

You had dashboards. Attribution models. Funnels. You could test, optimize, improve. It wasn’t perfect, but it was observable.

That era is over.

We’re entering a fundamentally different paradigm; where intelligent systems mediate how people discover, evaluate, and interact with brands. Whether it’s an LLM summarizing answers, an AI assistant making recommendations, or a multi-modal agent facilitating purchases, the new digital experience isn’t about users browsing websites. It’s about systems making decisions.

And those systems don’t behave like users.

They don’t search with discrete keywords. They don’t follow funnels. They don’t click.
They see differently. They reason differently. They decide differently.

When an AI assistant decides what to recommend based on Reddit sentiment, embedded documentation, third-party schema, or the tone of a YouTube review, your analytics stack won’t capture it. You won’t know what tipped the balance. You won’t even know you were in the running, let alone that you lost.

In response, the industry is scrambling to claw back visibility. A wave of AI tracking tools promise insight into where and how your brand appears in generative results. Platforms offer new impression metrics – buried, blended, and unsegmentable.

But these are comfort metrics . They offer the theatre of insight, not its substance. They retrofit old measurement paradigms onto systems that were never designed to be interrogated.

This article is about that shift – and what it means.

It’s about why:

The tools we’ve built no longer serve us.
Brand reputation now lives in the latent soup of model memory.
Being inferred by an AI matters more than being clicked by a human.

Because when everything is opaque, the job isn’t to be seen.

It’s to be unignorable .

The world we lost

For the better part of two decades, marketing was a game of measurable influence.

You could map a funnel. Model user journeys. Attribute value across channels. Sure, the tools were messy – tracking was incomplete, attribution was flawed, and platform data was often opaque. But there was at least a logic to the system. It was observable. Manipulable. Optimizable.

We could research what people wanted, through keyword volumes, search trends, social signals. We could place ourselves in their path via ads, content, listings, influencers. And we could observe what worked, through impressions, clicks, conversions, attribution models.

This was the golden era of the marketing stack:

Research tools to measure demand.
Campaign tools to reach audiences.
Analytics tools to track behavior.
Optimization tools to iterate and improve.

And, crucially, everything was wired into a loop. You did something. Something happened. You measured it. You adapted.

Even if you were wrong, you could know you were wrong.

That feedback loop didn’t just drive performance. It defined what marketing was. It gave us a sense of control. Of cause and effect.

Yes, the tools were flawed. Attribution was messy. Data was noisy. And in recent years, even that messiness has deteriorated further – between rising consent barriers, cookie blockers, and browsers systematically undermining tracking technologies, most of our reporting is already built on sand.

But the game was still legible. You could observe, infer, improve.

Now, the rules are changing – and we’re not the ones doing the observing anymore.

Mediated by machines

Today, users aren’t just exploring the web. Increasingly, they’re letting systems do it for them.

Instead of scanning search results or comparing reviews, people are beginning to delegate discovery and decision-making to intermediaries: LLMs, smart assistants, summarizing interfaces. These systems take a question, evaluate the landscape, and offer a single output – an answer, a product, a decision.

We can already see this shift in how Google presents information: AI Overviews distil SERPs into synthetic summaries, collapsing the decision space into a handful of unclickable takeaways. Other platforms are heading the same way.

These systems don’t behave like users. They don’t:

Search in discrete, one-shot queries.
Necessarily visit your site or engage with your funnel.
Click.
Convert.
Show up in your data.

Instead, these systems:

Evaluate .
Summarize .
Decide .

You’re not optimising for a visitor anymore.

You’re trying to be included in a model’s reasoning.

What used to be a competition for visibility is becoming a battle for inclusion .

We used to publish content to earn clicks. Now we publish signals in the hope of being cited, referenced, or trusted by a system that never tells us how it thinks.

That shift severs the connection between what we create and what we can see. It leaves us without feedback, without attribution, without certainty.

Measurement is dead

Now, marketers are flying blind.

In a world where intelligent systems shape outcomes invisibly, our tools for measuring success no longer apply.

Clicks, sessions, conversions – they might still happen, but they don’t explain why . The causal link between action and result has evaporated.

A user might never visit your site. They might act on a product recommendation surfaced in a generative answer, trust a quote pulled from a Reddit comment, or be swayed by a brand comparison made in passing by an AI assistant. They might hear your name in a podcast transcript, see it mentioned in a blog post, or catch it in a YouTube short – all without attribution, interaction, or awareness.

Every one of those moments is mediated by a system that evaluates your brand, weighs its context, and acts – without ever touching your funnel .

And yes, that conversion will show up in your reports just like any other. You’ll see the outcome, but none of the story behind it. The diagnostic power is gone.

This isn’t a minor loss of precision. It’s a severing of the feedback loop.

And that’s not just inconvenient – it’s existential. Marketing as a discipline depends on knowing what caused what. Without that, we’re guessing.

That’s why the industry is scrambling for something – anything – to measure. And so a wave of AI visibility tools now promise to show how your brand appears in generative answers. But they’re built on sand:

Panel-based tools rely on partial data from browser extensions or synthetic SERPs. They only capture a sliver of behavior – often from skewed cohorts.
Prompt-based tools fire zero-shot queries like “ What’s the best [X]? ” into LLMs and log the results. But that’s not how people use these systems. Real interactions are iterative, contextual, memory-driven. A single snapshot tells you almost nothing.
Even Google’s own data on impressions and clicks from AI Overviews offers no useful segmentation. You can’t isolate it. You can’t interrogate it. It’s visibility without clarity.

These aren’t insights. They’re artefacts of an old paradigm, duct-taped onto a new one.

They make us feel like we’re still in control. But they don’t help us make better decisions.

They’re theatre. Comfort metrics. Shadows on the wall.

To be clear, some proxies might still be useful, as long as we treat them solely as directional . The danger is in pretending they’re precise. Because they’re not. And the systems we’re dealing with were never designed to be measurable in the first place.

The longer we cling to legacy visibility, the more blind we become to the real shift underway.

And the less prepared we’ll be for the new game: one where visibility doesn’t matter.

Only influence does.

Influence is indirect and fragile

In this new world, you don’t get to make your case directly.

You don’t persuade the user. You don’t pitch the benefits. You don’t close the deal.

You present your evidence to a system, and hope it survives the summarization.

These systems don’t rank content. They assemble consensus. They don’t reward clickbait. They reward confidence. They weigh sources, distil perspectives, and deliver answers .

And in that distillation, much of what makes you special can be lost.

You’re not influencing decisions. You’re influencing reasoning.

That reasoning is probabilistic, synthetic, and unsentimental. Models don’t just look at your best content – they average across everything they can find. Documentation. Mentions. Citations. Schemas. Reviews. Context.

You might have the best product. The smartest team. The deepest expertise. But if the system doesn’t see that – if it isn’t consistently reinforced across credible surfaces – it doesn’t count.

This makes your position fundamentally fragile.

A competitor with better documentation, cleaner markup, tighter semantic alignment, and more coherent citations may become the default. Not because they’re better, but because they’re easier to summarize.

Worse, your absence can be caused by noise. A legacy schema conflict. A lukewarm review. A forum post from 2018. A few garbled citations. None of them matter on their own. But collectively, they push you below the threshold of inference.

You won’t get a warning. You won’t see a drop in rankings. You’ll just stop being included.

The system didn’t reject you. It just didn’t think of you.

That’s the new threat – not being disliked, but being omitted .

Not failing to win. Failing to be remembered.

The reputation war

If inclusion is everything, then reputation is infrastructure.

Not brand awareness. Not vanity metrics. But the ambient, structural signal your brand leaves in the latent layers of the web – and the models trained on it.

This isn’t just about who’s talking about you. It’s about what the system thinks they mean when they do.

Your reputation is the residue of context.

The echo of consistency across the network.

And it’s shaped not just by what you say, but by what others repeat – and what the model remembers .

This includes:

How consistently you’re described.
Where you’re mentioned.
Who links to you.
What context surrounds you.

A better competitor doesn’t need to out-market you. They just need to show up more coherently. Across more surfaces. In more trustworthy formats. With fewer contradictions, more citations, and cleaner associations.

That’s all it takes to win inclusion.

But this also opens the door to sabotage.

Your visibility can be undermined (deliberately or not) by:

Astroturfed reviews.
Low-quality forum chatter.
Negative sentiment loops.
Misattributed citations.
Fabricated content designed to erode confidence.

None of it needs to go viral. It just needs to exist , to be absorbed into the substrate.

And once it’s in, it’s hard to get out. The model won’t notify you. You won’t see a drop in rankings. You’ll just notice fewer mentions. Less traffic. Diminished presence. And no explanation.

The system didn’t attack you. It’s not biased or unfair. There is no social contract. It just learned to prefer someone else.

That’s the new competitive landscape:

Not fighting for attention.
Fighting for inference .

And winning that fight means not just defending your signals – but reinforcing them.

Because if reputation is the infrastructure of inclusion, then what you build next has to be unshakeable.

Marketing in the latent web

If reputation is what earns inclusion, then marketing’s role is to make that inclusion inevitable .

Not by shouting louder. Not by chasing clicks. But by becoming structurally obvious – woven into the fabric of the web in ways that models can’t ignore.

This isn’t about performance metrics. It’s about contextual permanence . You’re not just trying to rank. You’re trying to be the most consistent, confident, and computationally legible answer to the unspoken prompt: “Who belongs here?”

That means showing up everywhere that matters – and always in the same voice. It means repeating your positioning until it becomes a default assumption. And it means thinking less like a brand campaign, and more like a data layer.

You become influential not through exposure, but through consistency. Redundancy. Contextual gravity.

You’re not being seen.

You’re being remembered.

For years, we were told to “write for humans.” And that advice still matters – because human behaviour influences culture, and culture shapes the web.

But in a model-mediated world, that’s only half the picture.

These systems don’t just observe behavior. They interpret structure. They weigh phrasing, associations, citations, coherence. They draw conclusions from how information is embedded, not just how it’s received.

That means we need content that persuades humans , yes, but also content that reinforces models . Not spam. Not trickery. But clarity. Consistency. Strategic redundancy.

Not topical authority for humans – but for machines.

That means investing in four dimensions of latent influence:

Presence : Are you cited in places machines consider credible? Docs, forums, schema, transcripts, FAQs, datasets – not just blogs and landing pages.
Positioning : Are you described consistently and clearly across those sources? Or do you show up fragmented, contradictory, or vague?
Perception : What’s the quality and sentiment of your adjacents? Are you next to trusted voices, or surrounded by spam?
Permanence : Are your signals stable, persistent, and embedded in surfaces likely to be crawled, trained, and referenced long-term?

This isn’t SEO with extra steps. It’s branding for an audience that reads everything and forgets nothing.

You’re not just trying to win a SERP. You’re trying to shape a model’s memory.

That doesn’t show up in your dashboards. There are no conversion graphs. But the outcome is real and existential.

Because in this new ecosystem, inclusion isn’t earned through engagement.

It’s earned through embeddedness .

Defensive brand strategies

In a world where you can be forgotten without warning, you need to build your brand like it’s under siege – because it is.

Defensive strategy means treating reputation not as a campaign, but as a system. Not something you broadcast, but something you maintain .

You’re not building a monument.

You’re tending an ecosystem.

That means building redundancy across every axis:

Multiple trusted voices saying the same thing.
Multiple surfaces reinforcing your positioning.
Multiple third parties confirming your value.

If one citation gets diluted or poisoned, another can backfill. If one channel falters, others carry the weight. You’re not relying on a homepage and a handful of articles. You’re establishing a semantic fortress.

It also means proactive maintenance:

Audit model perception : Periodically prompt LLMs with naturalistic, multi-turn queries; not to generate metrics, but to spot-check how you’re being described, omitted, or misrepresented. This isn’t measurement; it’s sanity-checking inference.
Reinforce weak signals : Identify outdated or low-trust sources and upgrade them. Refresh your documentation. Reclaim neglected listings. Submit corrections where needed.
Patch reputation vulnerabilities : Identify and fix inconsistencies in how your brand is described across surfaces. Clarify ambiguities before they lead to confusion. Address negative discourse early, before it spreads or hardens. And don’t just respond to criticism – surround it with signals that restore confidence.

And most of all: own your category. Define it. Populate it. Be the default.

If you don’t control the frame, the model will borrow someone else’s.

You’re not just defending brand equity.

You’re defending inclusion .

The upstream advantage

In this landscape, traditional brand-building isn’t obsolete – it’s foundational .

Advertising, PR, sponsorships, cultural presence: these aren’t just tools for awareness. They’re how you generate the raw material that machines use to form opinions.

These efforts shape the conversations that shape the web – the Reddit threads, the YouTube reviews, the podcast mentions, the Wikipedia edits. They seed the citations, the sentiment, the context. They build the substrate.

The outputs might not be directly measurable in clicks or conversions, but their effects are trackable in the right frame:

Holdout testing across geographies or cohorts.
Brand lift studies.
Surveys on salience, recall, and preference.
Share of voice tracking across platforms.

The key is shifting the mindset – from deterministic attribution to probabilistic impact. From “ this ad caused this sale ” to “ this campaign lifted salience by X% in this cohort. ”

It’s not as satisfying as watching real-time dashboards tick up. But it’s more honest. And in this new world, it’s how we stay grounded in reality – without pretending to see through the fog.

As Barry Adams , renowned SEO expert and founder, Polemic Digital, put it:

“Marketers have been clinging to the illusion of measurement. Analytics platforms have provided the mirage of attribution and accountability, but that has always been a lie. What AI does is remove the mostly fake data that powered this illusion. It’s back to pre-internet basics, which is probably better for the marketing discipline as a whole.”

None of this is new. But in the context of model-mediated experiences, it becomes newly urgent. Because the real value isn’t just in what these campaigns do today – it’s in what they cause to be said tomorrow .

If you want to shape the model’s outputs, you must shape the culture that trains it.

In a world where direct influence is disappearing, upstream influence is your leverage.

In a world without signals, build for the shadows

This isn’t a passing trend. It’s a phase shift.

The tools we relied on – dashboards, attribution models, keyword lists – were never the point. They were artefacts of an era where visibility was linear and trackable. That era is ending.

What comes next isn’t about tactics. It’s about posture.

Because if you can’t track what works, you have to build what lasts . If you can’t prove your impact, you have to design for belief .

The web is still full of signals. But they’re no longer for us. They’re for the machines. And the machines aren’t watching for clicks. They’re absorbing structure .

So shape the discourse. Harden your reputation. Reinforce your adjacents. Leave traces in places that models crawl, weigh, and remember.

Not because it’s measurable.

Because it’s necessary.

You won’t get a report telling you it’s working. You won’t see the moment you’re forgotten.

But that’s the risk. Not rejection. Omission .

Because in this new world, it’s not enough to be the best. You have to be undeniable .

And that doesn’t start with visibility. It starts with being remembered .

This article was originally published on Jono Alderson’s website (as Everything is now opaque ) and is republished with permission.

Google Ads data shows query length shift post-AI Mode

Tue, 08 Jul 2025 13:00:00 GMT

Google hasn’t done a full cannonball into the large language model (LLM) pool, but they’re definitely in the water.

On May 20, Google launched AI Mode to all U.S. searchers , marking its most significant step yet toward integrating generative AI into the core search experience.

AI Mode is their version of what ChatGPT and Perplexity’s default models have been offering for months.

They see the writing on the wall ; search is undergoing its first fundamental shift since Google’s inception.

The entire model for Google, its advertisers, and its users is changing – and it isn’t going back.

So far, Google has provided little insight into how much search volume is flowing through AI Mode, leaving advertisers to figure it out themselves.

This article uses our internal search query dataset to infer user behavior patterns and evaluate how AI Mode may be impacting key advertising metrics.

The data I’m sharing comes from Google Ads search query reports spanning Jan. 1 to June 20.

Query length trends are evolving

Let’s start with impressions. Overall trends remain fairly stable at the long end of the curve (search terms with seven or more words), aside from a brief blip in April.

The biggest shift has been from 1-2 word keywords to 3-4 word search terms.

Short queries dropped from 42% in January to just 31% in June – suggesting users are increasingly communicating in more natural language.

Click data follows the same pattern.

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Click-through rates are down across the board

While this data indicates a shift in consumer behavior, it doesn’t necessarily mean that volume has changed.

That’s the current working hypothesis among many search marketing professionals: that given the nature of AI Mode and LLMs to deliver long-form answers, paid search and organic clicks are decreasing, or decoupling from impressions.

To better understand this, I looked at throughput metrics like click-through rate and conversion rates.

These data points paint a very interesting picture.

Across the board, CTRs have decreased fairly sharply. Every single category is down since January.

Shorter keywords have a CTR drop of 50%.

For longer keywords, the decrease is less dramatic, but only slightly.

Keywords with more than eight words are down by 26%.

Conversion patterns are shifting too

The volume of conversions – and the length of keywords driving them – has also shifted.

In this dataset, conversions were largely driven by shorter keywords (1-2 words) but have moved toward slightly longer ones (3-4 words).

Shorter keywords accounted for 76% of conversions in January, but dropped to 50% by June.

Three- and four-word queries now make up 40% of conversions, up from 20% in January.

Why this matters: Interpreting the behavioral shift

The dataset so far this year strongly indicates a significant shift in consumer search behavior since the introduction of Google’s AI Mode.

The drop in impressions and clicks for shorter (1-2 word) queries, alongside a rise in 3-4 word queries, suggests users are increasingly adopting more natural language in their searches.

This shift is reinforced by the across-the-board decline in CTRs and the migration of conversions from shorter to slightly longer keywords.

And this is just the tip of the iceberg.

Google is still rolling out AI Mode as the default to more users, and this behavioral shift will likely accelerate as they grow more comfortable with the new experience.

What advertisers should do next

To adapt to this evolving landscape, advertisers must take a data-driven, forward-looking approach.

Start with your own performance data, then refine your strategy around the growing trend toward natural language queries.

1. Scrutinize your own data

The first and most crucial step is to analyze your own Google Ads search query reports.

While this article highlights broad trends, your account performance may differ.

Look closely at patterns by keyword length across impressions, clicks, CTRs, and conversions.

2. Leverage your data to enable AI use cases

Use tools like Instant BigQuery to build more sophisticated AI-powered workflows and insights.

The more granular your data access, the better your ability to detect shifts and adapt.

3. Embrace longer-tail strategies

The rise of natural language queries calls for a shift in how you approach search.

Build campaigns and content that are designed for longer, more descriptive search terms. That includes:

Content optimization : Create comprehensive content that answers complex questions and meets specific user needs – these are increasingly discoverable through conversational queries.
Keyword expansion : Target more long-tail variations, phrases, and questions that reflect how users engage with AI models.
Ad copy relevance : Tailor your ad messaging to align with more specific, intent-rich queries, offering clear value and improving engagement.

4. Monitor AI Mode’s evolution

AI Mode is still in its early days. Keep a close eye on changes to features, rollout progress, and emerging user patterns.

Stay agile and be ready to pivot as Google continues to refine the experience.

Just the beginning of the shift

AI Mode isn’t a one-time change – it’s the start of a fundamental realignment in how users interact with search.

The shift toward longer, more natural queries is already underway, and the data shows it’s gaining momentum.

Advertisers who take this seriously now – by adapting content, strategy, and measurement – will be better equipped to compete in an increasingly AI-shaped search landscape.

How to stay grounded (and inspired) as AI changes search and SEO

Thu, 03 Jul 2025 13:00:00 GMT

If you are in or around SEO and have looked on LinkedIn at all in the last two years, you’ve likely witnessed firsthand the wildfire that our industry has become.

LLMs and AI chat tools are cutting into organic traffic in ways we can now see. ChatGPT is eating away at Google’s share.

AI-powered features like Copilot and AI Overviews are claiming clicks that used to be ours.

It’s a seismic shift – and many of us are left wondering if SEO is still worth pursuing.

The fear is real

It’s an honest question. Many people, even the most seasoned SEO pros, are asking it.

Traffic is down in many verticals, sometimes dramatically, and clients are nervous.

It’s not just numbers, it’s people worrying about their jobs, their agencies, their livelihoods.

The search results page itself feels like it’s getting carved up by AI-generated answers, endless ads, and zero-click experiences.

It’s easy to feel like the work we’ve built our careers on is crumbling.

I’ll be real: I’m writing this because half the time I feel like I’m scrambling to keep up.

The FOMO is real – and I know I’m not the only one. And if you’re nodding along right now, good. You’re not alone.

Organic web traffic is still valuable

Even with AI experiments everywhere, Google still drives over 60% of all U.S. web traffic referrals , according to SparkToro.

That’s not exactly “dead channel” territory.

Yes, AI Overviews and answer boxes might change how users click.

Some queries might get satisfied on the results page.

But users don’t stop needing in-depth content, products, services, or expertise. They still click through, just more thoughtfully.

Now is not the moment to throw up your hands and quit SEO.

It’s the moment to focus harder on being the site worth visiting, the answer worth citing, and the brand worth trusting.

If your strategy is just “rank for anything and hope for clicks,” yes, you should be worried.

But if your strategy is to actually help people? AI can’t kill that.

Dig deeper: The new SEO imperative: Building your brand

AI-EIEIO and the barnyard of new acronyms

In times like these, the industry does what it always does: it invents new acronyms to sell the fear back as opportunity.

I can’t tell you the number of courses, webinars, and straight-up service offerings flooding my newsfeed, all for a price.

You’ve probably seen some of these galloping across your screen: AI Overviews, GEO , and more.

They’re pitched as magic formulas to make your content “AI-friendly,” guaranteeing your brand will be cited in chatbot answers or appear in some AI-powered search result.

Peter Rota , an SEO I really respect, recently called these “hustlebro acronyms.”

Couldn’t have said it better.

Because that’s what they are – a new coat of paint on old, solid advice.

But here’s the truth: even the best in our industry admit there’s no sure-fire secret playbook.

Wil Reynolds put it so well on a recent LinkedIn post,

“The real truth is we don’t know what ranks in AI with the precision that we did in SEO and there’s a chance we never will.”

If Reynolds is willing to say that out loud, maybe the majority of us should too.

What these acronyms actually mean

Let’s break down the acronyms making the rounds. Here’s what they really mean, minus the hype.

AIO: Structuring content so even AI summarizers don’t get lost.
AEO: Crafting content designed to be the answer.
GEO: Aiming to earn generative AI citations.
SEX: The same UX best practices, but rebranded to sell.
ETC: Organizing info so AI and search understand your entities. (Yes, entity SEO… Still here.)

If you’re doing good SEO now, congratulations – you’re already halfway to “AIO” without even trying.

What actually still works

Let’s start with the good news. The rules haven’t changed as much as the hype suggests.

Yes, we need to understand how AI systems ingest, summarize, and present information.

Yes, we should keep learning.

But the best way to show up in those answers is the same as it’s always been: be the best, clearest, most trustworthy source.

Know your audience better than they know themselves.
Write the most helpful, thorough content out there.
Earn links because you’re genuinely worth mentioning.
Build a site that’s fast, accessible, and easy to love.

Optimizing for AI is just optimizing for people – because AI models train on our content , and they reward clarity, authority, and usefulness.

Dig deeper: How to get cited by AI: SEO insights from 8,000 AI citations

How to actually use AI in your SEO work

If there’s one thing I want to be clear about: AI isn’t just a threat.

It’s also an incredibly useful tool, if you take the time to adapt your processes to include it.

We might not know the “secret” to ranking in AI Overviews, but we know how to use AI to do our jobs better.

Here are a few ways I use AI in my processes:

Client education and discovery: Learn about your client’s industry faster. The conversational nature of ChatGPT cuts down on the time needed to search, read, re-search, and develop ideas for strategy, content, and clarifying questions for your client.
Faster and deeper research: Dive deeper into topics, use AI to pull from its massive knowledge base to strengthen your content. Find sources and previously published information to back up your claims. Provide something valuable that your competitors aren’t.
Competitive analysis: Summarize competitor content, identify gaps, and even ask AI to compare multiple webpages on the same topic. This is a great way to understand what your webpage needs to stand out from the crowd.
Content briefs for writers: Generate structured outlines to hand off to freelance or in-house teams. Once you’ve done the research, use AI to speed up the process of organizing it for a writer!

Is this a comprehensive list? No.

If you’ve spent any time toying around with LLMs, you’ve likely already gotten lost down the rabbit hole of their capabilities.

Everything from automating writing tasks to structured data generation and more.

AI tools have simply made great SEOs faster and broadened their abilities.

Is it magic? No.

You still need to check, edit, and think critically.

But AI can save hours of busywork, help you spot ideas faster, and let you focus on real strategy.

Dig deeper: Want to beat AI Overviews? Produce unmistakably human content

How to be the calm voice clients need

Here’s another truth: Many clients are scared right now.

They’re seeing traffic dip, hearing the hype about AI killing search, and worrying about their budgets.

They don’t need someone to sell them the latest acronym. They need someone calm, strategic, and honest.

This is your chance to be that voice:

Help them see the long-term.
Show them real opportunities to improve.
Advise them on diversifying their channels.
Teach them how to create content worth citing – even for AI.
Be the partner who helps them navigate change instead of reacting to it.

Final thoughts

I know it’s easy to get cynical or burned out. I’ve been there myself.

But here’s what I believe: SEO is not dead. It’s demanding more from us.

The work is still here for people willing to focus on the fundamentals.

The clients are still here for those willing to be calm, strategic advisors instead of selling the next acronym.

And honestly? It’s never been a better time to do meaningful work.

SEO leads as 68% of organizations shift strategies for AI search: Survey

Fri, 27 Jun 2025 15:02:00 GMT

As Google, OpenAI, and other AI platforms reshape the future of search, marketers seem unsure whether to run, walk, crawl, or just watch it all happen right now. That’s according to findings from a new BrightEdge survey of 750 search, content, and digital marketers.

“Marketers are caught in the eye of the AI search storm. While 57% are taking a ‘wait and see’ approach, the majority (68%) are already making strategic adjustments, with SEO teams leading the charge,” according to the report.

Why we care. SEO may be leading the AI search parade – but without support from content, PR, and leadership, it’s marching without a band. BrightEdge warns that without cross-functional backing, even the most skilled SEO teams risk turning experiments into dead ends as we transition to the era of generative engine optimization (GEO).

SEO leads the way. 68% of organizations are changing their search strategies right now in response to AI search, according to BrightEdge. And most organizations (54%) are relying on SEO/digital marketing teams to lead AI search initiatives. That’s more than all other departments combined:

Content/editorial: 14%
PR/communications: 8%
IT/engineering: 8%
Leadership/C-suite: 7%
Product/strategy: 6%
No one yet: 3%

However, BrightEdge warned that this “massive concentration of responsibility” leaves teams without the needed cross-functional support for a winning AI strategy.

AI Overviews. Google’s AI Overviews are creating subtle shifts rather than dramatic disruptions to most organizations, according to BrightEdge. Meanwhile, most marketers are optimistic, but also unsure about AI Overviews:

The largest group (57%) is “cautiously optimistic” about AI Overviews.
20% said they’re seeing changes but don’t know where to start.
Only 5% think search visibility “has never looked better” while another 4% are in a state of “complete confusion.”

Multi-platform mindset. AI search doesn’t mean “Google only,” which is why 45% of those taking action are prioritizing strategies around AI Overviews and ChatGPT (27%) or adding tools like Claude and Perplexity (18%).

What they’re saying. BrightEdge advised tracking your brand presence (mentions/citations) across all the generative engines and urged collaboration across teams, before concluding its report with this advice:

“The organizations that can transform cautious optimism into strategic confidence will emerge as the winners in this new search landscape,” according to the report.

The report. SEO Leads the Shift to GEO – Generative Engine Optimization

An AI-powered process to diagnose E-E-A-T issues

Wed, 25 Jun 2025 13:00:00 GMT

E-E-A-T is one of the most used topics in SEO .

However, for a world moving toward an AI SEO-focused future, E-E-A-T may be the only strategy that SEOs can use.

This article tackles why and how to audit – and optimize for – your E-E-A-T.

Why E-E-A-T matters for AI-powered search

E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) was never a ranking factor but rather a guide for website owners to understand the characteristics of sites that Google rewards.

It was based on years of Google quality raters assessing sites and Google understanding the aspects of websites that humans liked and thought were good.

This pattern analysis led to the now widely used term E-E-A-T as a catch-all guidance mechanism that Google employed and advised.

While this mattered for traditional SEO, many still ignored it, opting to buy links at scale or to scale AI content to create a topical authority model that would trick Google’s search signals into rewarding sites.

Each of these ends up with Google applying updates to search that have destroyed many SEO business models.

However, AI-powered search means all of this is essentially meaningless.

The industry is starting from scratch, and E-E-A-T is now the core of generative engine optimization ( GEO ).

Dig deeper: 6 easy ways to adapt your SEO strategy for stronger AI visibility

The new search process (and why E-E-A-T matters)

Perhaps it is best to think of the current search landscape as “Game of Thrones.”

House Google.
House OpenAI.
House Gemini (closely aligned with House Google).
House Perplexity.
House ChatGPT.

Sure, there are others – but the key takeaway is that it’s a war out there.

The reason? We’re on the brink of a new search paradigm, one where AI handles the “knowledge search” for you.

This shift spares users a major cognitive load – and since humans are cognitive misers, always looking to conserve mental energy, every platform knows what’s really at stake.

This isn’t a battle for the best results in search. It’s a battle to become the place search begins.

However, regardless of which platform you search on, the results will be similar.

This is because AI-powered search aims to guide users closer to their goals.

Search now works like this.

Step 1: The user has a problem.
Step 2: The user requires additional information to resolve the problem (this is known as knowledge search).
Step 3: AI conducts the knowledge search.
Step 4: AI presents the knowledge needed to solve the problem.
Step 5: AI presents options for solving the problem, such as tasks, products, or services.

In the past, Steps 3 and 4 were completed by the user.

They would then apply the knowledge and begin to identify the products or services required to solve the problem.

The user was heavily involved in knowledge search.

AI changes this and takes care of most of the searching for us.

Any SEO tactic involving knowledge search is largely redundant.

Today, you need to focus on being the product or service that AI-powered search recommends at the end.

Understanding how E-E-A-T works in GEO

GEO or AI-powered search works in two distinct ways.

An AI-powered NLP system will have training data they have stored within its model. This training data is updated periodically, and they now conduct real-time web search.

However, unlike traditional SEO, these systems can read multiple pages in split seconds. I refer to this as “scooping.”

Whereas previously we used to say that the best place to hide a body was on Page 2 of Google, today being on Page 2 or 3 can offer massive business opportunities.

This is because AI-powered search is highly personalized based on custom instructions and the system’s memory.

For ease, I suggest thinking of search in two brackets.

Knowledge search.
Solution search.

AI will conduct a knowledge search on behalf of the user, presenting that information as required.

It will then conduct a solution search based on the knowledge obtained and the user’s instructions.

And it’s here that AI-powered search will identify the products and services that best suit the user’s needs to solve their specific problem.

Again, this will involve using their internal data, which may be biased based on partner affiliations such as affiliate deals, and they will also conduct real-time web search.

For SEOs, you’re no longer optimizing for rankings; you’re optimizing for attributes that highlight you’re a good choice.

And it’s these attributes that you can roughly translate to E-E-A-T.

This is why it’s now essential that SEOs become obsessed with E-E-A-T.

To get started, we’ll show you how to diagnose this.

Common E-E-A-T issues

Where do you start to diagnose issues relating to experience, expertise, authoritativeness, and trustworthiness?

The first thing is to take a deep gulp and head to Google Trends.

Whack in your brand name and see what comes up.

Nothing? OK, that’s telling you that you haven’t made a dent in search. Now, being really honest, that’s not uncommon.

However, it should serve as a reality check that you need to do more brand marketing to be searched for by a large enough volume to appear in trends.

This isn’t really an SEO job. It comes down to a mix of internal budget and marketing activity.

Good marketing creates a solid link and mentions network around your business website.

But we’ll come back to this later.

E-E-A-T is a brand thing

Once you’ve recovered from that sting, head to the SERP and type in your brand name.

What shows up?

A knowledge panel ?

Nothing?

Or just your website or socials?

Fingers crossed all of the above, but also, it depends on your brand name.

If your brand name doesn’t show up, type in your brand name plus what you do.

So you’d search “brand name” + it’s core activity, such as “Smiths Accountants.”

If that doesn’t work, you’ve got brand issues, but generally, that’ll work.

Next, head to ChatGPT or other AI platforms and repeat.

However, add a “what is” before your brand name.

I recommend using a model such as ChatGPT o3, as it conducts more advanced research for you.

I also suggest opening the thoughts and activity section to view your citations and see what the AI system searched for.

Breaking down the key points in this search, ChatGPT is looking at:

Basic explanation.
Business performance.
Business performance related to the market.
Wikipedia search.
About page information.
Revenue.
Store count.
History.
Employee numbers.
Business strategy.
Publicity/ news citations.

You can see that AI deploys a model in a manner similar to how humans approach a task.

And this is how you should start to diagnose your E-E-A-T issues.

Dig deeper: Decoding Google’s E-E-A-T – A comprehensive guide to quality assessment signals

E-E-A-T diagnosis prompt

These days, an SEO article wouldn’t be an SEO article without a prompt, and here’s one to diagnose E-E-A-T issues with ChatGPT (adapt as you see fit).

A prompt like the above one will give you some ideas, but you should be thinking deeper than its likely returns.

Don’t choke on what you E-E-A-T

The thing with E-E-A-T. It’s genuinely hard to fake and almost certainly goes beyond pure SEO.

It’s Google and AI-powered search asking you to demonstrate your E-E-A-T.

It wants you to help it determine who you are and where to place you in its vast knowledge base.

I like to think of this as an expert witness on a stand giving evidence to a court.

You’ve told the judge who you are, and now the lawyer has asked you to explain your credentials to the judge and jury.

We all know that to be an expert witness, there are a lot of hoops to jump through.

You must prove and demonstrate that you are a specialist and expert in your field. E-E-A-T is no different.

What are your:

Qualifications.
Awards.
Policies
Testimonials.
Career history.
Case studies.
Published papers.
Media mentions.
Location
Contact information.

I like to suggest to website owners that in the world of E-E-A-T you need to be a showoff.

There is no time to be humble when it comes to E-E-A-T. It’s you proving you’re the best with tangible assets.

Now, his article isn’t long enough to dive deep, but here’s a simple framework.

Ask the client why they’re awesome.
Then, ask the client to prove it with evidence.

This process will help the SEO agency to build out the elements that AI-powered search and traditional search love.

Deeper E-E-A-T diagnosis

There’s no doubt that E-E-A-T isn’t just about how legit you are. It goes beyond this.

I like to use another simple framework.

Evidence from the past + evidence for the present = E-E-A-T

The evidence from the past is everything you’ve done so far for your brand or business to be classed as “authoritative.”

It’s the list we discussed earlier, but the evidence for the present is different.

This is the marketing you’re doing to show the world you’re an expert.

This list includes:

Creative advertising.
Helpful content for search.
Helpful content, not for search.
New insights and data creation.
Publicity.
Book writing.
Guest posts (on real sites with real traffic).
Developing your authorship profiles.
Podcast production.
Podcast appearances.
Links.
Media mentions.
Social media content.
Social media perspectives (others talking about you).

Budget dictates what you can do, but budget typically revolves a lot around time.

And that’s the thing with E-E-A-T. It’s not really about ROI. It’s about brand marketing; very often, brand marketing won’t fall into your remit.

For an SEO, you can give a client a list of things to do, but much will be out of your control.

And so, what you can control will tend to be:

Links.
Publicity.
Media mentions.
On-site content.

Digital PR takes care of links, media mentions, and publicity in one nice and tidy package.

Content is the other thing you can control.

But creating helpful content is a whole other subject area.

E-E-A-T may emerge as the only viable business model for the search industry

Ultimately, E-E-A-T is simply good marketing communication, and what works for humans will also work for AI.

Dig deeper: The myth of manufacturing author E-E-A-T

How to make your AI-generated content sound more human

Tue, 24 Jun 2025 12:00:00 GMT

Generative AI tools like ChatGPT can create content, but it doesn’t always sound natural.

Without the right guidance, the content can come across as dull or robotic.

Fortunately, there are techniques for making AI-written text more human-like, engaging, and fun to read.

This article explores ways to get more life-like, conversational content from AI.

Specifically, we’ll look at how to customize the prompts you give ChatGPT so it better understands the tone, emotion, and audience you want to target.

Prep your ChatGPT environment for stronger output

You need a user-friendly interface to interact with AI and refine the content it generates.

Most people are more familiar with OpenAI’s ChatGPT than other AI platforms (though you could also check out Anthropic’s Claude or Perplexity ).

In this article, I’ll focus on guidance for ChatGPT, though many of these tips will likely be useful across multiple AIs.

To get the most out of the tool, sign up for ChatGPT Plus, which costs just $20 per month.

Subscribing gives you access to OpenAI’s more powerful models, including:

GPT-4o.
GPT-4.1.
GPT-4.5.

Though slower than GPT-4o, GPT-4.1 and GPT-4.5 offer deeper reasoning and often generate longer, higher-quality content.

If you want faster responses but still prefer something more advanced than GPT-4o, the subscription also gives you access to o3 and o4-mini.

If you were generating tens of thousands of short snippets via OpenAI’s API, GPT-4o might be your best bet due to its speed.

But for our purposes, I recommend using GPT-4.1 or GPT-4.5. You could also experiment with o3.

Once you’re logged into ChatGPT and subscribed to ChatGPT Plus, you can select GPT-4.5 as your model of choice.

Dig deeper: How to blend AI and human input in your content approach

Let ChatGPT help you sound less like AI

You can begin by asking ChatGPT directly for help in terms of building your prompts (chat messages) so that they result in more human-like output:

Several of these listed items could prove extremely useful to us. Let’s explore this in more detail.

Building a sample blog post

You can start out with a simple prompt such as:

“Please write a blog post on the benefits of solar energy instead of wind farms.”

ChatGPT will have a go at producing something, even without too much direction:

This is fine, but it’s not too detailed and could sound “more” human.

It’s also too short and lacks defined subsections and subheadings. So far, we have defined:

A topic.
A content type (e.g., blog post).

What else can we define? Here are some key options:

Ask ChatGPT to include headings and subheadings for better structure.
Add specific details. For example, the author’s goal is to persuade authorities to build more solar farms, since wind farms are often noisy and visually intrusive. You could also note that solar farms may be easier to maintain, as they have no moving parts.
Request a conversational tone – technical and informative, but with a bit of creative flair to avoid sounding dry.
Aim for emotional impact. For instance, you might want readers to strongly support solar energy over wind power.
Ask for examples and analogies to make the writing more relatable, or suggest using a storytelling approach to increase engagement.
Define the audience so ChatGPT can tailor the reading level. In this case, the audience could include the general public and renewable energy decision-makers, so the content should be clear but persuasive.
Set a target word count. ChatGPT won’t be exact, but it usually gets close. Here, aim for 1,000 to 1,200 words.

Adjusting and enriching your prompt

Now that we’ve identified key details to include, it’s time to put them to work.

To do that, we’ll need to fully restructure the prompt to incorporate all the essential elements.

As you can see from the above, we have accounted for all the details and elements we wished to add to our prompt.

There’s a lot more detail here than there was at the start.

The prompt has expanded from 15 to 220 words (over 1,100 characters). This is roughly what you should be aiming for. Now it’s time to process our new prompt.

Dig deeper: Advanced AI prompt engineering strategies for SEO

Results from an enriched human-centric prompt

It’s immediately obvious that the AI is responding in a more human, more structured way, complete with formatted headings:

You can view the complete 930-word blog post here .

It isn’t quite as long as we wanted, but it’s close to our target length (ballpark accurate).

Iterating and refining the content

Remember that you’re interacting with AI via a chat interface to iterate and refine the AI’s output.

You could process follow-up prompts such as:

“This is really great, but it’s not quite long enough. Can you expand on a few points to make the word count slightly longer?”
“I really like this post, but I feel that the writing tone is too informal. Please make the content sound a bit more formal and fact-driven.”
“Please rewrite the post in the style of [famous editor or well-known columnist name].”
“Please add a bullet point summary at the top of the post.”

As you can see, there are a number of ways to refine your AI-generated content:

Once you are done refining, your content is complete.

You can enhance it with your human creative spice, fact-check the produced content, and ensure that formatting matches your expectations.

Dig deeper: The art of AI-enhanced content: 8 ways to keep human creativity front and center

Alternative AI platforms and chatbots

ChatGPT isn’t your only option:

Claude by Anthropic : While often slower than OpenAI’s models, Claude (e.g., Claude 3.7 Sonnet, Claude 4 Sonnet) excels at writing. For long-form content, I sometimes prefer it over ChatGPT.
Perplexity : A solid choice, especially for research-driven queries.
Sonar : Built by the team behind Perplexity, Sonar is particularly strong for coding tasks.
Google’s Gemini : Evolved from the Bard project, Gemini can sometimes process larger inputs more quickly (depending on the model). That said, its output isn’t always as strong.

Don’t overlook the limits of AI-generated content

AI can be incredibly helpful, but it still has blind spots.

You’ll often need to fact-check or apply a human editorial lens to its output. One recurring issue is hallucination, where AI infers details that weren’t provided.

For example, if you ask it to include specs for a brand-new product, it might substitute data from a similar item it already “knows,” leading to inaccuracies.

In 2025, retrieval-augmented generation (RAG) helps reduce this by enabling AI to fetch real-time information from the web.

However, RAG has its own risks.

AI crawlers aren’t as sophisticated as traditional search engines, so they may surface misinformation, spam, or low-quality sources.

That’s why a human-in-the-loop process is still essential.

Also, avoid overly abstract or ambiguous prompts. AI performs best when it is given clear, grounded direction.

Enhancing AI-generated outputs

To get better results from AI:

Lean on the most advanced models available (e.g., GPT-4.5 or Claude Sonnet 4), especially for long-form content. These tools are more capable of producing detailed, natural-sounding output.
Refine prompts for human-centric results : Be specific. Include the article’s purpose, desired tone (e.g., informative but conversational), emotional impact, intended audience, and any relevant examples. For instance, you might ask the AI to write persuasively about solar energy while gently contrasting it with wind power’s drawbacks.
Use the iterative nature of AI : Don’t expect perfection on the first try. Prompt, review, and revise. You might start with a general draft, then refine for tone, structure, or clarity – just as you would when collaborating with a human writer.
Recognize where AI needs support : Even strong models can produce generic, vague, or factually shaky content. You’ll often need to fact-check and apply a human editorial lens. That final polish makes the difference between usable and publishable.
Apply AI to a range of content types : It’s not just for blog posts. Use it for internal documentation, onboarding materials, and even print assets. Just tailor your prompt to suit the format and channel.
Aim for emotional and visual resonance : Look for content that evokes feelings or paints a picture. A comparison, anecdote, or metaphor can make content more human.
Balance technical accuracy with creative flair : Complex ideas shouldn’t come across as dry or inaccessible. Direct the AI to use everyday language alongside clear explanations or analogies.
Encourage narrative and example-based structure : A storytelling approach – anchored by real-world examples or step-by-step breakdowns – often results in more compelling and memorable output.
Adjust for audience and use case : Whether your readers are decision-makers in clean energy, everyday consumers, or technical professionals, make sure the AI adapts accordingly. One-size-fits-all won’t cut it.

And if you’re not using AI for full-length articles? It’s still incredibly useful for summarizing, reformatting, or condensing human-written material – quickly and efficiently.

Dig deeper: 3 ways to add a human touch to AI-generated content

AI agents in PPC: What to know and build today

Mon, 23 Jun 2025 16:53:00 GMT

AI in paid search isn’t a future prediction; it’s already reshaping how campaigns are managed.

AI agent tools are now capable of transforming your PPC workflows.

The question isn’t if AI will change PPC management.

It’s which tools you should start using now, and what you can build today to make tomorrow’s work faster, smarter, and easier.

Scripts vs. automation vs. agents: Understanding the evolution

Before diving into specific tools, it’s crucial to understand where AI agents fit in the spectrum of PPC management solutions.

The progression from scripts to agents represents a fundamental shift in how we approach campaign optimization.

Scripts: The foundation

Google Ads scripts and similar tools are predefined code paths that execute specific actions based on fixed conditions.

They’re powerful for repetitive tasks but limited in scope. They can:

Execute predetermined logic (e.g., if-then statements).
Require explicit programming for every scenario.
Struggle to adapt to unexpected situations.
Excel at rule-based optimizations, like pausing low-performing keywords.

A classic example of script usage is a reporting script that exports daily metrics into a Google Sheet.

Possible extensions are focus reports on Performance Max , Demand Gen , or quality score .

As Google and Microsoft cover hundreds of metrics, there are almost no limits on reporting script ideas.

Another key use case for scripts is automation through action scripts.

For example, you might set up a script to pause campaigns if ROAS falls below 5, or pause keywords with a quality score under 3.

These scripts go beyond reporting; they actually perform tasks, taking some manual work off your plate.

However, they remain limited: their actions are predefined and can only handle specific, recurring scenarios.

While useful, ad scripts offer minimal flexibility for more advanced or adaptive automation.

Automated rules

Rules let you make changes in your account automatically, based on settings and conditions you choose.

You can change your ad status, budget, bids, and more.

For example, you can set a rule to increase your keyword bid whenever your ad drops off Page 1 of the SERPs.

Here’s what automated rules can do:

Execute simple if/then logic (if cost per click rises above $5, then pause keyword).
Monitor specific metrics and trigger actions when thresholds are hit.
Send email alerts when certain conditions happen.
Save time on repetitive manual tasks.

But automated rules have clear limits. They:

Only work with the conditions you set up in advance.
Can’t adapt when unexpected situations happen.
Need manual updates when your strategy changes.
Work within one platform at a time.

Automated rules are similar to action scripts, but they are easier to set up and still offer a lot of value for early automation.

Advanced automation is not possible due to built-in limits.

AI agents: The strategic evolution

AI agents represent a new level of automation.

They’re systems where LLMs dynamically direct their own processes, maintaining control over how they accomplish tasks.

In PPC terms, agents can:

Reason through complex scenarios using natural language understanding.
Plan multi-step strategies across different campaign elements simultaneously.
Adapt approaches based on changing performance patterns and market conditions.
Execute coordinated actions across platforms while maintaining strategic oversight.

Think of the progression this way.

Scripts and rules follow recipes, but agents can become the chef.

They understand your business goals and can craft strategies to achieve them.

Even better, there are few limitations on agents, which means that you can automate almost everything.

A simple AI agent could be a keyword research agent.

Dig deeper: 4 ways to connect your ads data to generative AI for smarter PPC

How AI agents scale: From single tasks to full-funnel automation

Not all AI agents are created equal.

Some handle one task really well, while others coordinate multiple systems across your PPC stack.

Here’s how to think about the different levels – and how to choose the right starting point.

Level 1: Single-task agents

Let’s start with a simple example.

You build an agent that works with Google Keyword Planner and Google Trends. It can pull keyword data and export everything to Google Sheets.

This looks basic, but it’s already solving real problems.

Which metrics matter most?
Do you want all keywords or just the profitable ones?
Should it filter by search volume or competition level?

Depending on your goals, this could be a weekend project or something much bigger.

You might add SEO insights or connect to tools like Semrush or Ahrefs.

Level 2: Multi-agent teams

The next step is building a lead agent that manages other specialized agents. Think of it like a marketing manager with a team of specialists.

Your lead agent might have access to a keyword research agent and a campaign builder agent.

Tell it “create a search campaign for our new product,” and it coordinates both agents to deliver a complete campaign.

One lead agent handles the strategy. The sub-agents handle the execution.

Level 3: Complete marketing operations

Now imagine this approach across all of Google Ads:

Search campaigns.
Performance Max .
Demand Gen .
YouTube ads .
Analytics.
Product feeds.

Each area has its own specialized agents, all coordinated by your lead marketing agent.

This is where things get exciting.

Need to expand internationally? Your lead agent talks to your market research agent and competitor analysis agent for insights.
Starting a new ecommerce account? It builds search campaigns and shopping campaigns automatically.
Working on brand awareness? The creative agent makes ads while the demand gen agent sets up campaigns.

Dig deeper: 4 ways to connect your ads data to generative AI for smarter PPC

Current reality: 3 practical paths forward

The possibilities are endless, but there’s a catch: building everything takes serious time and technical skill.

You need a starting point that gives you quick wins while you learn what works.

That’s why most successful agent implementations start small and grow over time.

Claude MCP for Google Ads: Conversational campaign management

Anthropic’s Model Context Protocol (MCP) for Google Ads represents a breakthrough in how we can interact with campaign data.

Rather than navigating through multiple dashboards and reports, marketers can now query their Google Ads data conversationally and receive intelligent analysis.

What MCP enables:

Natural language queries like “Show me which keywords are driving the highest ROAS but have impression share below 80%.”
Automated analysis of performance patterns across campaigns.
Intelligent recommendations based on account-specific data and industry benchmarks.
Seamless data extraction for deeper analysis in other tools.

Building applications or agents around MCP is a first step for much faster analysis and reporting.

Google ADK: Building custom PPC intelligence

Google’s ADK takes customization to the next level, allowing marketers to build specialized agents tailored to their specific business logic and KPIs.

Key ADK capabilities:

Direct integration with Google Ads API for real-time data access.
Custom decision-making logic based on your business rules.
Multi-platform coordination (Google Ads, Analytics, Search Console).
Automated reporting and stakeholder communication.

ADK is the first big step into automated PPC management.

As shown before, ADK allows users to quickly build sub-agent structures that are managed by a lead agent.

Tutorials, templates, and lots of documentation make it easy to start with basic ideas and develop complex systems over time.

Open-source agent frameworks: Build your own intelligence

For teams with development resources, open-source frameworks like CrewAI, LangChain, and AutoGen offer complete control over agent behavior and integration capabilities.

What these frameworks provide:

CrewAI : Specialized for multi-agent teams where different agents handle specific PPC tasks (one for keyword research, another for bid management, another for creative testing).
LangChain : Advanced toolkit for building custom agents with memory, planning, and tool integration.
AutoGen : Focus on multi-agent conversations and collaborative problem-solving.

If Google ADK is something you want to leave out, there are plenty of other agent frameworks to start with.

When to consider this path:

You have dedicated development resources.
Your business logic is too complex for off-the-shelf solutions.
You need agents that work with proprietary tools or data sources.
You want complete control over agent behavior and decision making.

The technical reality: What you need to know

Implementation and costs

Both Claude MCP and Google ADK require technical setup, but the barriers are lower than many marketers expect.

Claude MCP involves API access and basic configuration, something most marketing teams can handle with minimal support.
Google ADK is more complex and typically requires developer involvement during initial setup, but it offers significantly more customization.

API costs are usually manageable, but the real investment lies in strategic planning and the ongoing refinement of agent behavior.

It’s also important to note that the success of AI agents depends heavily on data quality and volume – accounts with limited history or poor conversion tracking may see weaker results.

Strategic implementation: Lessons from the field

The best approach is to start small and think big.

Begin with a use case where manual work is time-consuming but the logic is well-defined, like keyword bid management or budget reallocation.

Clearly define your success metrics up front; while AI agents can optimize for almost any goal, they need priorities before they’re deployed, not after.

And while agents can execute complex decisions, they still require human oversight.

Regular reviews, strategic guidance, and override mechanisms are essential for sustainable performance.

The bottom line for PPC marketers

AI agents in PPC aren’t futuristic concepts – they’re active tools creating real competitive advantages today.

The technology is here. The APIs are available.

The real question is: Will you lead or lag behind?

For those ready to dive in, start with Claude MCP to get comfortable with conversational campaign management.

As your needs grow, explore Google ADK for more advanced, custom agent development.

A smart starting point?

Rebuild the automated rules or scripts you’re already using, then expand into routine tasks like keyword research, trend monitoring, or competitive analysis.

Whatever takes up time and follows repeatable logic is ripe for automation.

Dig deeper: Top AI tools and tactics you should be using in PPC

If AI killed your SEO strategy, you didn’t have one by Victorious

Mon, 23 Jun 2025 11:00:00 GMT

AI hasn’t broken SEO. It has exposed it.

The shortcuts, the content mills, the over-optimized FAQs – all fragile tactics built to game a system instead of serve an audience.

While many have decried a loss of search visibility and blamed AI, the fact is that AI didn’t invent new rules. It’s just finally enforcing the ones that always mattered. So while those who took the easy way out complain about AI, brands that prioritized intent, quality, and long-term value are holding steady or even gaining. One study showed drops of 64% and gains of 219%, depending on the approach.

If AI tanked your traffic overnight, you aren’t alone, and it doesn’t mean you failed.

To be clear, the problem wasn’t AI. The problem was the strategy holding it all together. And not because you didn’t know better, but because the system rewarded fragility. Thin content and scaled sameness did well. Until they didn’t.

At Victorious, we guide customers through this shift every day, running retrieval simulations, identifying why good content gets skipped, and restructuring pages to align with human intent and AI logic. The strategies built on substance are holding up. The ones built for short-term wins are showing their limits.

SEO shortcuts are failing, again

This isn’t the first time SEO tactics have hit a wall.

For years, some SEO teams got by on mechanics: near-duplicate blog posts chasing long-tail variations, ultra-basic FAQ pages aimed at featured snippets, bloated templates stuffed with exact-match keywords.

But that wasn’t strategy. It was structural support built for a different time.

Now, AI is answering questions directly, and Google is no longer rewarding content that just checks the boxes. If it doesn’t offer something original or isn’t clearly aligned with intent, it’s not penalized; it’s ignored.

This is the new filter: If your content wouldn’t help someone make a decision or deepen their understanding, it probably won’t show up. That’s AI raising the bar.

Search behavior is changing, and AI is leading the shift

Search isn’t just 10 blue links and a featured snippet anymore. AI Overviews, Bing summaries, and tools like ChatGPT are reshaping how results are delivered and how people search.

With more choices, user behavior is evolving. In late 2024, more than 25 percent of U.S. consumers said they were using tools like ChatGPT instead of Google for certain queries. AI is pulling search into new environments and cutting into the clicks that traditional SEO relied on.

A user looking for “how to negotiate startup equity” might have a conversation with ChatGPT instead of running a search. It’s faster, it’s more personal, and it feels more trustworthy.

That’s why Google is doubling down on AI, accelerating investment to stay ahead as search behavior evolves.

But this isn’t just a Google problem. SEOs and marketers need to expand their definition of SEO. It’s not just about ranking: It’s about showing up wherever your audience is learning and making decisions.

Unfortunately, the path isn’t always clear. We’ve seen well-structured, helpful content still get skipped by AI Overviews and poor content get featured. And of course, there’s volatility. But over time, the pattern is clear: Content that anticipates intent, adds original value, and earns trust tends to hold up.

The fundamentals haven’t changed. Serve your audience better than anyone else, and you’re still in the game.

A framework for building SEO that survives AI

The shortcuts are gone. So what actually works?

At Victorious, we’ve stress-tested these principles across hundreds of SEO programs, seeing firsthand what holds up in AI-driven search and what collapses. This isn’t about chasing perfection. It’s about building visibility that lasts, even as search evolves.

This framework assumes you’ve already handled the technical foundation, or that you’re working with a partner who has. Without enhanced crawlability and a clean structure, even the best content can disappear.

The following principles are simple, but the execution takes discipline, experience, and the right support.

1. Align SEO with user intent and the customer journey

Start with searcher intent. Every query is a question or a goal. Your job is to answer it or help the user achieve it.

Group queries by type:

Informational (learning)
Commercial (comparing)
Transactional (acting)
Navigational (finding)

Then map your content to match:

Top of funnel : guides, how-tos, research
Mid-funnel : comparisons, case studies, testimonials
Bottom of funnel : product and service pages with strong CTAs

Use Google Search Console or your analytics platform to segment keywords by intent, then fill the gaps. If a high-intent query isn’t addressed on your site, build something better than what’s out there.

Zoom out and layer in the journey stage. Content should walk people from awareness to action. If AI takes the top-funnel summary, your job is to own the deeper click. Intent mapping isn’t just for visibility. It’s how you guide real people from interest to action and earn their trust along the way.

And here’s what often gets missed: SEO done right doesn’t just drive traffic, it drives the performance of everything else. It:

Makes paid campaigns more efficient.
Fills the gap between email sends.
Answers questions that sales teams keep getting.

At Victorious, we design SEO strategies to amplify the rest of your marketing mix, not compete with it.

2. Double down on quality, expertise, and trust

Google’s AI Overviews favor content that’s clear, credible, and comprehensive. That means:

Expert voices : Feature real people with real experience. Add bios to your site and quote SMEs in your content so your content speaks with authority.
Original insights : Use internal data, unique case studies, or real customer stories to showcase your work. Generic content won’t make the cut. Google’s AI is designed to skip it.
Comprehensiveness : Don’t make pieces long for the sake of it, but be sure they fully answer the search intent. Anticipate follow-up questions and link to next steps.
Trust signals : Make it clear you’re legit with a secure site, clean design, clear authorship, up-to-date content, and testimonials.

Most importantly: Don’t chase scale by churning out low-quality AI content. It will hurt more than help.

Authority isn’t something you claim, it’s something you prove. Now more than ever, that proof must appear in your content.

3. Focus on conversions and value, not just traffic volume

AI might reduce traffic volume , but it can raise traffic quality . One high-intent click that converts is worth more than a hundred that bounce. That’s a trade worth taking.

Traffic is a vanity metric without conversions. If your SEO program still reports wins in terms of sessions or rankings alone, it’s time to update the dashboard.

Stakeholders want numbers that translate into pipeline and proof of return. Your SEO strategy has to speak in terms of leads, conversions, and revenue, not just sessions and CTR.

Measure:

Leads and revenue attributable to organic search.
Conversion rates on key organic landing pages.
Share of total conversions from organic.

At the same time, expect analytics to get messier. Some AI-driven visits show up as direct or referral traffic, so use blended attribution models and focus on the bigger picture. If your SEO is driving growth, that’s what matters.

4. Embrace change and integrate AI (don’t fight it)

AI isn’t a passing phase. Google, Bing, OpenAI, and everyone else are betting big on it. So should you. Here are three ways you can do this:

1. Use AI tools to scale responsibly:

Analyze large keyword or data sets.
Cluster queries by intent.
Draft content outlines (but keep a human in the loop).

2. Adapt how you publish:

Structure content clearly for readers and AI.
Use paragraph-level answers for key questions.
Add schema markup and structured data when appropriate.

3. Think beyond the Google SERP:

Explore ChatGPT plugins or custom bots trained on your site content.
Create content for YouTube, Reddit, TikTok, and other discovery platforms.

Most of all, stay agile. Set aside time and budget for SEO R&D. Pilot ideas. Measure outcomes. Evolve.

AI didn’t kill SEO, it’s forcing us to do it right

This isn’t the end of SEO. It’s the beginning of what’s next.

AI is pushing the industry to mature away from shortcuts and toward what actually drives results.

If you’re a marketer, this is your moment. Toss the checklist playbooks and build something aligned to your specific audience. Because the companies that will come out ahead aren’t clinging to the past. They’ll use AI to see what really matters and double down on it.

SEO has evolved before. Each time, the fundamentals held. AI is forcing another evolution; it’s just happening faster. Your job isn’t to outsmart the algorithm. It’s to be so useful, so clear, and so credible that AI has no choice but to include you.

That’s the kind of visibility we earn for our customers at Victorious, not just through SEO, but through disciplined AEO strategy , continuous testing, and hands-on support. It’s the kind that compounds in an AI-enabled world.

This article was written by Houston Barnett-Gearhart , Director of SEO at Victorious.

44 marketing initiatives where AI alone will fail

Fri, 20 Jun 2025 13:00:00 GMT

Marketers are under pressure to move faster, do more, and cut costs, so it’s no surprise many turn to AI.

But there are still plenty of areas where automation isn’t just risky – it’s a liability.

My colleague Adam Tanguay has already done a stellar job of explaining why you can’t just let AI run your SEO and content .

What brand marketers often need now are specific, practical examples of where unsupervised AI falls short.

The list below isn’t exhaustive – new edge cases emerge daily – but it’s a solid starting point.

Bookmark it as a reference for when and where human judgment, creativity, and critical thinking are still non-negotiable.

Brand-critical copy and messaging

1. Final approval of headlines, slogans, and value-prop statements

Subtle shifts in tone can mis-position a brand, introduce unintended promises, or clash with existing campaigns. Only a human can judge nuance, cultural connotation, and political sensitivity in real time.

2. Long-form thought-leadership articles and bylined pieces

AI can draft, but a true SME must ensure the argument reflects proprietary experience, adds genuinely new insight (anything “new” is beyond AI’s grasp at this point), and aligns with corporate positioning (E-E-A-T).

Legal, compliance and reputation-sensitive outputs

3. Statements that touch on regulated advice (finance, health, privacy, etc.)

AI may hallucinate regulations, cite outdated statutes, or miss jurisdictional nuance, all of which expose the company to legal risk.

4. Crisis communications or sensitive PR responses

Tone, empathy, and fact-checking must be impeccable and empathetic; AI can misinterpret context or use language that escalates rather than diffuses.

Data interpretation and strategic decision-making

5. Root-cause analysis of traffic drops or ranking volatility

This kind of analysis requires correlation across datasets (GSC, GA4, log files, release notes, SERP features) and an understanding of site-specific quirks and market shifts that models don’t “see.”

6. OKR / KPI target setting

Effective targets incorporate seasonality, competitive landscape, resourcing, and business constraints, all of which are contextual factors AI lacks without guided inputs.

7. Attribution-model adjustments and revenue forecasting

Minor formula changes can materially affect budgeting; a strategist must sanity-check assumptions and edge cases.

Link acquisition and digital PR

8. Prospect qualification and outreach personalization

AI can scrape lists, but a human must evaluate site quality, audience fit, previous relationships with the organization, and brand safety (political leanings, spam history) before conducting outreach.

9. Negotiating partnership placements or guest posts

Relationship-building, pricing, and editorial standards require empathy, persuasion, and judgment beyond scripted messages.

10. Determining whether a site is PBN/parasite or legitimate

Requires manual backlink-profile and traffic checks. AI classifiers still mislabel gray-hat networks.

11. Executing broken-link outreach to .gov/.edu domains

Institutional gatekeepers expect personalized, policy-aware pitches.

12. Live spokesperson prep for broadcast interviews

Requires media-training nuance, real-time Q&A rehearsal, and brand-risk coaching.

13. Crisis-response FAQ creation

Brand tone and legal liability make human vetting mandatory.

UX / CRO testing

14. Hypothesis selection for A/B tests

Test ideas must map to user research, funnel friction points, and technical feasibility; AI may propose low-impact or infeasible variations.

15. Final design QA before going live

Visual hierarchy, accessibility, and micro-interaction quality still depend on human eyes (and real devices).

Content quality and factual assurance

16. Stat-driven sections, case-study numbers, or medical claims

AI often fabricates sources or misquotes figures. Humans must verify every stat against primary research.

17. Multi-language copy or cultural localization

Literal translations ignore idioms, taboos, and regional context that affect conversion and brand perception.

Ethical and bias audits

18. Reviewing personas, examples, or imagery for DEI sensitivity

Models can reinforce stereotypes. A diverse human review panel can spot exclusionary language or visuals.

Competitive and market intelligence

19. Interpreting competitor feature launches or funding news

Requires reading SEC filings, founder interviews, or release notes that AI summaries may miss or misinterpret.

20. SWOT and positioning updates

Strategic implications depend on insider knowledge of buyer objections, sales feedback, and roadmap realities.

Technical SEO changes

21. Site-wide architecture modifications (URL migrations, canonical rules)

One misapplied directive can tank organic visibility. Humans must confirm edge-case scenarios in staging and production.

22. Robots.txt or security header edits

An incorrect AI suggestion could deindex critical pages or expose user data.

Stakeholder and executive communications

23. Quarterly business reviews and board-level decks

Must blend storytelling with metrics, anticipate objections, and reflect organizational politics, which carry nuance AI can’t parse. Advanced QBRs and board decks also include forward thinking and projection, which humans are far better equipped to deliver.

Content optimization

24. Updating statistics, legal references, or medical data points

AI frequently mis-dates or fabricates sources; a strategist must verify against primary research and current regulations.

25. Re-ordering H-tag hierarchy after a site-wide template change

Requires live QA to ensure headings still map to design constraints, accessibility, and internal-link logic.

26. Choosing canonical vs. noindex on overlapping assets

Misjudging intent or revenue value can quickly de-rank high-converting pages.

Content ideation and production

27. Predictions, projections, and philosophical content ideation

AI is reactive, not predictive. Only humans can break new ground in content topics and creation.

28. Approving on-the-record quotes from SMEs, executives, or customers

Consent and nuance matter; AI can’t confirm attribution rights or embargoes.

29. Selecting real-world examples or anecdotes

Requires brand-safe judgment; a poorly chosen example can alienate core audiences.

30. Tone-of-voice alignment reviews for different funnel stages

Only humans can sense when an otherwise “perfect” AI paragraph feels off-brand or mismatched to reader sophistication.

Content distribution and promotion

31. Negotiating syndication terms with third-party publishers

Licensing fees, link attributes, and exclusivity windows need human negotiation.

32. Finalizing paid-boost copy for social or native ads

Platform policy nuances (Meta, LinkedIn, TikTok) shift weekly; compliance stakes are high.

33. Selecting hero imagery or video thumbnails

Brand, cultural, and accessibility sensitivities can’t be fully automated.

Conversion rate optimization

34. Interpreting statistical significance for multivariate tests

Requires understanding of business impact, traffic quality, and seasonality that AI can’t infer from raw numbers alone.

35. Mapping experiment insights back to product-roadmap priorities

Only humans can weigh political capital, sprint capacity, and revenue forecasts.

36. GDPR/CCPA review of new data-collection elements

Legal compliance overrules “best-practice” test ideas.

Keyword research

37. Final clustering and naming of content hubs

Needs brand lexicon awareness and cross-team alignment (product, sales).

38. Eliminating negative or brand‐unsafe terms

AI might group “exploit kits” with legitimate “security testing” keywords; human intent review is vital.

39. Balancing search volume vs. sales qualification

Only domain experts know when a high-volume phrase drives the wrong ICP.

Competitive/market research

40. Validating feature-gap grids with product and sales

Public docs often lag reality; humans must confirm roadmap truth.

41. Monitoring rumored M&A or funding rounds

Requires reading paywalled or insider sources that AI training data can’t access.

42. Assessing sentiment in analyst reports (Gartner, Forrester)

Nuanced language (“visionary,” “challenger”) impacts positioning and must be interpreted by strategists.

43. Running voice-of-customer interviews and extracting pains in their own words

Empathy, follow-up probing, and body-language cues are non-automatable.

44. Triangulating TAM/SAM/SOM figures for board decks

Requires proprietary ARR numbers, channel capacity, and realistic penetration scenarios.

Even as the list grows, human judgment holds the line

This list was probably outdated the minute it was published.

People are discovering new AI shortcomings and functionalities by the day to add to or subtract.

Each vertical has its own initiatives to add.

But even as things shift, you get the overall idea.

There are and will always be initiatives that need a strategic, experienced human at the wheel, no matter how valuable AI can prove in doing some of the block-and-tackle work.

SEO KPIs to track and measure success in the age of AI

Wed, 18 Jun 2025 12:00:00 GMT

If something can be measured, it can be improved.

By carefully measuring your SEO with metrics that matter to your business, you can understand where you are currently and what value SEO brings to your marketing.

SEO KPIs connect your marketing objectives to your SEO reporting and should feature prominently in your SEO plan .

In a time of rapid change – when both search engines and users are embracing AI – tracking your SEO and organic performance is more critical than ever.

This article breaks down the key SEO metrics and KPIs you need to keep up with, stay ahead of, and succeed in this new era of AI-driven search.

What are KPIs?

A KPI is a key performance indicator. This is a quantifiable measurement of performance in relation to a specific objective.

KPIs help you define targets and milestones and measure progress toward those goals.

What are SEO KPIs?

SEO KPIs are metrics used to measure SEO performance.

Typically, SEO KPIs track visibility or traffic and how it fares in relation to your SEO goals .

SEO KPIs should help you understand your visibility, traffic, and the real-world performance of your SEO approach (leads and sales).

SEO KPIs in the age of AI search

Search is changing. The integration of AI answers and a full conversational AI Mode will allow users to interact with search engines in ways never before possible.

This sudden and accelerating change will create new challenges for marketers, but also new opportunities.

It is important not to lose sight of the fundamentals when measuring SEO.

AI is moving fast – and it matters. Your job is to stay sharp:

Watch what’s happening.
Learn what’s working.
Use AI where it gives you an edge.

Start by tracking AI metrics wherever you can. This helps you spot problems early and uncover opportunities.

But don’t lose sight of what already works. Proven, reliable data still drives smart decisions.

The SEO KPIs below are your foundation.

Focus on these core metrics, and layer in AI insights as they become available.

This is how you prepare for the next era of search: clear-eyed, informed, and strategically focused on what drives results.

Using SEO KPIs

Careful measurement and tracking act as a control system, helping ensure:

Your tactics drive real results.
Your time is focused on what truly moves the needle.

In times of rapid change, it’s just as important to spot what’s not working as it is to track what’s gaining traction. KPIs give you that visibility.

Staying on top of both traditional and AI-driven SEO KPIs is essential if you want to operate as a modern SEO .

This isn’t just about rankings – it’s about integrating SEO into your broader marketing strategy to ensure your website and business thrive in the age of AI-powered search.

It is also important that KPIs can be tiered to provide understandable metrics for all stakeholders.

The marketing team will need more details to ensure time is spent wisely on SEO vs. PPC , but the board may only need to see top-level performance figures (like revenue from search).

Tailoring the KPIs to your situation and the needs of all key stakeholders is essential to creating truly useful SEO KPIs.

Objectives and KPIs

KPIs are a measurement criterion for your objectives.

SEO KPIs track progress toward your goals and show whether your efforts are delivering the real-world results you expected or not.

Good KPIs should challenge you to ask: Is your SEO strategy actually driving performance? And if not, what needs to change?

SEO KPI process

The following is a simple process that you can use to determine your SEO goals and KPIs.

Step 1: Determine your SEO goals

The first job is to identify your SEO objectives. What are the important tasks you need to achieve now, in a month, three months, six months, and a year?

The SEO goals should connect to established organizational goals, and you should clearly articulate how the SEO goals drive the overall business goals.

I suggest three to five goals initially to keep focus.

You should then define your SEO goals using the SMART goals system .

Alternatively, you can use the OKR approach to objectives and key results detailed in “Measure What Matters” by John Doerr.

This simple system was used by Google, Microsoft, and many other huge companies to simplify goal-setting to drive massive growth.

The right approach will depend upon your business, but whatever you choose, your goals should be relevant, measurable, achievable, and time-bound.

Step 2: Define what you will measure

The next step is to define what you will measure.

Your measurement metrics can be thought of as steps, and each should take you closer to the overall goal. As with the goals, you want 3–5 measurement metrics for each goal.

When your goals are achieved, you should be able to measure performance to determine if your strategy here is valid.

Step 3: Decide on the length of your cycle

As a general rule of thumb, SEO metrics are tracked monthly.

There are exceptions to this rule. Keyword rankings may be tracked daily (but reviewed monthly), and you may also want quarterly, bi-yearly, and annual reports.

Remember, with SEO, you may only see tangible results like leads and sales when your keywords rank highly and drive traffic.

Factor progress toward the goal into your overall cycle length.

Step 4: Review and reflect

The goal is to review each KPI and, ideally, score performance.

What is working?
What can you do differently next month?

The purpose of KPIs is to help you review your approach and think critically about what is or is not working.

A highly ranked organic keyword in modern search may not drive the expected traffic numbers.

Think of this as an experiment. Your KPIs are there to test the validity of your hypothesis.

Be flexible, and be ready to adapt and adjust your tactics based on the results.

Example

Objective : To sell more kitchens.
- KPI 1 : Increase rank for kitchen keywords.
- KPI 2 : Increase organic traffic on kitchen pages.
- KPI 3 : Improve engagement rates.
- KPI 4 : Increase organic conversions.

Here we have a simple objective measured with four simple SEO KPIs. This does not have to be complicated!

Following this approach, you will create three to five relevant SEO goals that you can easily measure with three to five SEO KPIs each.

Dig deeper: How to benchmark your SEO performance in 2025

Example SEO KPIs

There is no shortage of SEO metrics for tracking your campaigns.

Data points can be found in free SEO tools like Google Search Console and Google Analytics or in the many commercial SEO tools.

Unfortunately, while we have lots of data in its native format, commercial SEO tools and even Google Analytics often do more to confuse than deliver insight.

The purpose of SEO KPIs is to make those metrics useful.

We do that with the four-step model, where we connect SEO metrics to SEO goals connected to the broader business and marketing goals.

The following is a categorized list of SEO metrics to build your SEO KPIs.

Remember, don’t just blindly use the metrics below. Connect them to your SEO goals in groups of three to five to drive insight and understanding.

1. SEO keywords

First up is the old SEO stalwart of keywords.

Tracking keywords for SEO is complicated nowadays as search results have so many elements.

But it is still important to show progress toward your goals and get a sanity check on how much traffic a given keyword will deliver.

Track keywords for progress and verify your strategy.

Modern search results will include:

Local pack.
Organic listings.
Featured snippets.
Sitelinks.
Knowledge cards and panels.
Videos.
People also ask.
Video previews.

You may also want to consider:

By location.
By country.
Search Console CTR.
Search Console Average Rank.
Search Console Clicks.
Search Console Impressions.

When tracking keywords, combine multiple factors to give the complete picture.

At the very least, track rankings, organic impressions and clicks (from Google Search Console or Bing Webmaster Tools).

Depending on the SERP layout, a top 5 keyword can still do very little in actual clicks.

As time progresses, with this level of detail in your keyword KPIs, you will learn what works and what does not, which will feed into your strategy and goal setting.

Tip: Remember, rankings are just one factor. When working on your web design and SEO , make sure the content that ranks is a good fit for the intent behind the keyword.

2. SEO metrics

These are the traditional SEO metrics provided by various SEO tools.

Most of these metrics grew out of the hysteria around Google PageRank and aim to provide a general authority score at a site and page level.

These metrics can be helpful, but they mean very little alone, and they need to be compared to competitor values to provide accurate context.

You should measure whether your site’s perceived trust and authority will correlate with an increase in the rank for your main search terms and the amount of organic traffic received.

These metrics are important, but do not talk in the language of the business, so their prioritization in your reports will depend on the stakeholders’ understanding of SEO.

Majestic Citation Flow.
Majestic Trust Flow.
Majestic Trust and Citation Balance.
Moz Domain Authority.
Moz Page Authority.
Moz Spam Score.

Tip: Remember that these specific SEO metrics should be tied to the overarching goals and strategy.

For example, suppose you want to build traffic to your upper-funnel marketing content.

In that case, you may want to improve site authority, but remember to sanity check improvements against real-world metrics (rankings, traffic, etc).

3. Real-world SEO KPIs

While many SEO KPIs relate to perceived authority and keyword rankings, what really matters is whether this is making a difference.

Are we getting more traffic? Is that generating more leads?
Is the cost per lead coming down vs. other channels?
Are we seeing more branded searches due to raised awareness?

This is where the SEO rubber hits the SERP road, folks, so these metrics are essential.

Increase in organic traffic.
Increase in the number of pages on the site that generate traffic.
Increase in non-branded search traffic.
Percentage increase in traffic from specific geographic regions.
Organic Impressions (Search Console).
Organic Click-Through Rate (CTR) (Search Console).

Tip: We would also like to see real-world SEO metrics increase as SEO metrics improve.

This ensures that your work helps move the dial rather than just satisfying the whim of an SEO tool.

4. Conversion KPIs

In most cases, the most important SEO KPI is conversion. Tracking conversions from organic search should be at the top of your list.

These KPIs need to be customized around your specific goals. Some examples here include:

Conversions from organic search.
Percentage increase in conversion rate.
Organic search conversion rate.
Sales conversions.
Lead conversions.
Downloads.
Email clicks.
Phone number clicks.
Form fills.
Cost per acquisition (CPA).

Note:

Calculating and tracking CPA for organic search allows you to compare the return from organic with other channels. This helps determine if you are spending your precious marketing budget wisely.
Branded search traffic can easily skew these figures, so keep a close eye on branded search traffic in the search console along with user journeys in GA4 and factor that into your conclusions.

Tip: The specifics here really depend on your business and marketing objectives, so ensure you track these in GA4.

5. Engagement KPIs

Google Analytics 4 has three key engagement metrics:

Average engagement time (over the date range).
Engaged sessions per user.
Average engagement time per session.

Tip : Tracking engagement as a general KPI but also for all channels allows you to evaluate the quality of traffic from SEO vs. PPC, social, etc. Again, this helps determine if you are spending your time and efforts on the right tactics.

6. Referral traffic

A solid SEO campaign can impact your business beyond driving more organic traffic.

If content and links are vital to your strategy, this exposure can drive more high-quality referral traffic, so it is prudent to demonstrate the extra value here.

This helps to illustrate how your SEO strategies have multiple marketing benefits.

Often, solid referral traffic can convert at a better rate than organic search traffic, so ignore this at your peril.

Percentage increase in referral traffic.
Percentage increase in referral conversions.
Percentage increase in engagement metrics (bounce, pages, time).

Tip: The links that will build authority will also drive referral traffic, so factor this into your qualitative measurement.

7. Brand impact

Improved search visibility equates to improved overall visibility – advertising by another name.

Look at the increase in branded search traffic and brand mentions and how this correlates to your work.

Percentage increase in branded search traffic.
Percentage increase in brand mentions.
Visibility on third-party sites.

The visibility on third-party sites is another way for your audience to discover you.

For example, more people will discover me and my business by writing and publishing this article here on Search Engine Land.

If you do this kind of marketing, then ensure you measure the impact.

Tracking how many people search for you by name helps you understand and measure overall recognition.

Comparing these values to your main competitors helps you measure relative market awareness.

Note: AI will often provide brand mentions , and this can also drive a spike in branded traffic. This tallies with the uptick we are seeing in homepage traffic vs. the general dip in traffic to informational content.

Dig deeper: The new SEO imperative: Building your brand

8. Link building KPIs

Links are still one of the primary drivers of search engine visibility, and links (plus content) are often the main tangible element of a long-term SEO campaign.

We must still report on the total number of links from authority sites and those from highly relevant sites.

These will generally be pulled from our link wishlist and should be customized around your client’s industry.

Total links.
Total links gained this time period.
Number of links from authority sites.
Number of links from relevant sites.
Links lost.

To drive exposure in AI, getting coverage in highly visible sites will only help drive awareness with your target audience and the AI tools that may recommend you.

Tip: To determine the relative value of your link building, compare your link profile with your competitors’ using one of the many SEO tools available.

9. Soft conversion KPIs

Leads are vital to your overall digital marketing, yet the classification of leads and the sales funnel becomes increasingly complex.

As such, we should measure our SEO efforts’ impact on lead generation, whether social signups, newsletter signups, or some other download or lead gen specific to your business.

Percentage increase in newsletter signups.
Percentage increase in social followers/likes, and so on.
Business-specific lead generation goals (data sheets, white papers, and so on).

Tip: Try to understand the customer journey and the steps required, from awareness and a visit to a paying customer and lifetime value.

SEO may drive most of your email signups, and the email campaigns drive sales. Customize this to your situation and measure what matters.

10. Business objective-specific KPIs

It’s a little more challenging to create a generic example for this category. However, it’s important from a reporting perspective.

We must do all we can to connect the business strategy with our SEO strategy via KPIs.

Often, the KPIs detailed above will give you what you want here, so this is more of a case of structuring them to align with your goals and reporting them meaningfully.

Remember, the higher-ups may only want a very low-resolution overview, but the marketing team may need more detail.

To provide some direction, if your goal is to build awareness of a new product, you should focus on awareness metrics:

Impressions.
Average position.
Keywords.
Clicks.
And so forth.

If you want to drive more signups, we want to track the number of people looking at the signup and pricing pages.

If we look at more traditional conversions, we must look at the total number of sales/leads/inquiries.

The point here is that you already have the SEO KPIs, but remember to:

Frame them around your business objectives.
Clearly highlight that you are helping move the business forward and not just providing a set of esoteric SEO tracking metrics.

Your SEO work has much more value than simply moving a keyword ranking. It adds real value to the business, reduces marketing costs, builds the brand, and more.

Just remember to let the right people know in a way they can understand.

Tip: If you are struggling with how to categorize your SEO KPIs, there are two approaches that we have found helpful over the years.

The first is to group your KPIs along the marketing funnel:

Awareness.
Engagement.
Conversion.

An alternative approach is to use the VQVC model, which stands for:

Volume.
Quality.
Value.
Cost.

If you are struggling, investigate these options to ensure you build truly insightful SEO KPI reports.

11. AI SEO KPIs

Yes, I made you wait until the very end for the hot topic.

Measuring visibility in AI results is important, and contrasting this with the performance (and losses) of traditional SEO metrics can help you understand what is really happening and what matters to you.

The best advice: focus on the metrics that matter most from the list below.

Tracking too many can overwhelm your reporting – and make it hard to see the AI landscape through the SEO noise.

Your goal here is to understand the impact AI search features have on your marketing performance (good and bad).

Additionally, if one area drops, does another rise? This will help you determine where to best focus your efforts.

As such, I think we can do something a little different here and track both positive and negative impacts.

Positive AI SEO KPIs

The following are all good potentials to track.

Branded search volume: A general KPI but one also impacted by mentions in AI (with homepage traffic up for many sites).
Increasing impressions in Google Search Console: Shows your pages are still being surfaced in AI-enhanced search results.
Traffic from Discover, Perspectives, or similar AI surfaces: Indicates your content is featured in newer, AI-powered visibility channels.
Inclusion in Perspectives, Discussions, or Q&A boxes: Confirms your brand is being surfaced as a trusted or relevant source.
Rising branded search volume or direct traffic: Suggests AI exposure is increasing awareness and downstream interest.
Structured data indexed and active (FAQ, How-To, etc.): Means your site is eligible for enhanced display and AI summary inclusion.
High engagement on informational pages (scroll depth, time on page): Shows users are interacting with the content that AI may be using or surfacing.

Negative KPIs (Warning signs of AI interference)

The other side of the AI coin – negative impact.

This is as useful to know so you can ensure that any losses or weaknesses are addressed.

CTR dropping while impressions remain steady or rise: Suggests users are getting answers from AI without clicking through.
Clicks dropping despite strong or rising impressions: Indicates visibility is there but not converting to traffic, likely due to AI.
Loss of rankings for question-style or informational queries: May show that AI Overviews or other features are replacing traditional listings.

Note: Remember, this is the Wild West to some extent. Our goal here is to understand how the tectonic plates of AI and SEO are shifting – and to use the insights to guide our path forward into a new era.

Dig deeper: 12 new KPIs for the generative AI search era

The winds of AI change are blowing

The only constant in search marketing is change. Historically, it’s been a gradual evolution, occasionally punctuated by sudden leaps forward (or backward).

Now, with the integration of generative AI, we’re likely witnessing the single biggest change in search since Day 1.

Bigger than mobile.

Bigger than RankBrain.

Bigger than BERT.

These changes are already showing up in the form of new page features and increasingly dynamic search results – directly impacting visibility and traffic.

Organic clicks are undeniably down, even as overall search volume continues to rise.

The truth is, no one knows exactly how this will unfold. There are no definitive answers.

AI SEO for LLMs and generative technologies won’t replace traditional SEO – but it will continue to reshape it, building on the trajectory we’ve already been following.

SEO KPIs help you:

Understand what’s working.
Spot changes – both good and bad.
Identify which tactics drive results.
Build a strategic view of marketing across the business.

SEO reporting, now more than ever, should be a strategic process.

It’s a chance to build critical thinking, encourage problem-solving, and apply creativity to your SEO approach – so you can thrive in this fast-moving, exciting environment.

Evaluating OpenAI’s Sora and other AI video generators

Mon, 16 Jun 2025 14:20:00 GMT

As AI video generators become increasingly accessible, creators are turning to tools like OpenAI’s Sora, InVideo, Canva’s Magic Studio, and others to speed up production and reduce costs.

But how do these platforms actually perform?

This article evaluates six leading AI video tools – based on real-world use, user feedback, and classroom experimentation – to uncover:

What they do well.
Where they fall short.
How they fit into the future of content creation.

AI in action: Visualizing, iterating, and enhancing storytelling

Text-to-video models are revolutionizing the initial stages of production.

These advanced AI systems can generate complex scenes with remarkable detail, complete with:

Multiple characters.
Specific types of motion.
Accurate renditions of both subject and background.

This capability dramatically reduces the time and resources traditionally spent on pre-visualization, storyboarding, and even initial animation.

Imagine the ability to rapidly iterate visual concepts, test different settings, and refine character interactions before a single frame is actually shot or rendered by a human team.

AI takes on the heavy lifting of execution, allowing human creators to dedicate their energy to what they do best:

Conceptualizing.
Innovating.
Injecting that unique human perspective that transforms mere visuals into a memorable narrative.

Exploring popular AI video generators

When OpenAI’s Sora was finally released to the public on Dec. 9, 2024, it signaled a new chapter in AI-powered video creation.

Just a week later, Google responded with the launch of Veo 2 on Dec. 16, marking the start of an arms race among next-generation text-to-video platforms.

To explore these tools in a real-world setting, I invited students in my “Influencer Marketing and AI” classes – part of the Social Media Professional Program (SMPP) at the New Media Academy in Dubai – to put them to the test.

For their capstone projects, each student was asked to select one of six leading AI video generators:

OpenAI’s Sora.
Google’s Veo 2.
Magic Studio by Canva.
InVideo AI.
VEED.
Runway ML.

Their task?

Use the chosen platform to produce a short, persona-driven video – and then evaluate whether the tool actually helped them create quality content more efficiently or effectively.

With so many options now available in the ever-expanding AI toolkit, the goal was to move beyond hype and see how these tools perform in practice.

1. OpenAI’s Sora: Cinematic AI with striking realism

At the forefront of this brave new world is OpenAI’s Sora.

Its capabilities are truly astounding, enabling the generation of:

Complex scenes featuring multiple characters.
Specific types of motion.
Incredibly accurate details in both subjects and backgrounds.

Imagine the potential for rapid prototyping of visual concepts, creating compelling storyboards, or even generating entire short-form videos from a simple script.

For a deeper dive into its functionalities, resources like “Getting started with Sora” offer valuable insights into generating high-quality videos up to 20 seconds while maintaining visual fidelity and adherence to prompts.

2. Canva’s Magic Studio: Quick and customizable video creation

Beyond the cutting-edge, a suite of user-friendly tools is democratizing video creation for teams of all sizes.

Canva’s Magic Studio stands out as a prime example, offering an intuitive collection of AI-powered features.

Teams can swiftly transform text into video, then:

Refine the content with easy-to-use editing tools.
Apply filters.
Remove backgrounds with a single click.

3. InVideo AI: Simple and efficient video production

For those seeking to produce professional-looking videos quickly without extensive prior experience, InVideo AI presents a compelling solution.

This platform combines pre-made templates with an AI-driven text-to-video feature, significantly accelerating the production process.

While its free plan includes watermarked videos and limited export options, it serves as an excellent entry point for experimentation.

To grasp its efficiency, watch “You Can Start These 8 Faceless Channels Even With $0.”

4. VEED: Fast, accessible video creation with smart AI tools

Similarly, VEED empowers users to create and polish videos with remarkable speed.

Its AI capabilities extend beyond basic generation. You can initiate an entire video from a single sentence describing your desired outcome.

Furthermore, its AI voice recognition can auto-generate subtitles and create transcriptions, significantly boosting accessibility and discoverability.

The video, “NEW: Auto-Generate Reels, Shorts, and TikToks with AI | CLIPS 2.0,” provides creators with some valuable, engaging, and informative content.

5. Runway ML: Experimental video generation with cutting-edge creative tools

For the more adventurous and creative, Runway ML offers a unique and powerful platform with a free plan that includes credits for its AI features, including video generation.

While its free tier has limitations, it’s an exceptional choice for experimenting with innovative AI video tools.

Watch “Introducing Runway Gen-4 | Runway” to learn more about their next-generation series of state-of-the-art AI models for media generation.

6. Google’s Veo: Ambitious AI filmmaking with room to grow

Not to be outdone, Google re-entered the AI video arena with Veo 2, which was rapidly followed by Veo 3.

Then, on May 20, 2025, Google introduced Flow, a new “AI filmmaking tool custom designed for Google DeepMind’s most advanced models – Veo, Imagen and Gemini.”

Watch “Introducing Flow | Google’s New AI Filmmaking Tool” to see a demonstration of the “new AI filmmaking tool built with creatives for the next wave of storytelling.”

Insights from hands-on use of text-to-video models

For their capstone assignment, I asked my students to choose among these six text-to-video models.

I also let them know about this article and invited them to share their thoughts on using AI video tools.

Amna Abdalla Alketbi, senior electrical projects engineer at Dubai Municipality, said:

“Using the InVideo AI tool during the SMPP 20 program was an empowering experience. I created a persona-based video for ‘Fatma Al Suwadi,’ an Emirati fashion designer, and the tool provided incredible flexibility in shaping both the script and the visuals.”
“It allowed me to bring a culturally rooted yet modern brand story to life – quickly and creatively – without needing advanced video editing skills. This hands-on experience showed me how AI can simplify content creation while still allowing for meaningful customization and storytelling.”

Donia Eldesouky, social media manager at the New Media Academy, who also used InVideo AI, said:

“The AI video creation process was incredibly efficient, saving time, effort, and eliminating the need for traditional production steps like filming or voiceovers. However, the final output wasn’t perfect.”
“(My marketing persona,) ‘Yasmina,’ was portrayed more like a human home assistant rather than an AI smart speaker, and the lack of an interactive dialogue weakened the original concept. I’d rate the result 7/10.”
“It’s a promising tool for rapid prototyping, but not yet fully reliable for delivering nuanced, professional content.”

Other students, who wanted to remain anonymous, shared their comments about other AI video generators that they had used as well as their evaluations of whether the text-to-video model they had used had helped them to create high-quality content more efficiently or effectively.

Matt Bailey, founder of SiteLogic and lead instructor at the New Media Academy, said:

“Even though we’ve seen improvements, they only seem to be minor over the past year – but the core issues; bias, cultural bias, body parts, and repetitiveness are still the same.”

He also used Google’s Notebook LLM to generate the following summary of comments by students:

Tools used

Several AI video tools were explored by multiple participants, including Sora, InVideo, and Veo.
One participant reported using Nvidia AI.
Another participant mentioned using Midjourney in the context of AI tools for analysis, although this tool is primarily for image generation.

Ease of use

It was described as easy to create a video using AI.
Creating a how-to video using AI was found to be easier than expected.
The Veo tool was reported as fun to play with.
Sora was found to be easy to use.

Outcome and quality

AI engines were observed to be biased when generating visuals, emphasizing the need to monitor for bias.
One tool produced output quickly but initially had no video output, only text and audio.
Another video generated by Veo included a redundant emoji.
The outcome from using Veo was described as not satisfying, with the participant expecting something better.
AI did not use the script provided. Instead, it tried to manipulate it, which resulted in the user being unhappy with the outcome.
An AI-generated video was rated as not good by one user, giving it a 7/10.
One tool presented the concept of an AI assistant as a human assistant instead of a smart speaker.
One user’s prompts didn’t effectively translate into the generated video, noting that the AI didn’t really get my ideas.
It was stated that AI tools might help generate general ideas, but to generate the real scene, no, it wasn’t that easy. This was especially true for specific and sensitive subjects that are not widely used.
An AI video created was described as quite funny.
One participant felt their AI videos had a little bit more uncanny valley issues than some others seen, suggesting this might be due to the specific software used (Sora).
It was noted that Sora needs a lot of work to make realistic videos appear realistic, and it had a lot of issues.
Specific odd details observed in AI videos included a character walking backwards, a weird door, and strange walking movements. One comment speculated AI might make videos weird on purpose to encourage upgrading to premium versions.
It was suggested that the graphic version of an AI video (using the same concept) looked more realistic than the one featuring human-like characters.

Time and effort saved

It was astonishing to get a certain degree of content in no time, noting it didn’t take the expected longer time or more effort.
Using InVideo AI was described as very fast.
Using InVideo AI meant no filming, editing, or production crew needed.

Limitations and challenges

A significant challenge faced was the lack of traditional clothes and culture in the AI databases or applications. While the music, place, and people were OK, traditional clothes were not represented.
A fundamental limitation mentioned is that AI can never replace empathy and sympathy.
AI is limited by existing data and can’t share different experiences that humans bring.
AI capabilities are limited to executing predefined instructions.
One tool was not able to capture everything from the text prompt, regardless of the amount of text provided.
AI videos were noted to lack emotion and were described as not authentic.
Some AI video outputs included repetitive content.
The generated video did not accurately reflect what was intended in the text prompt.
Difficulty was encountered when trying to prompt AI for something technologically advanced like a “smart mirror,” suggesting AI video is not yet equipped to handle such concepts.
Sora was stated to need a lot of work, especially for realistic videos, having many issues.
Bringing scenes together for a realistic AI video took a considerable amount of time (around one hour).
It was highlighted that AI has issues, especially with heavy or serious topics, making it difficult to use for that kind of content.

Potential use cases and benefits

AI can be utilized in an easy and fun way.
AI video generators are described as powerful tools for producing stunning visuals quickly.
AI tools may be helpful in generating general ideas for content.
AI could potentially be used for a cover for a reel or something similar.
AI is typically used for photos showing ups and downs (e.g., stock market trends).
Sora offered rich suggestions that helped spark new ideas.

Personal preference and comparison to human creation

Despite trying Sora, a participant stated they still enjoy creating things by myself more because it is more creative and better conveys their intended message.
While AI can be used, the human touch is still required and is crucial for creating impactful videos.
AI is viewed as a tool, like a high-tech camera, but not the photographer; it needs human direction, creativity, and vision.
The human mind’s creativity is needed to provide the specific prompts for AI to generate desired results.
One participant subjectively felt that the graphic style AI video was more realistic than the human-like character one.

Cost and access

One participant reported that they didn’t have access to Sora or Runway.
Trying Sora and InVideo AI revealed that both required buying a plan.

Navigating the transformative power of AI in video content creation

Participants found AI video tools like Sora, InVideo, and Veo to be generally fast and easy to use, though less effective for producing realistic or emotionally nuanced content.

While the models delivered impressive visuals and sped up production, users also noted key limitations:

Lack of emotional depth.
AI bias.
Trouble with complex prompts.
The need for human input to ensure originality and avoid repetition or uncanny results.

For marketers, creators, and businesses competing in a visually saturated digital space, these tools aren’t just helpful – they’re essential.

AI isn’t replacing human creativity; it’s expanding what’s possible.

By reducing production barriers and streamlining workflows, AI video generators are empowering storytellers to work faster and smarter.

In today’s content arms race, adopting these tools isn’t just an advantage – it’s a strategic necessity.

How AI is reshaping SEO: Challenges, opportunities, and brand strategies for 2025

Fri, 13 Jun 2025 15:45:00 GMT

The cyclical, now AI-fueled, “ SEO is dead” panic cycle is in full swing.

But here’s what most of those headlines miss:

Search isn’t a platform, it’s a behavior.

Whether people are typing into Google or prompting ChatGPT, consumers are still asking questions that require content, expertise, and trust.

Search isn’t dying – it’s fracturing – as it always has when new platforms emerge and achieve mainstream adoption.

Fortunately, generative engine optimization (GEO) is based on similar value systems that advanced SEOs, content marketers, and digital PR teams are already experts in.

The entity authority framework

Media mentions across trusted publications.
Schema-enriched pages with structured data.
First-party research that establishes expertise.
Expert quotes and branded citations.

While AI will radically change consumer search behavior over the next few years, it’s relying on a lot of the same signals Google’s been rewarding for over a decade.

To maintain brand visibility as search fractures, brands need unified content and authority-building strategies that drive brand visibility across the multiple platforms where your target market resides:

Indexable web content (for Google and Bing), schema markup for context, E-A-A-T signals and author bios, fresh data-driven insights
Structured data and mentions (for LLMs and GEO), expert quotes in trade publications, first-party research citations, brand mentions across authoritative sites
Short-form social content (for TikTok and Reels), searchable captions and hashtags, video content optimized for discovery, cross-platform content threading

Beyond algorithms, I wanted to understand how AI was influencing consumer search behavior and how marketers were adapting their strategies with these transformative workflow tools.

At SMX Advanced 2025 , I presented a cobranded study I led between Fractl Agents, Search Engine Land, and MFour that demonstrates:

How consumers are shifting toward AI-powered search.
How their trust in AI content is evolving.
How marketers are leveraging AI to optimize workflows while maintaining brand credibility.

What follows is an actionable playbook built around the key takeaways from our research.

Consider this your guide to future-proofing your brand visibility and workflow efficiencies in the age of AI.

1. Two-thirds of consumers think AI will replace search in the next five years

But, AI chatbots only represent 2.96% of search engine traffic, currently .

This contradiction reveals the deeper truth: we’re in a paradigm shift of consumer search behavior, not an SEO extinction event.

Consumers are experimenting with new tools like ChatGPT, but they’re still heavily reliant on traditional search engines for the majority of their queries.

The consumer data tells a story that most headlines miss.

People are experimenting with AI-powered search, but 49% of them are still clicking the little blue link for deeper dives.

Nonetheless, friction and innovation drive adoption, and users have been increasingly frustrated with Google’s search experience .

In my February 2025 study, users cited ads cluttering search results, inaccurate AI Overviews , and irrelevant results as major friction points in traditional search.

Consumers aren’t abandoning Google for lack of quality content; they’re escaping SERP friction for clean interface design and faster answers.

When ads bury answers, clean interfaces reign supreme, and AI tools deliver. At least, they’re ad-free , for now.

That’s why even as Google gets 5 trillion searches per year (with 20% YoY growth ), tools like ChatGPT are already ranking as the 5th most visited site globally , with nearly 5 billion visits per month.

2. 64% feel positive about AI, 74% feel confident using AI tools

Think consumers aren’t AI savvy? Think again.

When it comes to the technology adoption lifecycle , we’re officially entering the Early Majority phase.

After ChatGPT’s explosive growth, 100 million users just two months post-launch and over 200 million registered by Q1 2025, we’re seeing:

Major SaaS platforms (Microsoft 365, Salesforce, Slack) bake generative-AI features into everyday workflows.
Mid-market and large enterprises piloting (and increasingly licensing) ChatGPT for content.
ChatGPT itself achieving household-name status.

This signals that AI has crossed the chasm and is rapidly becoming a go-to search platform for everyday consumers.

It’s no longer a matter of if this technology will go mainstream – it has.

Only 8% of respondents weren’t familiar with ChatGPT or Gemini, and 47% said they would explore AI tools as soon as they launch.

It’s no wonder why, with 59% of people believing AI will improve the quality of their life and only 33% thinking “AI is overhyped.”

3. 82% find AI-powered search more helpful than traditional SERPs

Yet, only ~ 22% of marketers track LLM brand visibility or traffic.

Google Search (77%), LLMs (49%), and AI Overviews (38%) top the charts as preferred consumer search tools.

But with 82% of respondents agreeing that “AI-powered search is more helpful than traditional search engines,” it’s clear how consumer search will evolve in the near future.

Although consumer search behavior is rapidly evolving, marketing executives haven’t quite caught up.

Only 22% of marketers have set up LLM brand visibility or traffic monitoring.

At least marketers recognize the urgency, with 53% saying they’re in the early stages of exploring how to monitor this emerging brand channel.

4. 78% of marketers and 68% of consumers are concerned about AI-driven misinformation, more than the fear of job loss

It’s not all sunshine and daisies in the world of AI innovation.

Hallucinations and misinformation are legitimate concerns for consumers and marketers alike.

Over the next 12–18 months, “search” will increasingly feel like a dialogue with a model.

Worried about hallucinations?

It’s a short-term problem due to premature rollouts, but ultimately:

Google’s AI Overviews is built to cite sources.
Bing Chat taps real-time crawling to cut down on made-up answers.

Those safeguards aren’t flawless (yet), but transformer architectures, retrieval-augmented generation, and other core technologies are improving exponentially.

In 6-12 months, blatant hallucinations should be much rarer, and AI responses will include the trustworthy brand citations we’re all after.

Still, it’s imperative that we address consumer concerns around the potential impacts of AI, especially regarding misinformation.

To fight misinformation in AI-driven content workflows, marketers need to double down on:

Proprietary source of truth.
Source transparency.
Credible author review on E-E-A-T topics.
Bias and fact-checking.
Robust author bios.

Proprietary “sources of truth” are one of the best ways for you to produce unique, high-value content with robust, accurate data.

This means giving your AI workflow direct access to proprietary datasets or information, whether that’s:

A knowledge base (i.e., training guides, brand guides).
A data feed (i.e., brand research, an API).

With this, every AI-generated statement can be produced and cross-checked against factual information.

By feeding AI models proprietary sources of truth, you eliminate guesswork and significantly reduce hallucinations.

For instance, my company creates proprietary brand research enriched with insights from client SMEs, designed to earn newsworthy coverage and authoritative mentions.

Every step of that process, from research to digital PR, is enhanced by agentic workflows and custom GPTs.

These AI tools handle the tedious parts, drawing on a decade of training materials, proprietary data, and client guides as their source of truth.

This streamlines content production and digital PR, while reassuring stakeholders that every AI-driven workflow is grounded in proven industry best practices.

Source transparency and credible author review on E-E-A-T topics (or really, all topics) are other critical steps for establishing brand credibility.

All of our clients are encouraged to build authoritative author profiles for SMEs so consumers (and ranking algorithms) have clear source transparency and an immediate understanding of the educated stakeholders who were involved in publishing the content.

Beyond that, AI-driven content should be audited for bias and inaccuracies, especially if you aren’t using a proprietary source of truth.

While there should always be a “human-in-the-loop” in AI workflows, even humans have these same fallacies.

That’s what agentic workflows can be ideal for doing scaled audits of bias and fact-checking.

5. 80% of consumers have a neutral or positive attitude toward trusting brands that use AI-generated content, only 20% don’t

While AI Overviews have driven high levels of skepticism in the SEO industry, the broader consumer market is open to trusting AI-generated content.

Especially when brands follow the guidelines outlined above.

6. Only 11% of marketers feel ‘over-reliant’ on AI tools, yet over 50% of feel ‘high pressure’ to adopt AI to stay competitive

When half your industry feels pressure to adopt something, but only 11% worry they’re overdoing it, you’re looking at classic early-majority anxiety – not actual overreliance.

Marketers who feel “high pressure” to adopt AI are typically part of:

The 30% struggling to keep up with the pace of AI advancements.
The 29% that fear job security due to AI automation.
The 15% who lack internal resources to implement AI effectively.

Still, the only way to keep AI from taking your job, is to use AI to do your job better.

If these fears exist within your organization, you have a cultural enablement problem, and it’s time to get proactive before cultural sentiment and brand visibility suffer:

Adopt a companywide growth mindset .
Promote internal champions to pioneer AI innovation in each department, focusing on custom GPTs, prompt libraries, and agentic workflows.
Launch quarterly AI upskilling sprints to keep your team ahead of the curve.

The pressure will only increase as adoption hits mainstream, but the advantage goes to teams that integrate thoughtfully, not frantically.

7. 74% of marketers are using AI tools in some part of their workflow

74% of marketers are using AI tools in some part of their workflow, but only 4% rely on it for more than three-quarters of their work.

The data reveals where marketers are finding the most value:

44% use AI for social content.
43% for SEO analysis
40% for data reports.

On the other hand:

In-house teams lean heavily on social drafting (45%).
Agencies focus on SEO optimization and analytics (44%).
Consultants prioritize keyword research (48%).

The pattern? AI’s winning at high-volume, low-creativity tasks.

The biggest surprise in our research?

SEO penalties from AI content barely register as a concern among marketers, but misinformation anxiety is through the roof.

The assumption is that Google will penalize AI-generated content.

The reality?

Google’s more likely to reward AI content that demonstrates originality, depth, and clarity than punish content simply because it was AI-assisted.

It’s time to shift your concern from “will AI hurt rankings?” to “will AI content deserve rankings?”

Here’s the operational framework that works:

Use proprietary sources of truth to combat misinformation risks.
Demand editorial fact-checking before anything ships.
Design writing prompts that prioritize divergent thinking over generic tone – because creativity still matters, a lot.

The guardrails matter:

Use proprietary sources of truth to combat misinformation anxiety.
Design prompts that prioritize divergent thinking over generic tone.
Shift concern from “will AI hurt rankings?” to “will AI content deserve rankings?”

Teams don’t need to fear AI, they need to fear mediocre brand content in a sea of sameness.

8. 93% of marketers say AI tools are saving them time each week, but only 19% reinvest it in professional development

The assumption is that AI adoption equals productivity gains, but the time paradox reveals the deeper problem:

93% of marketers say AI tools are saving them time each week.
But only 19% reinvest it in professional development.

Most? They just produce more.

That’s not optimization, that’s just faster hamster wheels.

Across the industry, teams that treat AI as a volume multiplier burn out faster than teams that treat it as a capability amplifier.

As an example, my team drives 70% human strategy, creativity, and relationship building and 30% AI execution, research, and optimization.

We expect this ratio to shift to 60/40 by 2027, but the human elements will become more valuable, not less.

As a result of our AI efficiency gains over the last two years, we started piloting a four-day workweek in 2023.

As a result, our company culture, client retention, and department KPIs are stronger than ever.

The lesson for management?

Build AI usage into your org’s KPIs, appoint internal champions, create learning paths, and reward and recharge your team with the time saved.

9. Search isn’t a platform – it’s a behavior that’s fracturing across interfaces

SEO is no longer about gaming keywords; it’s about earning authority that algorithms recognize universally.

Let’s all take a deep breath and remind ourselves: content marketing is an advanced skillset that has driven visibility across most of the platforms that emerged over the last decade.

After all, the most effective SEOs have always focused on producing fresh, educational, data-driven content, which:

Drives qualified brand traffic and engagement.
Builds authority.
Achieves cross-channel brand visibility.

Today, these strategies have all been proven to elevate brand visibility in organic SERPs, LLMs, SGEs, and AI Overviews.

We’re not watching the death of search.

We’re watching the convergence of channels – search, social, AI – all optimized by the same core signals:

Authority (citations and mentions).
Originality (first-party research and insights).
Trust (consistency across platforms and personas).

Digital PR and brand visibility are now essential LLM inputs.

The same tactics that earn coverage, backlinks, and social engagement also improve your odds in AI summaries and SERP overviews.

If you can master this convergence, you’ll win on Google, ChatGPT, TikTok, and whatever fractures emerge next.

Remember : the fractures aren’t the problem, they’re the opportunity.

But only if you stop treating them like separate games and start building the credibility that works across all of them.

Methodology : We surveyed 2,302 U.S. adults and 810 marketers in May 2025 to explore how attitudes and behaviors around AI vary across consumers and marketing professionals. Marketer responses were drawn from a balanced sample, ensuring all referenced subgroups comprised at least 10% of the total sample.

Chunks, passages and micro-answer engine optimization wins in Google AI Mode

Wed, 11 Jun 2025 13:00:00 GMT

The best CRM article on Page 1 might lose to a single paragraph buried on Page 3.

That’s how AI search works now, and it’s why traditional SEO strategies are failing businesses that should be winning.

AI Mode: Synthesizing answers from multiple background queries

When you use Google’s AI Mode (the new tab next to All ), you’re not getting a direct answer from one query.

You’re seeing a synthesized response created from a cascade of related questions that Google asks behind the scenes.

If you query [What’s the best CRM for SaaS startups?], the machine might also run:

[What features do B2B founders care about?]
[What’s the pricing model of HubSpot?]
[Which CRMs integrate best with Slack and Zapier?]

The machine builds its answer from multiple fragments or chunks of content – the best passages it can find for each of those background queries.

That layered, stitched-together response is a summary of multiple queries related to the original query with a huge chunk of “informed guessing” about the original intent.

As a business (or a person), your presence in those results depends on the power of your digital brand echo – what Google’s AI believes about you or your company based on every web-wide brand signal.

Controlling your digital brand echo is the only thing that matters in modern SEO – a.k.a. generative engine optimization (GEO), assistive engine optimization (AEO), or whatever you choose to call it.

Dig deeper: How AI Mode and AI Overviews work based on patents and why we need new strategic focus on SEO

Ranking in Position 1 isn’t the be-all, end-all it once was

You don’t need to rank first for the primary query.

If any passage from your web-wide content ranks well for just one of the related cascading queries, you’ll be included in the final AI response.

And inclusion is everything.

I call this micro-AEO ranking – small, precise moments where a chunk (passage) of your content hits exactly what the machine is looking for and ranks first as one facet of the answer to a wider, explicit or implicit question.

In traditional SEO, your focus was on the entire page.

Now, your focus needs to be on being the best passage for one angle or facet of the user’s query intent.

Pages rank in the All tab, hyper-relevant passages win in AI Mode

Here’s the real shift:

In the All tab, Google ranks full pages.
In AI Mode, it ranks chunks – individual passages that answer a very specific sub-question in a larger chain/cascade of related queries.

That means you could be completely absent from Page 1 traditional search results, but still show up in AI Mode because you wrote the best paragraph on “CRM integrations for early-stage SaaS.”

This is what I call implicit ranking .

It’s not about what users see. It’s about what machines research behind the scenes.

As a business or SEO, this is opaque: you don’t know the query and yet you need to win it… with a single paragraph within one page within your digital ecosystem (your website is just one of the many opportunities you have, if you open your eyes).

One AI Mode response = a network of micro-wins stitched together

AI Mode is not one big win – it’s a dozen micro-wins stitched into a summary.

Google is choosing helpful, trustworthy, self-contained answers and stringing them together.

This isn’t limited to Google. The same logic applies in ChatGPT , Perplexity , Microsoft Copilot, and other assistive engines.

The difference between them is simply the balance of the algorithmic trinity they use.

The assistive engines protect the click — they summarize, interpret, and recommend

This is why I talk about:

The perfect click (the direct-to-conversion path assistive engines are designed to give you).
And zero-click reputation (how the person perceives your brand before ever visiting your website).

In a world where AI delivers the answer instead of linking to it, your goal is no longer to win the click.

Your goal is to be part of the answer.

What chunks, passages and micro-answer engine optimization mean for your strategy

If you’re optimizing your digital presence only for Google’s All tab, you’re fighting a battle that ended years ago.

Way back in 2020, Darwinism in search (a term coined following a conversation with Google’s Gary Illyes) was already pointing everyone to the multi-vertical approach to SEO and the “survival of the fittest,” with the whole page algorithm as the final arbiter.

In a Search Off the Record podcast episode in 2022 , John Mueller irreverently referred to the whole page algorithm as “the super search engine,” and Gary Illyes used the term “universal mixer.”

They confirmed that a whole page algorithm exists at Google.

With AI Mode, the concept of Bing’s whole page algorithm and Google’s universal mixer takes on a whole new meaning.

To have any chance of being included in that one single multifaceted answer that doesn’t need a click, you have to provide the snippet that best answers one aspect of the question.

That is micro-answer engine optimization in a nutshell.

Dig deeper: Answer engine optimization: 6 AI models you should optimize for

Inclusion in AI answers is the new metric that matters

The shift from traditional SEO to GEO is well and truly here.

SEO is no longer about visibility in isolation. It’s about the usefulness of one of your snippets of content in synthesis for Google, ChatGPT, Microsoft Copilot, Perplexity, and other assistive engines.

To earn your place in the AI-generated narrative, your content must deliver clarity, precision, and trust at the passage level.

You aren’t optimizing to rank. You’re optimizing to be included in the answer. And in the AI era, inclusion is everything.

Dig deeper: SEO in an AI-powered world: What changed in just a year

How Google Ads’ AI tools fix creative bottlenecks, streamline asset creation

Tue, 10 Jun 2025 13:00:00 GMT

Advertisers are facing mounting pressure to produce high-quality images and videos at scale.

Google Ads’ new AI-powered tools aim to ease that burden, streamlining asset creation and addressing the creative bottlenecks that slow campaign performance.

From creative fatigue to AI fix: Google’s plan for scalable visual content

We’re constantly told that video and lifestyle imagery are an important part of the buyer’s journey, and the statistics, along with our own consumption habits, confirm this.

To address this, marketers must create assets that are not only visually engaging but also captivating enough to hold audience interest and inspire actions.

Most advertisers know they should be prioritizing creative strategies to stand out from the crowd.

Yet, producing a consistent stream of effective creative, especially video, can drain resources, time, and budget, often leading to creative fatigue and missed opportunities for advertisers.

And who wants to miss out on boosting Father’s Day sales and have to report that to the CMO?

Google sought to address this during Google Marketing Live , when it introduced AI-driven video and image creation tools.

Developed to optimize and scale visual asset production for brands, these innovations address the increasing need for effective upper funnel marketing assets.

The tool updates are not just incremental changes, they represent a potential transformation in how advertisers can:

Approach the development of creative assets.
Ensure alignment with brand standards.
Engage audiences within increasingly dynamic and complex visual environments.

But as with any AI technology tool, critical questions emerge:

How will these tools truly impact workflows?
What are the implications for brand control and representation?
And what do the industry’s leading paid search professionals – whose insights we’ll share directly – think about this AI shift?

Transforming workflows: From manual tracking to centralized scale

Scaling creative production is becoming increasingly complex. So, can these new tools actually help streamline workflows?

Historically, managing campaign assets has been a painful, manual process.

Despite countless hours spent creating asset tracking sheets and exchanging hundreds of emails to communicate and collect assets, I can’t count the times I went to launch a Performance Max campaign only to realize I was missing image sizes or videos.

This often forced me to pull images directly from a client’s website, which is far from ideal.

When juggling Performance Max, Demand Gen, and YouTube campaigns within the same account, a recurring problem surfaced: the need to use identical images and videos across campaign types.

This made it incredibly tough to determine which campaign was delivering which creative.

Clients emailing screenshots of ads asking, “Where is this ad coming from?!” on a Sunday afternoon certainly didn’t help.

Ideally, each campaign type would benefit from its own tailored creative assets, ensuring clear performance attribution and maximizing the marketing funnel’s effectiveness.

But there simply aren’t enough unique assets to go around, forcing us to recycle.

This ongoing challenge has become a major bottleneck for advertisers.

That’s why the upcoming Asset Studio in Google Ads is so promising.

It’s designed to centralize and simplify asset creation, helping marketers generate more visuals quickly, efficiently, and without sacrificing quality.

It’s important to understand, this isn’t only about more images and videos.

It’s about transforming the entire workflow of asset collection and management.

This potentially means an end to the frantic scrambling for unique images and videos.

Features like “Generated for you” campaign ideas and Brand Profiles in Merchant Center help keep brand identity consistent and make managing assets easier.

This reduces repetition and lets marketers focus more on each campaign’s specific goals, resulting in:

More tailored creative.
Clearer performance attribution.
Far fewer emails from clients asking about the origin of ads.

What are the implications for brand control and representation?

Powerful new AI tools bring important concerns about brand control and how a company is represented.

Google’s AI image and video tools, like Veo’s image-to-video transformation and outpainting, make creating assets easier but also raise key questions for advertisers.

Since brand identity is a company’s most valuable asset, businesses worry if automated content can truly match their style and values.

Using creative that doesn’t follow brand rules can hurt a brand’s reputation and upset brand managers and legal teams.

The ability to create “in action” product images and turn static pictures into videos is a big breakthrough for ads.

But brands need to make sure these AI-made visuals match their lifestyle image and stay true to their brand identity.

Advertisers worry about giving full creative control to algorithms, fearing off-brand content might be used by mistake.

Google addresses this with tools like Brand Profiles in Merchant Center, which help retailers keep their brand consistent across Google.

Plus, features like the upcoming Asset Studio include human checks to ensure AI-generated content follows brand rules and is approved before it’s used.

This balances automation and brand control well.

Industry reactions to Google Ads’ AI innovations

The industry is watching closely, and leading paid search professionals offer nuanced perspectives on Google’s AI shift.

Julie Friedman Bacchini highlights the dual nature of these advancements:

“AI-generated content is a double-edged sword. On one hand, it’s a major advancement that gives businesses – especially those with limited creative resources – greater flexibility and access to high-quality visuals without the need for constant photoshoots. Even large companies, surprisingly, often struggle with strong visual assets, so AI can be a valuable tool for anyone not front and center at events like GML.”
“But there are real concerns. Will AI content be clearly labeled? Will consumers be able to tell what’s AI and what’s real? And as more of us use the same underlying technology, are we headed toward a world where everything starts to look and feel the same? While AI can make content creation easier and more scalable, its long-term impact on creativity and authenticity depends on how thoughtfully it’s used. Transparency and diversity will be crucial.”

Jeremy Krantz offers a pragmatic, yet hopeful, assessment of their current state:

“The new image and video creation features in Google Ads show promise, but they’re still very much in their infancy. Right now, they feel more like early experiments than fully usable tools.”
“That said, as AI continues to improve, I think we’ll see these features evolve into something genuinely useful for performance marketers – it’s just not quite there yet.”

Echoing the potential for democratizing creative resources, Harrison Jack Hepp , sees particular benefit for smaller businesses:

“Asset Studio is especially interesting for small-medium size advertisers that don’t have access to the resources for creative that larger advertisers do.”
“If Asset Studio can give these advertisers the ability to make their products stand out and generate more variations or create useable video assets, it opens up more advertising opportunities and gives them effective tools to compete with large brands.”

Where Google’s new AI creative tools live: A quick guide

To help cut through the confusion, here’s a quick overview of where Google’s new AI-powered creative tools are located or are rolling out:

Looking ahead: The future of AI in creative

This image powerfully shows why Google’s new AI tools are so critical: 83% of people use Google or YouTube daily.

With such widespread engagement, the integration of AI into creative production marks a pivotal moment for paid search marketing.

Producing creative at scale without losing quality saves time and money, allowing brands to invest in other key marketing efforts.

Google’s AI tools give businesses of all sizes a competitive edge:

Large brands can run global campaigns while staying locally relevant.
Midsize companies can stretch their budgets further.
SMBs and solopreneurs gain affordable access to video and image assets.

By cutting production costs, these AI-driven tools level the playing field.

AI-powered image and video creation is still maturing, but future advances will bring:

Real-time creative generation.
Better understanding of brand nuances.
Greater inclusivity in visuals.

These tools will get smarter, meeting users where they are.

Today’s creative often targets an ideal customer, but AI enables ads tailored to the right user at the right moment.

Google is becoming a unified marketing platform, and as creative becomes faster and more precise, ads will align closely with individual searchers’ intent and context.

Google’s AI video and image tools represent a major marketing shift toward faster, smarter, and more efficient creative tailored to a changing landscape.

While challenges like brand oversight remain, these innovations are directly addressing them. Already testing in the U.S., these features offer a more interactive Google experience.

Adopting these AI technologies is no longer optional for marketers aiming to elevate their campaigns – it’s essential.

How multimodal discovery is redefining SEO in the AI era

Tue, 10 Jun 2025 12:00:00 GMT

The SEO industry is undergoing a seismic shift – one shaped not just by algorithms but also by evolving user expectations.

At the heart of it is a radical transformation in how people search, and Google is adapting in real time.

Between the 2025 Google I/O Keynote and meetings with search experts and engineers, it’s clear: we’re entering a multimodal, AI-powered era of discovery.

At the forefront of this change is that search is no longer just a list of blue links.

It’s a personalized, visual, voice-enabled, and AI-summarized interface designed to anticipate the user’s intent, not just respond to it.

Google isn’t changing search, consumers are

Search behavior has evolved. Users now:

Expect instant, context-aware answers.
Ask complex questions.
Use voice, images, and multi-intent queries.

In response, Google is evolving from a reactive query engine into a proactive AI discovery system.

We are in the midst of a transformation from semantic to retrieval-augmented generation (RAG), and the expectation that consumers (searchers) are evolving the discovery journey.

“We are doing a better job at answering harder questions that users bring us than we’ve ever done,” said a senior Google engineer during a session at Google I/O. “Learning to search with keywords is no longer as relevant as a person’s ability to express a desire.”

AI Overviews , Gemini-powered queries, and real-time personalization via context from Gmail, Maps, and Calendar now influence results across verticals like travel, tickets, shopping, and SaaS solutions.

These changes are visible and measurable.

But here’s the reality: SEOs who don’t adapt their content, structure, and measurement models will become invisible in this new ecosystem.

Dig deeper: The next wave of search: AI Mode, deep research and beyond

AI Overviews and the decline of the blue link

With the rollout of AI Overviews , Google is now summarizing answers directly, often without requiring a click or even clearly attributing a single source.

While this may improve the user experience, it introduces a new challenge for content creators, SEO, and web development teams.

“Your entire site can be built on content you didn’t originate – and that’s likely to be a lot less lucrative in the long run,” noted a Google search liaison. “We’re focusing on non-commodity content.”

Google’s updated guidance on “ Succeeding in AI Search ” was a key theme during the 2025 Google I/O event.

Google’s guidance and recommendations to the web community will continue to emphasize originality, experience, structured data, and actionability.

This new guidance evolves “helpful content” to what I call value-rich content.

This level of content is not only useful but also understandable, attributable, and uniquely valuable, especially when answering layered, context-rich expectations.

It is rewarded in an AI-driven search experience.

Multimodal SEO is the new baseline

Search is no longer text-first.

It’s multimodal, integrating text, images, video, voice, and interactive components in one fluid interface.

Google’s Gemini-powered AI now interprets contextual signals across formats.

To stay visible, your content must be more than just readable: it must be visually and structurally rich.

To win in AI-driven SERPs, you must:

Integrate schema markup for products, events, how-tos, and FAQs.
Provide visual assets: annotated screenshots, diagrams, video walkthroughs.
Include structured media: alt-text, transcripts, labeled entities.
Design content to support complex, filtered, multi-intent queries.

During Google I/O, Liz Reid and even engineers after her keynote, noted that users are:

Increasingly interacting with search using complex, multi-step queries.
Expecting results that include images, step-by-step visuals, or embedded video.

In one example shared during the session, an engineer described how Google’s AI handled a search for comparing rental car gas mileage by surfacing not just summary data, but linking users directly to visual content and deeper reviews.

“I was doing a query… and it wasn’t done. I just felt like I understood it a bit better,” said a Google engineer. “Then I went onto the site and was more happy to get more.”

Another Googler added that AI reviews are already influencing the type of content surfaced, stating:

“We’re seeing people going further up the information chain… asking harder stuff. That’s where there’s room for more depth, more unique content, and more distinguished experiences.”

In this context, static documentation or feature articles alone won’t be enough.

If content doesn’t visually show how something works, or provide structured signals that Google can parse across formats, it’s likely to be skipped over in favor of content that does.

This shift isn’t optional – it’s foundational to ranking in a world where search looks less like a listicle and more like a guided assistant.

Dig deeper: SEO in an AI-powered world: What changed in just a year

New KPIs: From clicks to qualified engagement

The era of traffic-first SEO measurement is rapidly coming to an end.

As Google continues to roll out AI Overviews and AI Mode , the ability to track traditional user behavior, such as clicks, bounce rate, and even basic session attribution, is diminishing.

Google has confirmed that clicks from AI Mode are currently untrackable in Google Search Console or standard analytics platforms.

This has major implications. Teams are still being asked to measure SEO success by click-through rates and traffic volume, while the reality is that:

CTRs are collapsing as AI answers, maps, videos, and paid placements dominate SERP real estate.
AI features don’t always generate a click, even when the content is cited.
Search Console doesn’t report on AI Overviews or delineate AI-driven queries from standard ones.
Privacy changes and user consent models are further reducing visible attribution across GA4 and ad platforms.

As one Googler explained:

“People just looking for a short answer – that’s probably not that exciting traffic for a lot of sites. But the users asking harder questions? That’s where there’s room for more depth, more unique content, and more distinguished brand experiences.”

This disconnect between what the executive dashboard expects and what the user experience actually delivers is putting pressure on SEO teams, analysts, and web leads alike.

What’s needed is a redefinition of success metrics. Modern SEO KPIs must include:

Engaged sessions (scroll depth, time-on-content, event triggers).
Return visitor behavior and repeat engagement.
Assisted conversions, not just last-click.
Brand search volume growth, indicating user recall and demand.
Impression visibility in high-AI surfaces, even if not clicked.

In a world where AI is the first and last touchpoint, the SEO team’s role is to ensure that content is findable, referenced, and trusted, even if it’s not immediately clicked.

For organizations still measuring performance with a 2015 dashboard, this shift in mindset is urgent and existential.

Dig deeper: 12 new KPIs for the generative AI search era

Final takeaways

Google is building a search engine for the future of information consumption, not the past, Wikipedia-style content investments.

Web developers, SEO professionals, and content teams must evolve now or risk irrelevance.

Here’s what we can do today:

Audit our content for originality and usefulness – ditch commodity content. Change your optimization to focus on multimodal expectations.
Implement data into the story and use structured data to build a retrieval opportunity for search and consumers. Data is no longer about coverage on a page, it is about relevance and how well it maps to intent, context, and AI-driven summarization.
Design for discovery. Don’t assume the user will read; they might ask, watch, or scroll visually. Deeper expectations are critical in our SEO and content planning. Keyword research is not a linear expectation of volume to traffic. We must expand our expectations to research consumers and experiment within content to ensure the right design for discovery.
Align KPIs with engagement and outcomes, not just rankings! This has become a rallying cry across the SEO industry, but its implementation is complex and unique to each business and website. Turning this principle into practice is a critical mission for both SEO and web teams moving forward.

Most importantly, embrace the complexity with AI search optimization.

The future of search isn’t just about keywords.

It’s about helping people make better decisions through richer, more intuitive discovery experiences.

The good news?

SEO at large is uniquely positioned to lead in this new era.

No other discipline blends content, data, structure, user experience, and technical systems as seamlessly.

In a multimodal, AI-driven search landscape, SEOs aren’t being left behind – we’re being called to lead.

SEO attribution in 2025: Why it’s broken and what you can do

Mon, 09 Jun 2025 13:00:00 GMT

For years, SEO professionals have worked hard to earn a seat at the table.

We’ve built dashboards, reported wins, and tried to translate complex user journeys into something business leaders could understand.

We’ve tried – sometimes desperately – to connect the dots between what we do and the results the business sees.

Now those dots are disappearing.

Attribution has never been easy in SEO.

And today, it’s becoming nearly impossible.

Between AI-generated answers, completely changed user behavior, vanishing click-throughs, and broken analytics, we’re entering a phase where we know SEO’s influence is real, we just can’t prove it.

But what’s making attribution worse than ever, and what SEO teams can do to survive? Keep reading.

Attribution has always been hard

Attributing SEO efforts to actual results has never been straightforward.

SEO is a long-term game – you don’t make a change and see impact the next day.

It often takes weeks or even months for results to show.

By then, so many other things may have changed that it’s hard to know what really moved the needle.

We’ve always been in a state of guesstimation.

One of the biggest issues is isolating SEO from everything else happening at the same time.

It’s rarely one thing that drives a traffic spike or performance drop.

It could be:

An algorithm update .
A holiday.
A product launch.
Changes to the site you didn’t even know about.

In larger companies with many stakeholders, you can’t always control – or even track – what’s being changed.

One of the first things I experienced in my current role was a homepage headline update I didn’t even notice.

I was new and focused on catching up, unaware it had happened.

But constant change is just part of the problem.

More users are using ad blockers or opting out of cookies, making it harder to connect the dots.

Then there’s Google.

Anyone who’s tried to segment data in Google Search Console knows the numbers don’t always add up.

Try filtering by page type or section, and the sum of the parts doesn’t equal the whole.

To make things worse, there’s often confusion around what drives organic traffic.

A PR campaign or out-of-home ad might coincide with a spike, but that doesn’t mean it caused it.

These awareness plays benefit all channels, not just organic.

The reason is simple: users don’t care if a result is organic or paid. They click whatever best matches their intent.

Between tech limitations, shifting user behavior, and internal coordination challenges, gaining a clear view of SEO’s real contribution has always been difficult, and it’s only getting harder.

Dig deeper: How to measure SEO success when AI is changing search

AI search has changed the user journey

Things have always been hard when it comes to SEO attribution.

However, over the last year or so, it has reached a whole new level of uncertainty, largely due to the way people are now utilizing search engines and AI tools.

The old user journey used to be fairly straightforward:

Query → SERP → Click → Website → Conversion

Now, it looks more like this:

Query → AI reasoning → AI answer → Query → AI reasoning → AI answer → Decision made → (Maybe) Brand search → (Maybe) Website visit → (Maybe) Conversion

With AI Overviews in Google, which eliminate the need to open an additional app, users no longer need to visit a website to find the answer to their question.

The summary is right there, above the search results – and often, it’s “good enough.”

That single shift already reduces the chance of a click, even when your content is technically ranking.

And that’s just Google.

For product research or more complex queries, people are increasingly using tools like ChatGPT, Perplexity, or Gemini to get curated responses.

In that environment, both organic and paid traffic lose out, not only because your brand might not be relevant but also because the user might never even see your link.

Some will argue that this isn’t entirely new.

Google has been offering information-rich features for years.

Knowledge panels, featured snippets, and other SERP enhancements have already reduced the need to click.

However, in many markets – especially across Europe – we haven’t been exposed to the full range of these features until now.

Because of GDPR and restrictions around personalization, many of the SERP features that shaped user behavior elsewhere weren’t launched at all.

Now, AI Overviews are everywhere, and this time, their impact is more severe.

The answers are longer, more complete, and much more likely to satisfy the user right there in the search results.

And soon AI Mode will take this to space, adding on top additional layers of information, including such specific to you.

To make things even worse, we’re losing visibility into how users make decisions.

If your brand shows up in an AI-generated response, great.

But often, users don’t click straight away. They might come back later and search for your brand.

And when that happens, we don’t know what triggered their interest in the first place.

Even worse, I’ve seen AI answers mentioning one brand but citing a competitor source. Just try to search for “Best XYZ” and you will understand what I mean.

The best part is that most of us rely on almost the same toolset with almost the same features.

For years, we’ve worked around Google’s limitations – “not provided” keywords, missing data in GA4 – but at least we had some visibility.

Now, most analytics tools don’t capture what happens in AI environments, and those that try to offer some insight are often too expensive.

You can’t go to your manager and say, “Give me a few extra thousand dollars a month so I can maybe figure out how much traffic we’re not getting and potentially how to get back some part of it.”

Most of us need to start small and hope for the best.

Paid search is going through similar challenges, but there’s a key difference.

Platforms like Google have a direct financial incentive to solve attribution for ads.

We’re already seeing experiments like sponsored slots in AI Overviews, and you can bet there are more to come .

For organic? Not so much.

No one is rushing to build robust attribution pipelines for free traffic.

And that means SEO teams are flying blind.

Dig deeper: Will Google’s AI Overviews kill the click?

Rebuilding SEO attribution: What to track when clicks disappear

What’s happening right now feels like a step back.

SEOs have worked hard to make the channel more measurable, to connect:

Actions with outcomes.
Traffic with revenue.
Organic growth with real business value.

And just when we thought we were making progress, SEO armageddon came.

Now we’re told to:

Focus on “visibility” without traffic.
Look at “influence” without attribution.
“Trust the long-term value.”

It’s not that these things are untrue.

They’re just incredibly hard to sell to stakeholders who are still asking for dashboards and ROAS.

I will miss the days when, after optimizing a set of meta titles, we could expect an increase in the average position and an increase in traffic… It was so easy to explain.

But let’s try to think of the glass as half full and see how we can adapt.

Before we discuss adapting, it’s worth noting that if you want to connect SEO to results, you need to make sure you’re tracking the basics properly.

Traffic from AI platforms like Perplexity, ChatGPT, or Claude often comes through as referral in analytics.

This is technically accurate, but easy to lose track of once mixed with other sources.

To avoid this, it’s a good idea to create a custom channel grouping in your analytics platform.

This way, all AI-related referral traffic can be tracked in one place.

A regex filter for this might look something like:

This ensures you can track AI referrals separately and spot early trends – even if click numbers stay small.

Also, make sure to do the same in your CRM setup so that any leads or conversions coming from these sessions are properly tagged. Otherwise, SEO loses the credit again.

After the basics, let’s see if there is some light in the darkness.

While we may not get direct attribution, there are ways to track engagement and see if our content and the efforts for optimising it are doing their job.

Some of the signals we can look for are:

Time on site, engagement rate, and conversion rate (benchmarked against our own content).
Underperforming pages that fall below engagement baselines.
Feedback from sales or support about how users found the brand.
Growth in branded queries or product feature searches.
Users progressing across content in logical, funnel-like paths.

There are also some new metrics, on which we should start keeping an eye, measuring, and reporting.

1. Track AI visibility – not rankings and traffic

You might not get the click, but you can still show up in AI answers.

If your content is clean, factual, structured, and has unique information, LLMs are more likely to use it.

The reason is that they try to offer a wide range of information to the user.

And if you are one of the few that offers something, you will probably make it in the AI results.

If you’re a fan of Eli Schwarz’s product-led SEO concept, you’re probably already doing something similar.

Proving it?

Best-case scenario, you track brand search lifts or use a tool that tracks a limited number of prompts.

You can adjust your webforms by adding the question, “From where have you heard about us?”

If you work with a sales team, they can ask this question.

Dig deeper: How to track visibility across AI platforms

2. Shift from keywords to search intent signals

Keywords don’t work the way they used to.

Users don’t search linearly anymore – they ask AI tools.

If your content doesn’t fit into that flow, you’re out.

Proving it?

Maybe more branded queries that look vaguely like your page titles.

Or longer phrases that appear in the Google Search Console data.

3. Monitor AI mentions across tools and touchpoints

I am mentioning this as a separate item, mainly because many of the well-known SEO tools already show this information (for example, Semrush).

But also because Google says that making it in the AI Overview is shown in Google Search Console data.

Unfortunately, there isn’t a filter to show specifically these results.

Proving it?

Your best bet is a combination of an external tool that shows AI Overiews results and Google Search Console data for impressions.

Dig deeper: 12 new KPIs for the generative AI search era

Additional tip

Keep in mind all the new things Google and other AI tools are launching for paid traffic.

Remember, they need to prove that paying for ads is still meaningful, and it is highly probable that we will soon see some changes in this direction.

Final thoughts

SEO has never been easy.

But at least we used to feel like we were learning the rules of the game – slow results, sure, but measurable ones.

Now it feels like someone changed the game, moved the goalposts, and then set fire to the rulebook.

We’re being asked to navigate a system that’s less visible, less trackable, and far less accountable. That’s frustrating. Both for us and our stakeholders.

But people still search.

People still discover.

People still make decisions online.

Our job now is to keep showing up even if the results are harder to trace, defend, and explain.

That means developing a new measurement mindset, one that’s less focused on direct attribution and more about user momentum.

If your SEO is helping people move forward – discovering, engaging, and showing up in sales conversations – then it’s working.

You just need to adjust how you track it.

Weighted attribution models, user engagement metrics, and anecdotal signals from internal teams might not be perfect, but they can be enough to justify the work and shape what comes next.

Dig deeper: Want to beat AI Overviews? Produce unmistakably human content

Perplexity grows to 780 million monthly queries

Fri, 06 Jun 2025 17:29:00 GMT

Perplexity is on pace to serve 1 billion queries a week within the next year, according to CEO and co-founder Aravind Srinivas. That means Perplexity could answer 52 billion queries annually.

“In May, we did about 780 million queries. That’s growing 20% month over month,” he said at the Bloomberg Tech event in San Francisco.

Why we care. AI answer engines continue to gain traction. Still, right now, AI tools are tiny compared to Google . By comparison, Google sees 52 million queries in about four days and about 98 billion searches in an entire week .

Taking shots. Google’s AI search products are stuck in a loop of rebrands without launching for most users, according to Srinivas.

“In 2023, they called it Search Generative Experience. In 2024, they called it AI Overview. This year they call it AI Mode. Next year, they’ll call her another name. But … the feature never gets shipped to the user,” he said.

Google can’t afford to go full “AI mode” (pardon the pun) and replace traditional search completely because its ad business depends on it, Srinivas said.

“They need the money. That’s the money paying for all the R&D spend on Gemini.”

He also indirectly accused Google of lying about AI citations resulting in less traffic referrals:

“We hope to drive traffic through those citations. I’m never going to lie and say stuff like oh, we’re still sending a lot of traffic and all the stuff that the bigger companies are trying to say. All that’s definitely wrong. We have been very transparent that, yes, there will be less traffic referrals from these UIs and we are being open about it,” Srinivas said.

What’s next . Don’t call it a browser – Perplexity is building a hybridized “cognitive operating system” called Comet that aims to challenge Chrome by integrating navigation, information, and activity.

“Answers are essentially four or five searches in one. Actions are like an entire browsing session in one prompt,” Srinivas said.

The interview. Perplexity CEO Srinivas on Winning Search With AI

llms.txt isn’t robots.txt: It’s a treasure map for AI

Thu, 05 Jun 2025 14:00:00 GMT

In every corner of the SEO world, llms.txt is popping up in conversations, but it is frequently misunderstood and sometimes poorly explained.

If you’ve heard someone call it “the new robots.txt ,” or if ChatGPT itself told you it’s for controlling crawling behavior, it’s time for a reset.

Llms.txt isn’t like robots.txt at all.

It’s more like a curated sitemap.xml that includes only the very best content designed specifically for AI comprehension and citation.

If you structure it thoughtfully, it can be one of the most powerful tools in your AI SEO toolkit – like handing an intrepid AI explorer a map marked with Xs that say, “Start digging here.”

What llms.txt actually is (and isn’t)

Despite the name similarity, llms.txt is not a robots.txt replacement or extension. It doesn’t block crawlers, dictate indexing behavior, or restrict access to content.

Instead, it acts more like a menu – a curated map that guides AI models straight to the most valuable content without making them dig through the entire site.

Llms.txt is a plain text file that tells AI systems which URLs on your site you consider to be high-quality, LLM-friendly content – content you want AI models to:

Ingest.
Understand.
Potentially cite during inference.

Think of it more like a hand-crafted sitemap for AI tools than a set of crawling instructions.

So why the confusion? The name and location certainly don’t help.

Llms.txt lives in the same spot and sounds close enough to robots.txt that it’s easy to make the connection.

But it’s built for an entirely different voyage – and anyone who says otherwise is off the edge of the map, mate.

Why it matters now

Large language models are powering more and more of the search experience – AI Overviews in Google, the citations in ChatGPT Browse, summaries in Perplexity, etc.

And those models aren’t just pulling from whatever content is most recent or most linked.

They’re drawing from what’s easy to ingest, easy to understand, and easy to trust.

That’s where llms.txt comes in.

It gives you a direct line to inference-time ingestion, not just hoping a bot stumbles across the right content through generic crawling behavior.

It’s also not about blocking models from scraping you. It’s about helping them find the right content to cite.

More importantly, llms.txt can help solve a critical problem most site owners haven’t considered: when a language model lands on your site at inference time, it might not enter through the front door.

It might not hit your homepage. It might not even land on the right page at all.

And if the LLM fans out from its landing point looking for relevant content, it may never find that golden nugget of information that answers the user’s question, especially if your site has:

Poor internal linking.
Inconsistent structure.
Content buried six clicks deep.

Llms.txt gives you a chance to plant flags – or better yet, mark the spot with a giant X.

You’re telling the AI, “Here be treasure.”

Instead of letting it wander your site blindly like a ship lost at sea, you’re handing over coordinates to the most valuable loot in your content trove.

It’s also worth noting that llms.txt isn’t designed to allow or deny the use of your content for training purposes.

That’s typically controlled by other tools like robots.txt or specific opt-out signals.

And remember, even if you’ve blocked models from training on your content, they can still access it during inference as long as the page is public.

Inference is a fresh visit every time.

Llms.txt doesn’t contribute content to the model’s memory; it simply tells the model where to look while it’s actively generating a response.

That makes this file more like a live GPS – one that ensures the AI lands on the right page at the right time, without guessing or getting stuck in the wrong part of your site.

llms.txt vs. robots.txt vs. sitemap.xml

Here’s a simple way to think about it:

Robots.txt is about exclusion.

Sitemap.xml is about discovery.

Llms.txt is about curation.

What makes content ‘LLM-friendly’?

If you’re going to point an LLM to your content, it had better be structured for comprehension.

That means:

Short, scannable paragraphs.
Clear headings and subheadings (H1–H3 hierarchy).
Lists, tables, and bullet points.
Defined topic scope (get to the point early).
Minimal distractions (no pop-ups or modal overlays).
Semantic cues like “Step 1,” “In summary,” or “The key takeaway is…”

In other words, the same principles outlined in most AI-focused SEO playbooks.

LLMs don’t need your schema, but they do need your clarity.

Content that is easy to lift, quote, and reassemble will always have an edge.

The more legible and logically segmented your page is, the more likely it is to be cited by an LLM generating an answer to a query.

How to structure your llms.txt

Keep it simple. llms.txt is a plain text file, placed at the root of your domain (e.g., https://example.com/llms.txt).

It should include one URL per line, pointing to content you want large language models to ingest for inference.

Please note that it must be called llms.txt, not llm.txt. The proper name is the plural, and if you forget that ‘s’ on the end, the file will not be recognized.

This file is structured using markdown rather than XML or JSON, per the proposed llms.txt standard .

This is to ensure compatibility with the language models and agents that are likely to read and interpret the file.

While it’s human-readable and easy to create by hand, it also follows a defined structure that programmatic tools can parse reliably.

The llms.txt file should reside at the root of your domain (e.g., /llms.txt) and should include:

A single H1 heading (#) naming the project or site. (This is the only required element.)
A blockquote (>) giving a short summary or context for the links that follow.
Standard markdown sections (like paragraphs or lists) that provide further context. (These are not required, so you can have as many as you like, or none at all.)
One or more H2 headings (##) that introduce categorized link sections
- Each link in those sections is formatted as a markdown [title], hyperlink (url), optionally followed by a : description.

This structure is intentionally simple, but it’s not arbitrary – sticking to the order and syntax improves compatibility across AI tools and platforms.

Ready to try it out? Here’s a sample llms.txt file you can adapt:

It’s useful to note that you can name the H2 sections anything you like, but the section called “Optional” has a reserved function.

If it’s included, the URLs provided there can be skipped if a shorter context is needed.

You will want to use it for secondary information that you won’t mind being skipped.

When creating your llms.txt, please avoid the temptation to dump every URL on your site into the file.

Instead, focus on:

Evergreen content that answers specific questions.
Pages structured for comprehension.
Authoritative pieces that demonstrate E-E-A-T principles.
High-value guides, resource hubs, and pillar content.

If a page wouldn’t make sense quoted out of context, it probably doesn’t belong in llms.txt.

Should you include your homepage?

Maybe.

But consider this: most homepages are designed by marketing departments and meant to be the “welcome desk” of your website.

It’s not usually a place you find deep, useful answers.

Unless your homepage is an actual pillar of useful, structured, LLM-digestible content (and not just a brand billboard), it’s better to direct the AI to where the value lives.

In most cases, your top-level service pages, in-depth guides, and well-formatted blog posts will be more useful to the user, and (technically) that is who we really care about.

Who’s using llms.txt right now?

At the time of writing, OpenAI, Anthropic, Perplexity, and other leading AI companies have started referencing llms.txt when crawling sites, Mintlify reports .

The standard is still evolving, but early adoption is growing, and it’s quickly becoming a visible signal that your site understands how to communicate with AI.

Although including an llms.txt file does not guarantee that your site will be cited, it certainly improves your odds.

It tells models where to look, and gives you a chance to influence the narrative.

This is the new AI SEO frontier

SEO has always been about helping machines understand human ideas. Llms.txt is just the next iteration of that effort.

The biggest mistake SEOs can make today is to treat llms.txt like just another checkbox or compliance layer.

It’s not about blocking bots or appeasing ranking signals. It’s about earning a place in the answers.

And in a search landscape where citations are being generated by machines in real time, you want to be the site they trust enough to quote.

It’s a map, not a muzzle

Llms.txt isn’t about restriction or permission – it’s a compass rose on the edge of your digital parchment, pointing the way to buried gold.

You’re telling the models, “Here. The good stuff, the treasure , is right here. Use this when you answer questions about my field/product.”

And if you’ve structured your content well, it might just make you the go-to source in AI-powered results.

Don’t treat llms.txt like a robots.txt. Treat it like a treasure map.

Because when it comes to the future of AI search, the riches go to those who make their value easy to find.