<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience.png" class="attachment-large size-large wp-post-image" alt="AI can write SEO content, but it can’t replace real experience" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Generic advice is everywhere. Learn why firsthand knowledge and real-world experience are becoming more valuable for SEO.


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<p><a href="https://searchengineland.com/guide/what-is-seo">SEO</a> content is starting to sound painfully repetitive.</p>



<p>Search for almost anything online and you’ll find pages repeating the same advice in slightly different ways. Now that AI tools can create blog posts in seconds, the problem is getting worse.</p>



<p>There’s more content than ever, but much of it feels forgettable. This homogeneity is becoming a real issue for SEO.</p>



<h2 class="wp-block-heading" id="h-real-experience-is-becoming-a-bigger-seo-differentiator">Real experience is becoming a bigger SEO differentiator</h2>



<p>As AI-generated content floods search results, businesses need something that helps them stand out. Right now, one of the biggest things separating good content from bad content is real experience.</p>



<p>AI can write content. But it can’t replace lived experience.</p>



<p>It can’t tell people what happened when a strategy failed. It can’t explain the lessons learned from working with real clients. It can’t share the small details that come from years of doing the work.</p>



<p>That human side matters more than many businesses realize.</p>



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<h2 class="wp-block-heading" id="h-so-much-seo-content-sounds-the-same">So much SEO content sounds the same</h2>



<p>For years, SEO focused heavily on creating content around keywords and search terms. Businesses were encouraged to publish more and more articles to increase visibility.</p>



<p>The problem is that many websites ended up producing content that sounded identical.</p>



<p>Now AI has made that even easier.</p>



<p>Anyone can create a blog called &#8220;10 SEO tips&#8221; or &#8220;How to rank higher on Google&#8221; in under a minute. The internet already has thousands of them, and most add nothing new.</p>



<p>People are tired of reading content that feels copied, even when it technically isn’t.</p>



<p>The pages that stand out now usually feel more human.</p>



<p>They include:</p>



<ul class="wp-block-list">
<li>Real examples.</li>



<li>Honest opinions.</li>



<li>Lessons learned.</li>



<li>Client stories.</li>



<li>Testing and results.</li>



<li>Personal insight.</li>
</ul>



<p>In short, they sound like they were written by someone who has done the job. And that distinction is becoming increasingly important as search changes.</p>



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<h2 class="wp-block-heading" id="h-search-has-evolved-your-content-needs-to-as-well">Search has evolved. Your content needs to as well.</h2>



<p>Google has spent years talking about trust and experience in content. At the same time, AI search tools are making it easier for people to get quick answers without clicking through endless websites.</p>



<p>That evolution means basic information alone is becoming less valuable. If AI can already summarize general advice, businesses need to offer something more. This is where real experience becomes especially valuable for SEO.</p>



<p>A business owner sharing what genuinely worked for them will often build more trust than another polished article filled with generic advice. A case study showing real results carries more weight than a page stuffed with keywords.</p>



<p>Specific details make content feel believable. And that level of detail is something AI still struggles to do well because it can only work from existing information. It can’t truly speak from experience.</p>



<p>For small businesses, that distinction can be especially valuable. Big brands often rely on their name and reputation. Smaller businesses usually win customers because people trust them, like them, and feel a connection with them. That human side can become a real SEO advantage.</p>



<h2 class="wp-block-heading" id="h-ai-works-best-when-paired-with-real-expertise">AI works best when paired with real expertise</h2>



<p>None of this advice means businesses should stop using AI.</p>



<p>When used properly, AI can help with research, planning, idea generation, and speeding up content creation. Most marketers already use it in some way, and that use will continue.</p>



<p>But the businesses getting the best results aren’t relying on AI alone. They’re combining AI with real knowledge, personality, and experience. They’re adding opinions, stories, and insights that can’t simply be generated in seconds. That’s what makes people pay attention.</p>



<p>SEO isn’t about publishing the most content. It’s about creating content people remember and trust. As more websites fill up with AI-generated articles that all sound the same, genuinely human content becomes more valuable.</p>



<p>Because while AI can write words, it still can’t replace real experience.</p>

SEO content is starting to sound painfully repetitive.
Search for almost anything online and you’ll find pages repeating the same advice in slightly different ways. Now that AI tools can create blog posts in seconds, the problem is getting worse.
There’s more content than ever, but much of it feels forgettable. This homogeneity is becoming a real issue for SEO.
Real experience is becoming a bigger SEO differentiator
As AI-generated content floods search results, businesses need something that helps them stand out. Right now, one of the biggest things separating good content from bad content is real experience.
AI can write content. But it can’t replace lived experience.
It can’t tell people what happened when a strategy failed. It can’t explain the lessons learned from working with real clients. It can’t share the small details that come from years of doing the work.
That human side matters more than many businesses realize.

  
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So much SEO content sounds the same
For years, SEO focused heavily on creating content around keywords and search terms. Businesses were encouraged to publish more and more articles to increase visibility.
The problem is that many websites ended up producing content that sounded identical.
Now AI has made that even easier.
Anyone can create a blog called “10 SEO tips” or “How to rank higher on Google” in under a minute. The internet already has thousands of them, and most add nothing new.
People are tired of reading content that feels copied, even when it technically isn’t.
The pages that stand out now usually feel more human.
They include:
Real examples.
Honest opinions.
Lessons learned.
Client stories.
Testing and results.
Personal insight.
In short, they sound like they were written by someone who has done the job. And that distinction is becoming increasingly important as search changes.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Search has evolved. Your content needs to as well.
Google has spent years talking about trust and experience in content. At the same time, AI search tools are making it easier for people to get quick answers without clicking through endless websites.
That evolution means basic information alone is becoming less valuable. If AI can already summarize general advice, businesses need to offer something more. This is where real experience becomes especially valuable for SEO.
A business owner sharing what genuinely worked for them will often build more trust than another polished article filled with generic advice. A case study showing real results carries more weight than a page stuffed with keywords.
Specific details make content feel believable. And that level of detail is something AI still struggles to do well because it can only work from existing information. It can’t truly speak from experience.
For small businesses, that distinction can be especially valuable. Big brands often rely on their name and reputation. Smaller businesses usually win customers because people trust them, like them, and feel a connection with them. That human side can become a real SEO advantage.
AI works best when paired with real expertise
None of this advice means businesses should stop using AI.
When used properly, AI can help with research, planning, idea generation, and speeding up content creation. Most marketers already use it in some way, and that use will continue.
But the businesses getting the best results aren’t relying on AI alone. They’re combining AI with real knowledge, personality, and experience. They’re adding opinions, stories, and insights that can’t simply be generated in seconds. That’s what makes people pay attention.
SEO isn’t about publishing the most content. It’s about creating content people remember and trust. As more websites fill up with AI-generated articles that all sound the same, genuinely human content becomes more valuable.
Because while AI can write words, it still can’t replace real experience.

Generic advice is everywhere. Learn why firsthand knowledge and real-world experience are becoming more valuable for SEO.

AI can write SEO content, but it can’t replace real experience

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png" class="attachment-large size-large wp-post-image" alt="What co-mentions reveal about the AI recommendation gap" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Being understood by AI isn't the same as being recommended by it. New research reveals the gap between the two.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png" class="attachment-large size-large wp-post-image" alt="What co-mentions reveal about the AI recommendation gap" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>We&#8217;ve spent the last two years optimizing for AI visibility by focusing on what we say about ourselves: writing better About pages, adding clear schema and SameAs markup, structuring content more effectively, and providing more direct answers.</p>



<p>All of these principles still apply and are essential for the <a href="https://searchengineland.com/brand-ai-recommendation-set-477229">qualification phase</a> of an LLM&#8217;s brand processing (clarity + relevance). But a study João da Silva and I conducted using Friction AI&#8217;s platform puts a number on a factor the industry has been circling around but couldn&#8217;t prove.</p>



<p>Among brands that were already recognized (where the LLM could describe them accurately), Knowledge Graph (KG) strength predicted visibility within the category each brand was coded to. What it didn&#8217;t predict was whether a brand would surface in an adjacent category query, even if it belonged there from a business perspective. In other words, recognition didn&#8217;t guarantee recommendation. That’s <a href="https://searchengineland.com/framing-gap-brand-position-ai-475715">the framing gap</a>.</p>



<h2 class="wp-block-heading" id="h-what-brands-did-we-test-and-how-did-we-test-them">What brands did we test and how did we test them?</h2>



<p>For this case study, we tested 12 athleisure and activewear brands across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews: 14,140 API runs over seven days, using UK geography with web search enabled.</p>



<p>For each brand, we ran two types of prompts:</p>



<ul class="wp-block-list">
<li>Recognition prompts (&#8220;What is [Brand]?&#8221; and &#8220;Describe [Brand]&#8221;)</li>



<li>Recommendation prompts (&#8220;Best athleisure brands,&#8221; &#8220;Top 10 athleisure brands,&#8221; and &#8220;Which athletic apparel brands are worth buying in 2026?&#8221;)</li>
</ul>



<p>The brands spanned three Knowledge Graph tiers, assigned by Google KG resultScore (the raw score returned by Google&#8217;s Knowledge Graph Search API — a proxy for how strongly an entity is established in Google&#8217;s index), so we could test whether KG strength predicted recommendation visibility:</p>



<ul class="wp-block-list">
<li><strong>Low KG: </strong>LNDR, TALA, Gymshark, Varley.</li>



<li><strong>Mid KG: </strong>Reebok, Outdoor Voices, Rhone Apparel, Sweaty Betty.</li>



<li><strong>High KG: </strong>Alo Yoga, Nike, lululemon, New Balance.</li>
</ul>



<p>Spoiler ahead: The high-KG brands didn&#8217;t dominate recommendations. The mid-KG tier showed the largest average gap between recognition and recommendation.</p>



<p>Within the high-KG tier, some brands were universally recommended, while others were nearly invisible in recommendation prompts, despite being perfectly recognized across every LLM we tested.</p>



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<h2 class="wp-block-heading" id="h-recommendations-what-the-co-mention-data-showed">Recommendations: What the co-mention data showed</h2>



<p>We mapped how often brands appeared together in athleisure content across external sources (articles, reviews, comparison pieces, and editorial lists) crawled via API from UK-indexed sources.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1562" height="680" src="https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai.png" alt="Co-mentions Frictional" class="wp-image-479832" srcset="https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai.png 1562w, https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai-768x334.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai-1536x669.png 1536w" sizes="(max-width: 1562px) 100vw, 1562px" /></figure>
</div>


<p>Some of the most interesting results include:</p>



<ul class="wp-block-list">
<li>lululemon + Alo Yoga: 534 co-mentions.</li>



<li>lululemon + Nike: 482 co-mentions.</li>



<li>Alo Yoga + Nike: 449 co-mentions.</li>



<li>Gymshark + lululemon: 264 co-mentions.</li>



<li>Gymshark + Alo Yoga: 252 co-mentions.</li>
</ul>



<p>These brands appear together repeatedly in the same articles, roundups, and editorial comparisons across independent sources. Together, they form a cluster that the LLM treats as &#8220;athleisure.&#8221;</p>



<p>Now look at the other end of the spectrum. New Balance co-occurs with lululemon in athleisure content so rarely that it doesn&#8217;t appear in the top pairs at all. Nike co-occurs with lululemon roughly 50 times more often than New Balance does.</p>



<p>Nike, New Balance, and Reebok share the exact same Google Knowledge Graph description: &#8220;Footwear company.&#8221; From an entity standpoint, they start from the same position. But Nike is inside the athleisure cluster. New Balance and Reebok are entirely outside it.</p>



<p>The LLM isn&#8217;t evaluating these brands independently and deciding which ones fit athleisure. It&#8217;s pattern-matching against associations built from external content. If a brand hasn&#8217;t appeared consistently alongside lululemon, Alo Yoga, and Gymshark in the content the model trained on — or retrieves from — it doesn&#8217;t belong in that cluster because the semantic association was never built.</p>



<h2 class="wp-block-heading" id="h-nike-the-hero-same-kg-description-completely-different-results">Nike, the hero: Same KG description, completely different results</h2>



<p>Nike, New Balance, and Reebok share the same KG entity description: &#8220;Footwear company.&#8221; LLM probing across all five systems assigns all three unanimously to the athletic_footwear category, so from a pure entity-clarity standpoint, they start from the same position.</p>



<p>However, their recommendation rates in athleisure queries aren’t remotely equivalent.</p>



<p>Nike surfaces in 71% of athleisure recommendation prompts, while New Balance and Reebok appear in 0% across all five LLMs and all 14,140 runs.</p>



<p>The difference isn&#8217;t how they&#8217;re defined (&#8220;Footwear company&#8221;). It&#8217;s which conversations they appear in and which other brands appear alongside them.</p>



<p>LLMs don&#8217;t infer category adjacency. If a brand hasn&#8217;t been consistently mentioned alongside the relevant players in a category — in press, reviews, editorial content, and comparison pieces — the model doesn&#8217;t make the leap. Jason Barnard describes this well: if A plus B should equal J, you have to construct that path explicitly. The model won&#8217;t build it for you.</p>



<p>New Balance&#8217;s co-mention density lives in running and performance content. Nobody built the semantic bridge from running → athletic lifestyle → athleisure in external content, so the model doesn&#8217;t cross it. The Knowledge Graph says &#8220;Footwear company,&#8221; and the third-party corpus confirms footwear. Athleisure queries retrieve the athleisure corpus, and New Balance isn&#8217;t in it.</p>



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<h2 class="wp-block-heading" id="h-the-third-party-citation-weight-in-recommendation-vs-recognition-data">The third-party citation weight in recommendation vs. recognition data</h2>



<p>When we split citations by prompt type — recognition vs. recommendation — a pattern emerges that should reframe where most GEO budgets are being spent.</p>



<p>For recognition prompts — where the user has already typed your brand name — own-brand content is the dominant source:</p>



<ul class="wp-block-list">
<li>ChatGPT cited own-brand content 49% of the time.</li>



<li>Perplexity: 36%.</li>



<li>Claude: 23%.</li>
</ul>



<p>This is where your About page and homepage are used for clarity, and your services, category, and guide pages are used for relevance.</p>



<p>Recommendation prompts give us completely different results. When the user hasn&#8217;t named your brand and is asking for the best option in a category, own-brand citations drop to 18% on ChatGPT and to effectively zero on Gemini, Claude, Perplexity, and Google AI Overviews. Third-party sources account for 82% to 100% of what gets cited across all five systems.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1565" height="311" src="https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation.png" alt="the citation rate of owned content on LLM recommendation vs recognition answers." class="wp-image-479833" srcset="https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation.png 1565w, https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation-768x153.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation-1536x305.png 1536w" sizes="(max-width: 1565px) 100vw, 1565px" /></figure>
</div>


<p>The GEO community has argued for some time that external signals matter more than on-site optimization for recommendation visibility, and this data puts specific numbers behind that argument. It also shows that external signals aren&#8217;t all the same thing.</p>



<ul class="wp-block-list">
<li>Entity clarity gets a brand recognized. That&#8217;s a problem you solve on your own site.</li>



<li>External credibility gets it considered. That&#8217;s a PR and corroboration problem.</li>



<li>Co-mention density in the right category cluster places a brand in the concept graph for a specific recommendation query. That&#8217;s a category-positioning problem. </li>
</ul>



<p>These are three separate problems that require different solutions. Conflating them is why many GEO recommendations stop short.</p>



<p>The practical addition to any GEO audit is this: after checking entity clarity and external credibility, audit where you appear in relation to others.</p>



<ul class="wp-block-list">
<li>Are your press mentions listing you alongside your actual category competitors?</li>



<li>Do the roundups that include you also name the brands that dominate your target category? </li>
</ul>



<p>If not, the LLM has probably never learned to associate you with that category because it has never seen you in that &#8220;company.&#8221; Unlike entity clarity or schema, it&#8217;s not something you can fix on your own website. That&#8217;s the gap.</p>



<h2 class="wp-block-heading" id="h-what-the-co-mention-structure-means-for-pr-and-content-strategy">What the co-mention structure means for PR and content strategy</h2>



<p>As we&#8217;ve seen so far, being mentioned in a category isn&#8217;t enough. Being mentioned alongside the right brands in a category is what places you in the concept graph for that cluster.</p>



<p>A press mention that describes a brand as &#8220;performance apparel&#8221; in isolation does little to advance its athleisure concept graph placement.&nbsp;</p>



<p>A press mention that lists it alongside lululemon, Alo Yoga, and Gymshark in an editorial comparison does considerably more because it builds the co-occurrence signal the model needs to associate the brand with that cluster.</p>



<p>The same logic applies across content type.</p>



<h3 class="wp-block-heading" id="h-editorial-roundups-and-comparison-pieces-nbsp">Editorial roundups and comparison pieces&nbsp;</h3>



<p>Being included in &#8220;best of&#8221; lists that name your category competitors is worth more to your concept graph than a standalone brand profile. The cluster signal comes from appearing in the same article as the brands that define the category.</p>



<h3 class="wp-block-heading" id="h-podcast-appearances">Podcast appearances</h3>



<p>If the host introduces you in relation to specific named brands, or compares your approach to a category leader, that co-occurrence gets indexed.&nbsp;</p>



<p>A bio that says &#8220;founder of [Brand], which competes with lululemon and Gymshark in the premium athleisure space&#8221; does different work than a bio that says &#8220;founder of [Brand], a performance apparel company.&#8221;</p>



<h3 class="wp-block-heading" id="h-analyst-and-industry-reports">Analyst and industry reports</h3>



<p>Category-level reports that group brands together are high-signal co-mention sources. Being included in a sector analysis alongside your category peers places you in the concept graph in a way that standalone coverage doesn&#8217;t.</p>



<h3 class="wp-block-heading" id="h-retailer-and-comparison-taxonomy">Retailer and comparison taxonomy</h3>



<p>Being stocked and categorized alongside category leaders in a major retailer&#8217;s taxonomy is a co-mention signal. The retailer&#8217;s category page is external content that places your brand in a cluster.</p>



<p>The goal is visibility in the right company.</p>



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<h2 class="wp-block-heading" id="h-a-note-on-the-data-and-what-comes-next">A note on the data and what comes next</h2>



<p>This study covers a single category — athleisure and activewear — with 12 brands tested in the UK. The co-mention figures are raw co-occurrence counts from UK-indexed sources crawled via API, covering content indexed at the time of the study in May 2026. Cross-category validation and additional geography testing are in progress.</p>



<p>The full paper, &#8220;<a href="https://zenodo.org/records/20445151" target="_blank" rel="noopener">The Recognition-Recommendation Gap: Empirical Evidence That Category Coding, Not Knowledge-Graph Strength, Determines Brand Visibility in Generative AI Output</a>,&#8221; has been published by João da Silva and me on Zenodo and documents the methodology, brand sample, prompt set, and extraction code in sufficient detail for independent replication.</p>



<p>But the pattern in the co-mention data is clear enough to act on now. Three brands share the same Knowledge Graph description: one appears in 71% of athleisure recommendation responses, and two appear in 0%. The structural difference is co-mention density in category-aligned third-party content.</p>



<p>The question worth asking about any brand is this: In the content that talks about your category, are you in the room, and are you in the right company?</p>

We’ve spent the last two years optimizing for AI visibility by focusing on what we say about ourselves: writing better About pages, adding clear schema and SameAs markup, structuring content more effectively, and providing more direct answers.
All of these principles still apply and are essential for the qualification phase of an LLM’s brand processing (clarity + relevance). But a study João da Silva and I conducted using Friction AI’s platform puts a number on a factor the industry has been circling around but couldn’t prove.
Among brands that were already recognized (where the LLM could describe them accurately), Knowledge Graph (KG) strength predicted visibility within the category each brand was coded to. What it didn’t predict was whether a brand would surface in an adjacent category query, even if it belonged there from a business perspective. In other words, recognition didn’t guarantee recommendation. That’s the framing gap.
What brands did we test and how did we test them?
For this case study, we tested 12 athleisure and activewear brands across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews: 14,140 API runs over seven days, using UK geography with web search enabled.
For each brand, we ran two types of prompts:
Recognition prompts (“What is [Brand]?” and “Describe [Brand]”)
Recommendation prompts (“Best athleisure brands,” “Top 10 athleisure brands,” and “Which athletic apparel brands are worth buying in 2026?”)
The brands spanned three Knowledge Graph tiers, assigned by Google KG resultScore (the raw score returned by Google’s Knowledge Graph Search API — a proxy for how strongly an entity is established in Google’s index), so we could test whether KG strength predicted recommendation visibility:
Low KG: LNDR, TALA, Gymshark, Varley.
Mid KG: Reebok, Outdoor Voices, Rhone Apparel, Sweaty Betty.
High KG: Alo Yoga, Nike, lululemon, New Balance.
Spoiler ahead: The high-KG brands didn’t dominate recommendations. The mid-KG tier showed the largest average gap between recognition and recommendation.
Within the high-KG tier, some brands were universally recommended, while others were nearly invisible in recommendation prompts, despite being perfectly recognized across every LLM we tested.
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Recommendations: What the co-mention data showed
We mapped how often brands appeared together in athleisure content across external sources (articles, reviews, comparison pieces, and editorial lists) crawled via API from UK-indexed sources.




Some of the most interesting results include:
lululemon + Alo Yoga: 534 co-mentions.
lululemon + Nike: 482 co-mentions.
Alo Yoga + Nike: 449 co-mentions.
Gymshark + lululemon: 264 co-mentions.
Gymshark + Alo Yoga: 252 co-mentions.
These brands appear together repeatedly in the same articles, roundups, and editorial comparisons across independent sources. Together, they form a cluster that the LLM treats as “athleisure.”
Now look at the other end of the spectrum. New Balance co-occurs with lululemon in athleisure content so rarely that it doesn’t appear in the top pairs at all. Nike co-occurs with lululemon roughly 50 times more often than New Balance does.
Nike, New Balance, and Reebok share the exact same Google Knowledge Graph description: “Footwear company.” From an entity standpoint, they start from the same position. But Nike is inside the athleisure cluster. New Balance and Reebok are entirely outside it.
The LLM isn’t evaluating these brands independently and deciding which ones fit athleisure. It’s pattern-matching against associations built from external content. If a brand hasn’t appeared consistently alongside lululemon, Alo Yoga, and Gymshark in the content the model trained on — or retrieves from — it doesn’t belong in that cluster because the semantic association was never built.
Nike, the hero: Same KG description, completely different results
Nike, New Balance, and Reebok share the same KG entity description: “Footwear company.” LLM probing across all five systems assigns all three unanimously to the athletic_footwear category, so from a pure entity-clarity standpoint, they start from the same position.
However, their recommendation rates in athleisure queries aren’t remotely equivalent.
Nike surfaces in 71% of athleisure recommendation prompts, while New Balance and Reebok appear in 0% across all five LLMs and all 14,140 runs.
The difference isn’t how they’re defined (“Footwear company”). It’s which conversations they appear in and which other brands appear alongside them.
LLMs don’t infer category adjacency. If a brand hasn’t been consistently mentioned alongside the relevant players in a category — in press, reviews, editorial content, and comparison pieces — the model doesn’t make the leap. Jason Barnard describes this well: if A plus B should equal J, you have to construct that path explicitly. The model won’t build it for you.
New Balance’s co-mention density lives in running and performance content. Nobody built the semantic bridge from running → athletic lifestyle → athleisure in external content, so the model doesn’t cross it. The Knowledge Graph says “Footwear company,” and the third-party corpus confirms footwear. Athleisure queries retrieve the athleisure corpus, and New Balance isn’t in it.
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The third-party citation weight in recommendation vs. recognition data
When we split citations by prompt type — recognition vs. recommendation — a pattern emerges that should reframe where most GEO budgets are being spent.
For recognition prompts — where the user has already typed your brand name — own-brand content is the dominant source:
ChatGPT cited own-brand content 49% of the time.
Perplexity: 36%.
Claude: 23%.
This is where your About page and homepage are used for clarity, and your services, category, and guide pages are used for relevance.
Recommendation prompts give us completely different results. When the user hasn’t named your brand and is asking for the best option in a category, own-brand citations drop to 18% on ChatGPT and to effectively zero on Gemini, Claude, Perplexity, and Google AI Overviews. Third-party sources account for 82% to 100% of what gets cited across all five systems.




The GEO community has argued for some time that external signals matter more than on-site optimization for recommendation visibility, and this data puts specific numbers behind that argument. It also shows that external signals aren’t all the same thing.
Entity clarity gets a brand recognized. That’s a problem you solve on your own site.
External credibility gets it considered. That’s a PR and corroboration problem.
Co-mention density in the right category cluster places a brand in the concept graph for a specific recommendation query. That’s a category-positioning problem. 
These are three separate problems that require different solutions. Conflating them is why many GEO recommendations stop short.
The practical addition to any GEO audit is this: after checking entity clarity and external credibility, audit where you appear in relation to others.
Are your press mentions listing you alongside your actual category competitors?
Do the roundups that include you also name the brands that dominate your target category? 
If not, the LLM has probably never learned to associate you with that category because it has never seen you in that “company.” Unlike entity clarity or schema, it’s not something you can fix on your own website. That’s the gap.
What the co-mention structure means for PR and content strategy
As we’ve seen so far, being mentioned in a category isn’t enough. Being mentioned alongside the right brands in a category is what places you in the concept graph for that cluster.
A press mention that describes a brand as “performance apparel” in isolation does little to advance its athleisure concept graph placement. 
A press mention that lists it alongside lululemon, Alo Yoga, and Gymshark in an editorial comparison does considerably more because it builds the co-occurrence signal the model needs to associate the brand with that cluster.
The same logic applies across content type.
Editorial roundups and comparison pieces 
Being included in “best of” lists that name your category competitors is worth more to your concept graph than a standalone brand profile. The cluster signal comes from appearing in the same article as the brands that define the category.
Podcast appearances
If the host introduces you in relation to specific named brands, or compares your approach to a category leader, that co-occurrence gets indexed. 
A bio that says “founder of [Brand], which competes with lululemon and Gymshark in the premium athleisure space” does different work than a bio that says “founder of [Brand], a performance apparel company.”
Analyst and industry reports
Category-level reports that group brands together are high-signal co-mention sources. Being included in a sector analysis alongside your category peers places you in the concept graph in a way that standalone coverage doesn’t.
Retailer and comparison taxonomy
Being stocked and categorized alongside category leaders in a major retailer’s taxonomy is a co-mention signal. The retailer’s category page is external content that places your brand in a cluster.
The goal is visibility in the right company.
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A note on the data and what comes next
This study covers a single category — athleisure and activewear — with 12 brands tested in the UK. The co-mention figures are raw co-occurrence counts from UK-indexed sources crawled via API, covering content indexed at the time of the study in May 2026. Cross-category validation and additional geography testing are in progress.
The full paper, “The Recognition-Recommendation Gap: Empirical Evidence That Category Coding, Not Knowledge-Graph Strength, Determines Brand Visibility in Generative AI Output,” has been published by João da Silva and me on Zenodo and documents the methodology, brand sample, prompt set, and extraction code in sufficient detail for independent replication.
But the pattern in the co-mention data is clear enough to act on now. Three brands share the same Knowledge Graph description: one appears in 71% of athleisure recommendation responses, and two appear in 0%. The structural difference is co-mention density in category-aligned third-party content.
The question worth asking about any brand is this: In the content that talks about your category, are you in the room, and are you in the right company?

Being understood by AI isn't the same as being recommended by it. New research reveals the gap between the two.

What co-mentions reveal about the AI recommendation gap

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png" class="attachment-large size-large wp-post-image" alt="Interviewing SEOs in an AI-first world" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>One group of SEO talent is becoming dramatically more valuable. The other is watching their skill set become a commodity.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png" class="attachment-large size-large wp-post-image" alt="Interviewing SEOs in an AI-first world" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The search industry is experiencing something many of us have never seen before.</p>



<p><em>The supply of search talent now exceeds the demand for search talent.</em>



<p>Blame artificial intelligence. Blame the economy. Blame years of <a href="https://www.seoforlunch.com/i/168015503/business-priorities-over-seo-checkboxes" target="_blank" rel="noopener">checkbox SEO</a> execution becoming increasingly commoditized.</p>



<p>Whatever the reason, the result is the same.</p>



<p>Search layoffs are up. Job openings are down. The market is more competitive than at any point in my 15+ year career.</p>



<p>The uncomfortable reality is that many of the skills that once made an SEO valuable are becoming easier to <a href="https://www.seoforlunch.com/p/compromise" target="_blank" rel="noopener">automate, outsource, or generate with AI</a>.</p>



<p>Pull up a chair.</p>



<p>Let&#8217;s talk about why this is happening, what skills are becoming table stakes, and what employers should actually be looking for when hiring SEO talent in 2026.</p>



<p><a href="https://www.seoforlunch.com/embed" rel="noopener noreferrer">View embedded content</a>



<h2 class="wp-block-heading" id="h-the-first-seo-jobs-ai-comes-for">The first SEO jobs AI comes for</h2>



<p>One of the biggest misconceptions surrounding AI is that it’s coming for SEO jobs.</p>



<p><strong>I don’t believe that’s what’s happening.</strong>



<p>What I do believe is happening is that AI is changing which SEO skills employers are willing to pay a premium for.</p>



<p>Historically, a significant portion of SEO work revolved around gathering information and generating recommendations. Technical audits, content briefs, keyword clustering, schema markup, metadata recommendations, and competitive analyses all required time, experience, and effort.</p>



<p>Those activities still matter today.</p>



<p>The difference is that they are becoming dramatically easier to produce.</p>



<p>An SEO can now use AI to generate a first-pass audit, content brief, or optimization recommendation in minutes. What once took hours can often be accomplished in seconds.</p>



<p>This doesn’t make the output worthless. It simply changes where the value resides.</p>



<p>For years, many organizations treated recommendations as the deliverable. The audit was the deliverable. The roadmap was the deliverable. The deck was the deliverable.</p>



<p>But recommendations were never the end goal.</p>



<p>Recommendations only create value when they lead to prioritization, implementation, and measurable business outcomes.</p>



<p>AI helps solve the problem of idea generation.</p>



<p>It does very little to solve the implementation problem.</p>



<p>That’s why I believe the first SEO jobs AI comes for are those centered around producing recommendations rather than driving outcomes.</p>



<p>As the cost of generating recommendations approaches zero, employers naturally place greater value on the people who can determine which recommendations matter and implement them.</p>



<p>In other words, AI is commoditizing parts of SEO execution.</p>



<p>It is not <a href="https://www.seoforlunch.com/p/blind-trust" target="_blank" rel="noopener">commoditizing judgment</a>.</p>



<h2 class="wp-block-heading" id="h-what-ai-still-struggles-to-do">What AI still struggles to do</h2>



<p>As AI becomes better at generating recommendations, the value of SEO talent shifts elsewhere.</p>



<p>Prioritization. Testing. Communication. Influence. Judgment.</p>



<p>These aren’t new skills. They’ve always mattered.</p>



<p>The difference is that they are quickly becoming the primary differentiators.</p>



<p>Most organizations don’t suffer from a shortage of ideas. They suffer from a shortage of alignment, execution, and good decision-making.</p>



<p><strong>Finally, there is judgment.</strong>



<p>A few weeks ago, I found myself disagreeing with Gemini about a topic I know extremely well. The answer sounded reasonable. The explanation was polished. <a href="https://www.seoforlunch.com/p/ai-in-the-wild" target="_blank" rel="noopener">The problem was that it was wrong.</a>



<p>As AI becomes more capable, the ability to identify when it is confidently wrong becomes a skill in itself.</p>



<p>The future SEO isn’t the person who can generate the most recommendations.</p>



<p>It’s the person who knows which recommendations actually matter.</p>



<h2 class="wp-block-heading" id="h-the-new-seo-career-framework">The new SEO career framework</h2>



<p>For years, the career progression of an SEO was fairly straightforward.</p>



<ul class="wp-block-list">
<li>Learn more about SEO. <strong>Get promoted.</strong></li>



<li>Learn technical SEO. <strong>Get promoted.</strong></li>



<li>Learn content strategy. <strong>Get promoted.</strong></li>



<li>Learn analytics. <strong>Get promoted.</strong></li>
</ul>



<p>While those skills are still important, AI is rapidly reducing the value of knowledge for knowledge’s sake. The ability to produce SEO recommendations is becoming more accessible every day.</p>



<p>That doesn’t mean expertise no longer matters. It means the skills layered on top of that expertise matter more.</p>



<p>Today’s most valuable search professionals understand search. They understand AI. They understand how <a href="https://www.seoforlunch.com/p/domain-and-brand-built-to-rank-not" target="_blank" rel="noopener">businesses operate</a>. Most importantly, they know how to align people, priorities, and resources around a common objective.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1155" height="243" src="https://searchengineland.com/wp-content/seloads/2026/06/old-vs-new.png" alt="Old vs new" class="wp-image-479686" srcset="https://searchengineland.com/wp-content/seloads/2026/06/old-vs-new.png 1155w, https://searchengineland.com/wp-content/seloads/2026/06/old-vs-new-768x162.png 768w" sizes="(max-width: 1155px) 100vw, 1155px" /><figcaption class="wp-element-caption"><em>Image generated via human-led AI</em></figcaption></figure>
</div>


<p>The higher you climb within an organization, the less your success depends on your <em>ability</em> to identify problems and the more it depends on your ability to <span>solve them</span>.</p>



<p>AI scales execution. <a href="https://www.seoforlunch.com/i/194122154/the-middle-ground-nobody-wants" target="_blank" rel="noopener"><strong>People scale vision.</strong></a>



<h2 class="wp-block-heading" id="h-what-i-d-look-for-if-i-were-hiring-today">What I’d look for if I were hiring today</h2>



<p>If I were hiring an SEO in 2026, I wouldn’t spend much time asking about canonical tags, title tags, or XML sitemaps.</p>



<p>Not because those topics are unimportant, but because I can quickly determine whether someone understands the fundamentals.</p>



<p>What I’d really want to understand is how that person operates when things become messy.</p>



<p><strong>I’d ask them to share a recommendation nobody agreed with.</strong>



<p>Years ago, I argued that H1 tags provided virtually no ranking benefit.</p>



<p><em>People laughed. Some openly disagreed. Eventually, John Mueller echoed a similar position, and Bill Slawski had been discussing the concept for years.</em>



<p>I don’t care whether the candidate was right.</p>



<p>I care whether they had the conviction to challenge assumptions and the communication skills to navigate disagreement.</p>



<p><strong>I’d ask about a failed test.</strong>



<p>Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and maintain momentum?</p>



<p><strong>I’d ask about a project that stalled.</strong>



<p>Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and keep the momentum going?</p>



<p><strong>I’d also ask where AI gave them bad advice.</strong>



<p>The common theme across all of these questions is simple.</p>



<p>I’m not looking for someone who knows SEO. I’m looking for someone who can turn SEO knowledge into outcomes.</p>



<p>The easiest part of SEO has always been knowing what to do. The hard part is getting it done, generating tangible results.</p>



<p>AI won’t replace SEOs, but the lazy ones are toast.</p>



<p><em>This post first appeared on the author’s website and is republished here with permission.</em>


<div class="wp-block-image">
<figure class="aligncenter"><a href="https://seoforlunch.com/?utm_source=searchengineland.com&amp;utm_medium=social"><img decoding="async" width="1774" height="887" src="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg.webp" alt="Leroy2" class="wp-image-476987" srcset="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg 1774w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-768x384.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-1536x768.jpg 1536w" sizes="(max-width: 1774px) 100vw, 1774px" /></a></figure>
</div>

The search industry is experiencing something many of us have never seen before.
The supply of search talent now exceeds the demand for search talent.



Blame artificial intelligence. Blame the economy. Blame years of checkbox SEO execution becoming increasingly commoditized.
Whatever the reason, the result is the same.
Search layoffs are up. Job openings are down. The market is more competitive than at any point in my 15+ year career.
The uncomfortable reality is that many of the skills that once made an SEO valuable are becoming easier to automate, outsource, or generate with AI.
Pull up a chair.
Let’s talk about why this is happening, what skills are becoming table stakes, and what employers should actually be looking for when hiring SEO talent in 2026.
View embedded content



The first SEO jobs AI comes for
One of the biggest misconceptions surrounding AI is that it’s coming for SEO jobs.
I don’t believe that’s what’s happening.



What I do believe is happening is that AI is changing which SEO skills employers are willing to pay a premium for.
Historically, a significant portion of SEO work revolved around gathering information and generating recommendations. Technical audits, content briefs, keyword clustering, schema markup, metadata recommendations, and competitive analyses all required time, experience, and effort.
Those activities still matter today.
The difference is that they are becoming dramatically easier to produce.
An SEO can now use AI to generate a first-pass audit, content brief, or optimization recommendation in minutes. What once took hours can often be accomplished in seconds.
This doesn’t make the output worthless. It simply changes where the value resides.
For years, many organizations treated recommendations as the deliverable. The audit was the deliverable. The roadmap was the deliverable. The deck was the deliverable.
But recommendations were never the end goal.
Recommendations only create value when they lead to prioritization, implementation, and measurable business outcomes.
AI helps solve the problem of idea generation.
It does very little to solve the implementation problem.
That’s why I believe the first SEO jobs AI comes for are those centered around producing recommendations rather than driving outcomes.
As the cost of generating recommendations approaches zero, employers naturally place greater value on the people who can determine which recommendations matter and implement them.
In other words, AI is commoditizing parts of SEO execution.
It is not commoditizing judgment.
What AI still struggles to do
As AI becomes better at generating recommendations, the value of SEO talent shifts elsewhere.
Prioritization. Testing. Communication. Influence. Judgment.
These aren’t new skills. They’ve always mattered.
The difference is that they are quickly becoming the primary differentiators.
Most organizations don’t suffer from a shortage of ideas. They suffer from a shortage of alignment, execution, and good decision-making.
Finally, there is judgment.



A few weeks ago, I found myself disagreeing with Gemini about a topic I know extremely well. The answer sounded reasonable. The explanation was polished. The problem was that it was wrong.



As AI becomes more capable, the ability to identify when it is confidently wrong becomes a skill in itself.
The future SEO isn’t the person who can generate the most recommendations.
It’s the person who knows which recommendations actually matter.
The new SEO career framework
For years, the career progression of an SEO was fairly straightforward.
Learn more about SEO. Get promoted.
Learn technical SEO. Get promoted.
Learn content strategy. Get promoted.
Learn analytics. Get promoted.
While those skills are still important, AI is rapidly reducing the value of knowledge for knowledge’s sake. The ability to produce SEO recommendations is becoming more accessible every day.
That doesn’t mean expertise no longer matters. It means the skills layered on top of that expertise matter more.
Today’s most valuable search professionals understand search. They understand AI. They understand how businesses operate. Most importantly, they know how to align people, priorities, and resources around a common objective.
Image generated via human-led AI



The higher you climb within an organization, the less your success depends on your ability to identify problems and the more it depends on your ability to solve them.
AI scales execution. People scale vision.



What I’d look for if I were hiring today
If I were hiring an SEO in 2026, I wouldn’t spend much time asking about canonical tags, title tags, or XML sitemaps.
Not because those topics are unimportant, but because I can quickly determine whether someone understands the fundamentals.
What I’d really want to understand is how that person operates when things become messy.
I’d ask them to share a recommendation nobody agreed with.



Years ago, I argued that H1 tags provided virtually no ranking benefit.
People laughed. Some openly disagreed. Eventually, John Mueller echoed a similar position, and Bill Slawski had been discussing the concept for years.



I don’t care whether the candidate was right.
I care whether they had the conviction to challenge assumptions and the communication skills to navigate disagreement.
I’d ask about a failed test.



Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and maintain momentum?
I’d ask about a project that stalled.



Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and keep the momentum going?
I’d also ask where AI gave them bad advice.



The common theme across all of these questions is simple.
I’m not looking for someone who knows SEO. I’m looking for someone who can turn SEO knowledge into outcomes.
The easiest part of SEO has always been knowing what to do. The hard part is getting it done, generating tangible results.
AI won’t replace SEOs, but the lazy ones are toast.
This post first appeared on the author’s website and is republished here with permission.

One group of SEO talent is becoming dramatically more valuable. The other is watching their skill set become a commodity.

Interviewing SEOs in an AI-first world

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Ever wonder how many sites are using a specific type of structured data element? Now you can know.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The folks over at Schema.org are now providing aggregate usage statistics for Schema.org terms across the public web. This will show you how many domains are using a specific schema/structured data element.</p>



<p>Schema.org <a href="https://blog.schema.org/2026/06/04/announcing-the-schema-org-usage-statistics-dataset/">announced</a>, &#8220;we are pleased to share a new dataset providing aggregate usage statistics for Schema.org terms across the public web.&#8221; The dataset are updated monthly and they are aggregated at the domain level and presented in popularity range buckets. &#8220;This approach helps filter daily noise while highlighting meaningful adoption trends for researchers and toolmakers,&#8221; Schema.org wrote. </p>



<p><strong>What it looks like. </strong>Here is a screenshot of two of these Schema.org pages,  <a href="https://schema.org/author">author schema</a> and <a href="https://schema.org/Event">event schema</a> showing the usage statistics towards the top:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="1337" src="https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-scaled.png" alt="" class="wp-image-479848" srcset="https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-768x501.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-1536x1003.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p><strong>More about the data. </strong>Schema.org shared more information about the <a href="https://schema.org/docs/usage_stats.html">usage statistics over here</a>, here is a summary:</p>



<ul class="wp-block-list">
<li>Schema.org term frequencies are measured within Google&#8217;s public web crawling infrastructure. The data is aggregated at the domain level (like example.com), not by individual pages. This means if you use the same term on 100 pages of your site, it still only counts as one domain using it.</li>



<li>Instead of showing exact, raw numbers (which change daily and can be noisy), websites are grouped into range &#8220;buckets&#8221; (like &#8220;10K &#8211; 100K&#8221; domains). This keeps the data more stable and protects website privacy.</li>



<li>HubThe raw files are available on the GitHub site at <a href="https://github.com/schemaorg/schemaorg/tree/main/data/public_stats/google">Google Public Stats dataset on GitHub</a>. They are available in JSON and CSV with the same data and in a JSON summary format with aggregated bucket distributions. They are updated monthly.</li>



<li><strong>Term Type</strong>: The type of term. This is either <code>Type</code> (like &#8220;Person&#8221; or &#8220;Event&#8221;) or <code>Property</code> (like &#8220;price&#8221; or &#8220;telephone&#8221;).</li>



<li><strong>URI</strong>: The official URI of the term (for example: <code>http://schema.org/Person</code>).</li>



<li><strong>Domain Count Bucket</strong>: The range of unique domains using the term (for example: <code>100K - 1M</code> domains).</li>
</ul>



<p>Here is a screenshot of GitHub:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1980" height="1006" src="https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A.jpg" alt="" class="wp-image-479849" srcset="https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A.jpg 1980w, https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A-768x390.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A-1536x780.jpg 1536w" sizes="(max-width: 1980px) 100vw, 1980px" /></figure>



<p><strong>Why we care. </strong>Because we love data. Okay, outside of just loving data, knowing if a specific schema element is popular or not may be enough to convince your development team to implement that schema code on your website.</p>






The folks over at Schema.org are now providing aggregate usage statistics for Schema.org terms across the public web. This will show you how many domains are using a specific schema/structured data element.
Schema.org announced, “we are pleased to share a new dataset providing aggregate usage statistics for Schema.org terms across the public web.” The dataset are updated monthly and they are aggregated at the domain level and presented in popularity range buckets. “This approach helps filter daily noise while highlighting meaningful adoption trends for researchers and toolmakers,” Schema.org wrote. 
What it looks like. Here is a screenshot of two of these Schema.org pages,  author schema and event schema showing the usage statistics towards the top:




More about the data. Schema.org shared more information about the usage statistics over here, here is a summary:
Schema.org term frequencies are measured within Google’s public web crawling infrastructure. The data is aggregated at the domain level (like example.com), not by individual pages. This means if you use the same term on 100 pages of your site, it still only counts as one domain using it.
Instead of showing exact, raw numbers (which change daily and can be noisy), websites are grouped into range “buckets” (like “10K – 100K” domains). This keeps the data more stable and protects website privacy.
HubThe raw files are available on the GitHub site at Google Public Stats dataset on GitHub. They are available in JSON and CSV with the same data and in a JSON summary format with aggregated bucket distributions. They are updated monthly.
Term Type: The type of term. This is either Type (like “Person” or “Event”) or Property (like “price” or “telephone”).
URI: The official URI of the term (for example: http://schema.org/Person).
Domain Count Bucket: The range of unique domains using the term (for example: 100K - 1M domains).
Here is a screenshot of GitHub:




Why we care. Because we love data. Okay, outside of just loving data, knowing if a specific schema element is popular or not may be enough to convince your development team to implement that schema code on your website.

Ever wonder how many sites are using a specific type of structured data element? Now you can know.

Schema.org now shows you how many sites are using each schema type

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Ginny Marvin shared details on AI Max, AI Search ads, measurement and first-party data in a wide-ranging Q&#038;A.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Ginny-QA-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Google Marketing Live may be over, but advertisers still have questions. To help answer some of the biggest ones, Google Ads Liaison Ginny Marvin joined Julie Bacchini and the PPC Chat community for an extended Q&amp;A covering everything from AI Max and AI Overviews to first-party data, AI Brief, measurement and the future of search advertising.</p>



<p>The discussion offered several notable clarifications, particularly around AI Search eligibility, reporting limitations and Google&#8217;s growing emphasis on data quality.</p>



<h2 class="wp-block-heading" id="h-ai-max-isn-t-required-for-ai-search-ads">AI Max isn&#8217;t required for AI Search ads</h2>



<p>One of the biggest takeaways from the discussion was Marvin&#8217;s clarification that advertisers don&#8217;t necessarily need <a href="https://searchengineland.com/search?q=ai+max&amp;search_type=all&amp;filtered_q=ai+max">AI Max</a> enabled to appear in AI-powered search experiences.</p>



<p>According to Marvin, campaigns using broad match keywords remain eligible to show <a href="https://searchengineland.com/google-ads-inside-ai-mode-tests-expand-464979">ads in AI Overviews and AI Mode</a>. AI Max expands eligibility by applying broad match behavior to phrase and exact match keywords while also enabling keywordless matching.</p>



<p>That distinction matters because many advertisers have assumed AI Max would become a mandatory gateway into Google&#8217;s emerging AI search surfaces.</p>



<p>The clarification suggests advertisers still have multiple paths into AI Search inventory, at least for now.</p>



<h2 class="wp-block-heading" id="h-don-t-expect-ai-search-reporting-anytime-soon">Don&#8217;t expect AI Search reporting anytime soon</h2>



<p>Advertisers hoping for placement-level reporting received less encouraging news.</p>



<p>Marvin confirmed that ads appearing in AI Overviews and AI Mode are currently reported alongside other top-of-page ads, with no separate performance breakdown available. Google is still evaluating what reporting should look like as these experiences evolve.</p>



<p>That means advertisers will continue to face limited visibility into how much traffic and performance is being driven specifically by AI-powered search experiences.</p>



<h2 class="wp-block-heading" id="h-ai-brief-is-coming-and-google-wants-advertisers-to-guide-the-machine">AI Brief is coming and Google wants advertisers to guide the machine</h2>



<p>Several questions focused on <a href="https://searchengineland.com/google-ai-brief-replacement-keywords-479576">AI Brief</a>, Google&#8217;s upcoming control layer for AI Max campaigns.</p>



<p>Marvin said advertisers will be able to provide both positive and negative guidance, including instructions such as &#8220;never mention prices&#8221; or specifying audiences, messaging themes and search intent they want AI systems to prioritize. The feature will also preview sample assets and queries before deployment.</p>



<p>The rollout will begin with AI Max for Search campaigns in English over the coming months before expanding to Performance Max and AI Max for Shopping.</p>



<p>For advertisers concerned about losing control in increasingly automated campaigns, AI Brief appears to be Google&#8217;s answer.</p>



<h2 class="wp-block-heading" id="h-first-party-data-remains-google-s-favorite-answer">First-party data remains Google&#8217;s favorite answer</h2>



<p>If there was one consistent theme throughout the discussion, it was data.</p>



<p>Marvin repeatedly emphasized the importance of what Google now calls &#8220;Data Strength&#8221; &#8211; the quality and completeness of first-party data flowing into advertising accounts. She highlighted tools such as <a href="https://searchengineland.com/google-shifts-enhanced-conversions-configuration-to-a-new-gtm-tag-456077">Enhanced Conversions</a>, <a href="https://searchengineland.com/google-rolls-out-tag-gateway-integration-via-google-cloud-467186">Google Tag Gateway</a>, <a href="https://searchengineland.com/google-launches-data-manager-api-465903">Data Manager</a> and direct database integrations as critical inputs for future bidding and measurement systems.</p>



<p>The message aligns closely with Google&#8217;s broader GML narrative: better data fuels better AI.</p>



<h2 class="wp-block-heading" id="h-a-new-metric-aims-to-measure-what-traditional-attribution-misses">A new metric aims to measure what traditional attribution misses</h2>



<p>Marvin also shed additional light on <a href="https://searchengineland.com/google-brings-meridian-marketing-mix-modeling-into-analytics-360-478110">Qualified Future Conversions (QFC)</a>, one of the more intriguing measurement announcements from GML.</p>



<p>The metric is designed to estimate conversions that may occur up to 180 days after an ad interaction, helping advertisers understand the long-term impact of campaigns that don&#8217;t immediately generate revenue. The feature is particularly relevant for B2B and lead generation advertisers with lengthy sales cycles.</p>



<p>QFC is currently being tested with a limited group of advertisers, with broader availability expected later this year.</p>



<h2 class="wp-block-heading" id="h-what-excites-google-most">What excites Google most</h2>



<p>When asked which GML announcements she was personally most excited about, Marvin pointed to three areas:</p>



<ul class="wp-block-list">
<li><a href="https://searchengineland.com/google-tests-new-conversational-ad-formats-in-ai-mode-and-search-478115">New ad formats for AI Search.</a></li>



<li>Measurement innovations such as Qualified Future Conversions.</li>



<li><a href="https://searchengineland.com/google-expands-demand-gen-with-youtube-creator-tools-478111">YouTube Creator Partnerships.</a></li>
</ul>



<p>Her answer offers a useful glimpse into where Google appears to be investing most heavily: AI-driven discovery, more sophisticated measurement and creator-led advertising.</p>



<h2 class="wp-block-heading" id="h-why-we-care">Why we care</h2>



<p>The Q&amp;A provided important context that was missing from the GML keynote presentations. Advertisers learned that broad match remains a viable route into AI Search, AI-specific reporting is still a work in progress, and Google&#8217;s long-term vision continues to revolve around automation powered by strong first-party data.</p>



<p>Perhaps most importantly, it showed that while Google is adding more AI to its products, it&#8217;s also building new controls like AI Brief to help advertisers influence how those systems behave — a balancing act that will likely define the next chapter of Google Ads.</p>



<h2 class="wp-block-heading" id="h-dig-deeper">Dig deeper</h2>



<p><a href="https://officialppcchat.com/2026/06/09/all-your-google-marketing-live-2026-questions-answered-with-ginny-marvin/">All Your Google Marketing Live 2026 Questions Answered with Ginny Marvin<br></a><br>


Google Marketing Live may be over, but advertisers still have questions. To help answer some of the biggest ones, Google Ads Liaison Ginny Marvin joined Julie Bacchini and the PPC Chat community for an extended Q&A covering everything from AI Max and AI Overviews to first-party data, AI Brief, measurement and the future of search advertising.
The discussion offered several notable clarifications, particularly around AI Search eligibility, reporting limitations and Google’s growing emphasis on data quality.
AI Max isn’t required for AI Search ads
One of the biggest takeaways from the discussion was Marvin’s clarification that advertisers don’t necessarily need AI Max enabled to appear in AI-powered search experiences.
According to Marvin, campaigns using broad match keywords remain eligible to show ads in AI Overviews and AI Mode. AI Max expands eligibility by applying broad match behavior to phrase and exact match keywords while also enabling keywordless matching.
That distinction matters because many advertisers have assumed AI Max would become a mandatory gateway into Google’s emerging AI search surfaces.
The clarification suggests advertisers still have multiple paths into AI Search inventory, at least for now.
Don’t expect AI Search reporting anytime soon
Advertisers hoping for placement-level reporting received less encouraging news.
Marvin confirmed that ads appearing in AI Overviews and AI Mode are currently reported alongside other top-of-page ads, with no separate performance breakdown available. Google is still evaluating what reporting should look like as these experiences evolve.
That means advertisers will continue to face limited visibility into how much traffic and performance is being driven specifically by AI-powered search experiences.
AI Brief is coming and Google wants advertisers to guide the machine
Several questions focused on AI Brief, Google’s upcoming control layer for AI Max campaigns.
Marvin said advertisers will be able to provide both positive and negative guidance, including instructions such as “never mention prices” or specifying audiences, messaging themes and search intent they want AI systems to prioritize. The feature will also preview sample assets and queries before deployment.
The rollout will begin with AI Max for Search campaigns in English over the coming months before expanding to Performance Max and AI Max for Shopping.
For advertisers concerned about losing control in increasingly automated campaigns, AI Brief appears to be Google’s answer.
First-party data remains Google’s favorite answer
If there was one consistent theme throughout the discussion, it was data.
Marvin repeatedly emphasized the importance of what Google now calls “Data Strength” – the quality and completeness of first-party data flowing into advertising accounts. She highlighted tools such as Enhanced Conversions, Google Tag Gateway, Data Manager and direct database integrations as critical inputs for future bidding and measurement systems.
The message aligns closely with Google’s broader GML narrative: better data fuels better AI.
A new metric aims to measure what traditional attribution misses
Marvin also shed additional light on Qualified Future Conversions (QFC), one of the more intriguing measurement announcements from GML.
The metric is designed to estimate conversions that may occur up to 180 days after an ad interaction, helping advertisers understand the long-term impact of campaigns that don’t immediately generate revenue. The feature is particularly relevant for B2B and lead generation advertisers with lengthy sales cycles.
QFC is currently being tested with a limited group of advertisers, with broader availability expected later this year.
What excites Google most
When asked which GML announcements she was personally most excited about, Marvin pointed to three areas:
New ad formats for AI Search.
Measurement innovations such as Qualified Future Conversions.
YouTube Creator Partnerships.
Her answer offers a useful glimpse into where Google appears to be investing most heavily: AI-driven discovery, more sophisticated measurement and creator-led advertising.
Why we care
The Q&A provided important context that was missing from the GML keynote presentations. Advertisers learned that broad match remains a viable route into AI Search, AI-specific reporting is still a work in progress, and Google’s long-term vision continues to revolve around automation powered by strong first-party data.
Perhaps most importantly, it showed that while Google is adding more AI to its products, it’s also building new controls like AI Brief to help advertisers influence how those systems behave — a balancing act that will likely define the next chapter of Google Ads.
Dig deeper
All Your Google Marketing Live 2026 Questions Answered with Ginny Marvin

Ginny Marvin shared details on AI Max, AI Search ads, measurement and first-party data in a wide-ranging Q&A.

Ginny Marvin clarifies AI Max, AI Search ads and what advertisers should prioritize after GML

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training.png" class="attachment-large size-large wp-post-image" alt="Common Crawl AI training" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Could AI lose a key source of training data? Major publishers want Common Crawl to stop collecting and sharing their content.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training.png" class="attachment-large size-large wp-post-image" alt="Common Crawl AI training" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/common-crawl-ai-training-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Digital Content Next (DCN) sent the Common Crawl Foundation a cease-and-desist letter demanding that it stop scraping and distributing protected publisher content.</p>



<p>The U.S. trade group, which represents major digital publishers (e.g., the AP, the New York Times, NBC Universal, Bloomberg, NPR, and Fox), also asked Common Crawl to remove DCN members’ content from its datasets, including paywalled and subscriber-only news articles.</p>



<p><strong>Publishers question opt-outs.</strong> DCN’s lawyers raised concerns about whether Common Crawl honored publisher opt-out requests and removed older content when asked.</p>



<ul class="wp-block-list">
<li>The letter said Common Crawl had, in some cases, told publishers it was complying, only to later say technical costs and delays prevented full removal. DCN’s lawyers said they were reviewing whether those statements may have been inaccurate or misleading.</li>



<li>Common Crawl publishes a registry of sites that have opted out of scraping. The list includes many large news publishers.</li>
</ul>



<p id="h-dcn-alleges-infringement"><strong>DCN alleges infringement. </strong>The letter argued that copyright law is not an opt-out system. DCN said Common Crawl “flagrantly infringed” publisher copyrights by creating and distributing datasets containing protected content without permission or compensation. </p>



<ul class="wp-block-list">
<li>The group also said Common Crawl made that content available to companies developing AI tools and large language models.</li>



<li>DCN CEO Jason Kint said the legal notice challenges the idea that online content can be collected, stored, and reused simply because it is accessible.</li>
</ul>



<p id="h-common-crawl-pushes-back"><strong>Common Crawl pushes back.</strong> Executive Director Rich Skrenta denied that CCBot bypasses paywalls to scrape websites. He also denied misleading publishers after The Atlantic reported in November that some content from publishers that had requested removal remained available.</p>



<ul class="wp-block-list">
<li>“When a publisher asks us to remove previously crawled material, we respond promptly and initiate a removal process that reflects the technical design of our dataset,” Skrenta said.</li>
</ul>



<p id="h-why-we-care"><strong>Why we care.</strong> This fight could shape how much publisher content AI search engines can use without permission. If courts or settlements impose stricter consent requirements, AI responses may rely more on licensed sources and less on the open web.</p>



<p id="h-why-we-care"><strong>AI training stakes</strong>. Since 2008, Common Crawl has scraped billions of webpages to build a free public archive. Its datasets have been widely used to train AI models. The New York Times’ 2023 copyright lawsuit against OpenAI cited Common Crawl as making up 60% of GPT-3’s training data, <a href="https://pressgazette.co.uk/media_law/common-crawl-ai-news-publishers-scraping-cease-and-desist-letter/">Press Gazette</a> reported.</p>



<ul class="wp-block-list">
<li>A 2024 Mozilla Foundation paper said that, in its current form, generative AI likely would not have been possible without Common Crawl.</li>



<li>Common Crawl has been working on open standards for AI crawling preferences, Skrenta said this week. DCN’s letter asks for a harder line: stop scraping protected publisher content and remove member content already in the datasets.</li>
</ul>


Digital Content Next (DCN) sent the Common Crawl Foundation a cease-and-desist letter demanding that it stop scraping and distributing protected publisher content.
The U.S. trade group, which represents major digital publishers (e.g., the AP, the New York Times, NBC Universal, Bloomberg, NPR, and Fox), also asked Common Crawl to remove DCN members’ content from its datasets, including paywalled and subscriber-only news articles.
Publishers question opt-outs. DCN’s lawyers raised concerns about whether Common Crawl honored publisher opt-out requests and removed older content when asked.
The letter said Common Crawl had, in some cases, told publishers it was complying, only to later say technical costs and delays prevented full removal. DCN’s lawyers said they were reviewing whether those statements may have been inaccurate or misleading.
Common Crawl publishes a registry of sites that have opted out of scraping. The list includes many large news publishers.
DCN alleges infringement. The letter argued that copyright law is not an opt-out system. DCN said Common Crawl “flagrantly infringed” publisher copyrights by creating and distributing datasets containing protected content without permission or compensation. 
The group also said Common Crawl made that content available to companies developing AI tools and large language models.
DCN CEO Jason Kint said the legal notice challenges the idea that online content can be collected, stored, and reused simply because it is accessible.
Common Crawl pushes back. Executive Director Rich Skrenta denied that CCBot bypasses paywalls to scrape websites. He also denied misleading publishers after The Atlantic reported in November that some content from publishers that had requested removal remained available.
“When a publisher asks us to remove previously crawled material, we respond promptly and initiate a removal process that reflects the technical design of our dataset,” Skrenta said.
Why we care. This fight could shape how much publisher content AI search engines can use without permission. If courts or settlements impose stricter consent requirements, AI responses may rely more on licensed sources and less on the open web.
AI training stakes. Since 2008, Common Crawl has scraped billions of webpages to build a free public archive. Its datasets have been widely used to train AI models. The New York Times’ 2023 copyright lawsuit against OpenAI cited Common Crawl as making up 60% of GPT-3’s training data, Press Gazette reported.
A 2024 Mozilla Foundation paper said that, in its current form, generative AI likely would not have been possible without Common Crawl.
Common Crawl has been working on open standards for AI crawling preferences, Skrenta said this week. DCN’s letter asks for a harder line: stop scraping protected publisher content and remove member content already in the datasets.

Could AI lose a key source of training data? Major publishers want Common Crawl to stop collecting and sharing their content.

Publishers push Common Crawl to stop collecting content for AI training

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image.png" class="attachment-large size-large wp-post-image" alt="How to make prompt tracking much more accurate - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI answers are variable, but measurable. Use repeated runs, confidence intervals, and journey tracking to separate signal from noise.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image.png" class="attachment-large size-large wp-post-image" alt="How to make prompt tracking much more accurate - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-prompt-tracking-much-more-accurate-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>By now, you understand that LLMs are probabilistic systems and that AI answers are highly variable. That fact has convinced a lot of people that prompt tracking is extra noise. But discounting prompt tracking as nonsense is the wrong conclusion.</p>



<p>Even though prompt tracking is much less deterministic than keyword tracking, we can significantly increase the accuracy of tracking AI mentions and citations. Repeated runs, fixed sampling rules, and confidence intervals turn variance from a reason to quit into a number you can defend.&nbsp;</p>



<p>By the end of this Memo, you&#8217;ll know how to build that system.</p>



<p><a href="https://www.growth-memo.com/embed?transparent=1" rel="noopener noreferrer">View embedded content</a>



<p>This memo assumes that you’re already:</p>



<ul class="wp-block-list">
<li>Operating under the philosophy of persona-based prompt design, argued for in <a href="https://www.growth-memo.com/p/synthetic-personas-for-better-prompt" target="_blank" rel="noopener">Synthetic Personas for Better Prompt Tracking</a>.</li>



<li>Bought into doing AI SEO / AEO and need a measurement system that actually tracks your progress vs. noise. Check out <a href="https://www.growth-memo.com/p/how-much-can-we-influence-ai-responses" target="_blank" rel="noopener">How Much Can We Influence AI Responses</a> to learn more.</li>
</ul>



<h2 class="wp-block-heading" id="h-the-prompt-tracking-backlash-is-only-half-right">The prompt-tracking backlash is only half-right</h2>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2040" height="1208" src="https://searchengineland.com/wp-content/seloads/2026/06/Where-the-prompt-critique-breaks-down.png" alt="Where the prompt critique breaks down" class="wp-image-479712" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Where-the-prompt-critique-breaks-down.png 2040w, https://searchengineland.com/wp-content/seloads/2026/06/Where-the-prompt-critique-breaks-down-768x455.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Where-the-prompt-critique-breaks-down-1536x910.png 1536w" sizes="(max-width: 2040px) 100vw, 2040px" /></figure>
</div>


<p>Prompt tracking critics are not wrong. Five people running the same prompt get five different answers. Within-LLM variance from sampling alone hits <a href="http://arxiv.org/pdf/2601.21339" target="_blank" rel="noopener">10-34% on identical prompts</a>.</p>



<p>Reporting a point estimate from one run is astrology. Together with AirOps, I looked at <a href="https://www.growth-memo.com/p/shorter-focused-content-wins-in-chatgpt" target="_blank" rel="noopener">815,000 prompt-page pairs</a> and found that after running the same prompt 3x in ChatGPT, only 2.2% of citations remain.</p>



<p>Every prompt is n = 1. Given that the average prompt is 5x longer than classic search keywords, the chance that 2 people around the world use the same exact prompt is close to 0. We currently don’t have any insight into what users prompt, and we might never get that data (although both <a href="https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview" target="_blank" rel="noopener">Bing</a> and <a href="https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports" target="_blank" rel="noopener">Google</a> are keeping us satiated, for now, by offering some AI-visibility data).</p>



<p>But &#8220;probabilistic = unmeasurable&#8221; is lazy thinking. The weather is probabilistic. Credit scores are probabilistic. We still forecast and track them. </p>



<h3 class="wp-block-heading" id="h-keyword-tracking-was-never-as-clean-as-we-d-like-to-remember">Keyword tracking was never as clean as we’d like to remember</h3>



<p>Classic keyword tracking was more deterministic, but not as much as you think:</p>



<ul class="wp-block-list">
<li>For local searches, results were personalized by location and device.&nbsp;</li>



<li>Google rescores results daily, so every rank tracker reports a position range, not a fixed number.</li>
</ul>



<p>The industry standardized the sampling, fixed location, clean profile, daily crawl, etc., until the noise disappeared. Prompt tracking needs the same move, applied to a harder problem. An added challenge: Keyword tracking was focused on Google, but now we have tons of engines. As the market consolidates, tracking simplifies.</p>



<p>I’d argue there&#8217;s no escaping this either as Google transitions from classic search to AI search. More searches than ever show AI Overviews, all while AI Overviews and AI Mode increasingly merge.</p>



<p>At I/O 2026, Search head Liz Reid said users increasingly ask &#8220;longer, more natural-language questions,&#8221; and Sundar Pichai described Search as &#8220;less about individual queries&#8221; and &#8220;more like an ongoing conversation.&#8221;</p>



<h2 class="wp-block-heading" id="h-where-common-prompt-tracking-breaks">Where common prompt tracking breaks</h2>



<p>Over the last 2 years, prompt-tracking tools have multiplied, while the methodology behind them has stalled. Where’s the innovation?</p>



<p><strong>The common prompt-tracking approach looks something like this:</strong>



<ul class="wp-block-list">
<li>Define 25-50 prompts (brand/category/problem split).</li>



<li>Run each prompt once per platform.</li>



<li>Track daily.</li>



<li>Score for citation, mention, sentiment, position.</li>
</ul>



<p><strong>Here are the problems I see with that approach:</strong>



<ul class="wp-block-list">
<li><strong>Variance</strong>: Only 2.3% of citations remain after three prompt runs [<a href="https://www.growth-memo.com/p/the-consensus-gap" target="_blank" rel="noopener">The Consensus Gap</a>]. One run is a coin flip with the answer hidden.&nbsp;</li>



<li><strong>Reasoning</strong>: High vs. low reasoning opens an 18 percentage point citation-rate gap and changes how the model searches, with high reasoning firing 4.6x more fan-out queries [<a href="https://www.growth-memo.com/p/reasoning-lift-what-happens-to-ai" target="_blank" rel="noopener">Reasoning Lift</a>]. An aggregate score blends two different engines into one misleading number.&nbsp;</li>



<li><strong>Personalization: </strong>Most prompt-tracking is not <a href="https://www.growth-memo.com/p/synthetic-personas-for-better-prompt" target="_blank" rel="noopener">persona-specific</a>, so it reports generic answers that no one sees.</li>



<li><strong>Monthly cadence</strong>: SISTRIX <a href="https://www.sistrix.com/blog/ai-citation-drift-how-stable-are-sources-in-ai-search-results/" target="_blank" rel="noopener">tracked</a> 82,619 prompts over 17 weeks and found Google AI Mode replaces 56% of its cited sources every week, while ChatGPT replaces 74%. At that drift, monthly tracking is like checking your bank account once a quarter.</li>



<li><strong>Cross-platform aggregation</strong>: Blending your ChatGPT + Perplexity + Gemini visibility into one &#8220;AI visibility score&#8221; is like averaging your Google rank with your Bing rank.</li>



<li><strong>Conversations</strong>: A single Turn 1 query tells you whether you get mentioned. It says nothing about whether you survive Turn 2 onward, when the user asks about alternatives, pricing, integrations, or risk. AI is a conversational interface, so the journey is the unit of measurement, and a one-shot prompt misses most of it.&nbsp;</li>



<li><strong>Context</strong>: Pure mention counting with no context treats every appearance as a win. Get named first for &#8220;what are the worst CRMs to avoid?&#8221; and a mention tracker still records a victory.&nbsp;</li>
</ul>



<p>So, while we can’t remove AI answer variance, we can run prompts multiple times and measure what parts, brand mentions, and citations of the AI answer remain.</p>



<p>Mirroring follow-up prompts is hard because we don&#8217;t know exactly what people will ask, but we can use AI to estimate likely follow-ups, enrich them with real conversation transcripts, and track the follow-ups LLMs suggest inside their own answers. We can also record the attributes a brand gets mentioned with, not only whether it shows up.</p>



<h2 class="wp-block-heading" id="h-what-good-prompt-tracking-looks-like-in-practice">What good prompt tracking looks like in practice</h2>



<p>Worked example: B2B SaaS, CRM category.</p>



<ul class="wp-block-list">
<li><strong>Prompt set: </strong>40 seed prompts, weighted toward problem prompts where purchase intent lives (12 brand, 12 category, 16 problem).</li>



<li><strong>Platforms:</strong> ChatGPT, Perplexity, Gemini, Google AI Overviews. Tracked separately.</li>



<li><strong>Run config: </strong>Five reps per prompt per platform, every week.</li>



<li><strong>Personas:</strong> The 28 category and problem prompts are customized for three key personas (CFO, IT, marketing).</li>



<li><strong>Metrics:</strong> Mention rate (± CI), citation rate (± CI), average position when mentioned (1-5), sentiment, and the attributes attached to each mention.</li>
</ul>



<p>Level it up by adding the journey layer. A flat list of 40 prompts only measures Turn 1. To measure conversations, build the high-intent prompts into journeys that follow the buyer across the five stages from <a href="https://www.growth-memo.com/p/reasoning-lift-what-happens-to-ai" target="_blank" rel="noopener">Reasoning Lift:</a> Problem, Exploration, Comparison, Validation, Selection.&nbsp;</p>



<p>Each seed prompt for Turn 1 becomes the “seed prompt,” and each stage adds a natural follow-up prompt on subsequent turns.</p>



<p>For a buyer evaluating CRMs, one journey runs:</p>



<ul class="wp-block-list">
<li>Problem: &#8220;How do I know if my sales team needs a CRM?&#8221;</li>



<li>Exploration: &#8220;What types of CRM software exist for B2B SaaS?&#8221;</li>



<li>Comparison: &#8220;HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team&#8221;</li>



<li>Validation: &#8220;Is HubSpot worth the price for mid-market B2B?&#8221;</li>



<li>Selection: &#8220;How do I get started with HubSpot Sales Hub?&#8221;</li>
</ul>



<p>Run the full sequence as one conversation rather than five isolated prompts, and score every turn. The payoff is persistence: in Reasoning Lift, a brand cited at the Problem stage carried all the way to Selection in four journeys under high reasoning and in zero under minimal. Persistence is the metric a one-shot tracker can never see.</p>



<p>Scope it so the run volume stays sane. Track all 40 seed prompts at Turn 1 for breadth, and build the 16 problem prompts into full five-stage journeys for depth.</p>



<p>Insight example: HubSpot is mentioned in 78% ± 6pp of ﬁproblem prompts on ChatGPT vs. 34% ± 9pp on Perplexity. Perplexity pulls from comparison posts (G2, Capterra); ChatGPT pulls from HubSpot&#8217;s own blog plus integration and compliance docs.</p>



<p>Action: invest in integration guides and API docs to win ChatGPT. Invest in G2 review velocity and comparison content to win Perplexity.</p>



<h2 class="wp-block-heading" id="h-the-next-generation-of-tracking-looks-like-polling">The next generation of tracking looks like polling</h2>



<p>Prompt tracking won’t become keyword tracking. AI answers are too variable, too personalized, and too dependent on source selection. But that doesn’t make them unmeasurable.</p>



<p>The next iteration of prompt tracking will look less like rank tracking and more like polling: repeated runs, clear sampling rules, confidence intervals, segmented panels, and raw-answer audits.</p>



<p><em>This post first appeared on the author’s website and is republished here with permission.</em>


By now, you understand that LLMs are probabilistic systems and that AI answers are highly variable. That fact has convinced a lot of people that prompt tracking is extra noise. But discounting prompt tracking as nonsense is the wrong conclusion.
Even though prompt tracking is much less deterministic than keyword tracking, we can significantly increase the accuracy of tracking AI mentions and citations. Repeated runs, fixed sampling rules, and confidence intervals turn variance from a reason to quit into a number you can defend. 
By the end of this Memo, you’ll know how to build that system.
View embedded content



This memo assumes that you’re already:
Operating under the philosophy of persona-based prompt design, argued for in Synthetic Personas for Better Prompt Tracking.
Bought into doing AI SEO / AEO and need a measurement system that actually tracks your progress vs. noise. Check out How Much Can We Influence AI Responses to learn more.
The prompt-tracking backlash is only half-right




Prompt tracking critics are not wrong. Five people running the same prompt get five different answers. Within-LLM variance from sampling alone hits 10-34% on identical prompts.
Reporting a point estimate from one run is astrology. Together with AirOps, I looked at 815,000 prompt-page pairs and found that after running the same prompt 3x in ChatGPT, only 2.2% of citations remain.
Every prompt is n = 1. Given that the average prompt is 5x longer than classic search keywords, the chance that 2 people around the world use the same exact prompt is close to 0. We currently don’t have any insight into what users prompt, and we might never get that data (although both Bing and Google are keeping us satiated, for now, by offering some AI-visibility data).
But “probabilistic = unmeasurable” is lazy thinking. The weather is probabilistic. Credit scores are probabilistic. We still forecast and track them. 
Keyword tracking was never as clean as we’d like to remember
Classic keyword tracking was more deterministic, but not as much as you think:
For local searches, results were personalized by location and device. 
Google rescores results daily, so every rank tracker reports a position range, not a fixed number.
The industry standardized the sampling, fixed location, clean profile, daily crawl, etc., until the noise disappeared. Prompt tracking needs the same move, applied to a harder problem. An added challenge: Keyword tracking was focused on Google, but now we have tons of engines. As the market consolidates, tracking simplifies.
I’d argue there’s no escaping this either as Google transitions from classic search to AI search. More searches than ever show AI Overviews, all while AI Overviews and AI Mode increasingly merge.
At I/O 2026, Search head Liz Reid said users increasingly ask “longer, more natural-language questions,” and Sundar Pichai described Search as “less about individual queries” and “more like an ongoing conversation.”
Where common prompt tracking breaks
Over the last 2 years, prompt-tracking tools have multiplied, while the methodology behind them has stalled. Where’s the innovation?
The common prompt-tracking approach looks something like this:




Define 25-50 prompts (brand/category/problem split).
Run each prompt once per platform.
Track daily.
Score for citation, mention, sentiment, position.
Here are the problems I see with that approach:




Variance: Only 2.3% of citations remain after three prompt runs [The Consensus Gap]. One run is a coin flip with the answer hidden. 
Reasoning: High vs. low reasoning opens an 18 percentage point citation-rate gap and changes how the model searches, with high reasoning firing 4.6x more fan-out queries [Reasoning Lift]. An aggregate score blends two different engines into one misleading number. 
Personalization: Most prompt-tracking is not persona-specific, so it reports generic answers that no one sees.
Monthly cadence: SISTRIX tracked 82,619 prompts over 17 weeks and found Google AI Mode replaces 56% of its cited sources every week, while ChatGPT replaces 74%. At that drift, monthly tracking is like checking your bank account once a quarter.
Cross-platform aggregation: Blending your ChatGPT + Perplexity + Gemini visibility into one “AI visibility score” is like averaging your Google rank with your Bing rank.
Conversations: A single Turn 1 query tells you whether you get mentioned. It says nothing about whether you survive Turn 2 onward, when the user asks about alternatives, pricing, integrations, or risk. AI is a conversational interface, so the journey is the unit of measurement, and a one-shot prompt misses most of it. 
Context: Pure mention counting with no context treats every appearance as a win. Get named first for “what are the worst CRMs to avoid?” and a mention tracker still records a victory. 
So, while we can’t remove AI answer variance, we can run prompts multiple times and measure what parts, brand mentions, and citations of the AI answer remain.
Mirroring follow-up prompts is hard because we don’t know exactly what people will ask, but we can use AI to estimate likely follow-ups, enrich them with real conversation transcripts, and track the follow-ups LLMs suggest inside their own answers. We can also record the attributes a brand gets mentioned with, not only whether it shows up.
What good prompt tracking looks like in practice
Worked example: B2B SaaS, CRM category.
Prompt set: 40 seed prompts, weighted toward problem prompts where purchase intent lives (12 brand, 12 category, 16 problem).
Platforms: ChatGPT, Perplexity, Gemini, Google AI Overviews. Tracked separately.
Run config: Five reps per prompt per platform, every week.
Personas: The 28 category and problem prompts are customized for three key personas (CFO, IT, marketing).
Metrics: Mention rate (± CI), citation rate (± CI), average position when mentioned (1-5), sentiment, and the attributes attached to each mention.
Level it up by adding the journey layer. A flat list of 40 prompts only measures Turn 1. To measure conversations, build the high-intent prompts into journeys that follow the buyer across the five stages from Reasoning Lift: Problem, Exploration, Comparison, Validation, Selection. 
Each seed prompt for Turn 1 becomes the “seed prompt,” and each stage adds a natural follow-up prompt on subsequent turns.
For a buyer evaluating CRMs, one journey runs:
Problem: “How do I know if my sales team needs a CRM?”
Exploration: “What types of CRM software exist for B2B SaaS?”
Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team”
Validation: “Is HubSpot worth the price for mid-market B2B?”
Selection: “How do I get started with HubSpot Sales Hub?”
Run the full sequence as one conversation rather than five isolated prompts, and score every turn. The payoff is persistence: in Reasoning Lift, a brand cited at the Problem stage carried all the way to Selection in four journeys under high reasoning and in zero under minimal. Persistence is the metric a one-shot tracker can never see.
Scope it so the run volume stays sane. Track all 40 seed prompts at Turn 1 for breadth, and build the 16 problem prompts into full five-stage journeys for depth.
Insight example: HubSpot is mentioned in 78% ± 6pp of ﬁproblem prompts on ChatGPT vs. 34% ± 9pp on Perplexity. Perplexity pulls from comparison posts (G2, Capterra); ChatGPT pulls from HubSpot’s own blog plus integration and compliance docs.
Action: invest in integration guides and API docs to win ChatGPT. Invest in G2 review velocity and comparison content to win Perplexity.
The next generation of tracking looks like polling
Prompt tracking won’t become keyword tracking. AI answers are too variable, too personalized, and too dependent on source selection. But that doesn’t make them unmeasurable.
The next iteration of prompt tracking will look less like rank tracking and more like polling: repeated runs, clear sampling rules, confidence intervals, segmented panels, and raw-answer audits.
This post first appeared on the author’s website and is republished here with permission.

AI answers are variable, but measurable. Use repeated runs, confidence intervals, and journey tracking to separate signal from noise.

How to make prompt tracking much more accurate

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920.png" class="attachment-large size-large wp-post-image" alt="Google Search court" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The injunction targets AI-generated accusations that tied two publishers to scams despite no support in the linked pages.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920.png" class="attachment-large size-large wp-post-image" alt="Google Search court" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-court-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google can be directly liable for false claims in AI Overviews, a German court ruled. The Regional Court of Munich found that AI-generated summaries are Google&#8217;s own content and not protected as traditional search results are.</p>



<p>The court issued a temporary injunction barring Google from repeating false claims about two Munich publishers, according to <a href="https://the-decoder.com/landmark-german-ruling-declares-googles-ai-overviews-are-googles-own-words-and-makes-it-liable-for-false-answers/" target="_blank" rel="noopener">The Decoder.</a> This case centered around AI Overviews wrongly linking the publishers to scams, subscription traps, and questionable business practices.</p>



<p><strong>AI Overviews aren</strong>&#8216;<strong>t search results. </strong>AI Overviews do more than help users find third-party content, the court said. They rewrite, combine, and evaluate information “in its own words and according to its own structure.”</p>



<ul class="wp-block-list">
<li>In the disputed searches, Google&#8217;s AI Overview allegedly presented standalone claims about questionable business practices, along with warnings and red flags. The court found those claims didn&#8217;t appear in the linked sources.</li>



<li>Because Google created the feature, controls its presentation, and controls the underlying algorithms, the court treated the statements as Google&#8217;s own content.</li>
</ul>



<p id="h-search-protections-did-not-apply"><strong>Search protections didn&#8217;t apply. </strong>Google argued that German case law limiting liability for traditional search engines and autocomplete should apply. Those rules generally treat search providers as indirect infringers when they surface third-party content.</p>



<ul class="wp-block-list">
<li>The court rejected that argument. Unlike traditional search results, which direct users to external pages, AI Overviews generate new substantive statements from multiple sources.</li>



<li>The court also rejected Google&#8217;s claim that users could verify the information by reviewing the cited links. It found the AI Overview stood on its own and presented its claims as a complete answer.</li>
</ul>



<p><strong>Why we care. </strong>The ruling doesn&#8217;t treat AI Overviews like neutral search links. If an AI-generated summary makes false claims about a company, Google may be directly liable for those statements.</p>



<p><strong>Wrong links, wrong claims. </strong>The court found that the AI Overview conflated information about other companies with that of the two publishers and created unsupported connections to the source material.</p>



<ul class="wp-block-list">
<li>Because the linked sites didn&#8217;t make the disputed claims, the publishers would have had no clear third party to sue if Google were treated only as a search intermediary.</li>



<li>The court said Google can compare AI-generated statements with underlying sources, at least in cases like this.</li>
</ul>



<p><strong>Google must stop repeating the claims. </strong>The injunction bars Google from repeating most of the challenged claims, including allegations involving scams, subscription traps, questionable company ties, phone calls that allegedly never occurred, and lack of availability.</p>



<ul class="wp-block-list">
<li>Google must pay 80% of the legal costs. Each publisher must pay 10%.</li>



<li>The court said the risk of repeat violations remains because Google did not provide a cease-and-desist declaration with a penalty clause, and the same algorithms could generate similar claims again.</li>



<li>The ruling is temporary and may still be challenged. However, it gives publishers and brands a path to challenge false AI Overviews as Google&#8217;s own statements rather than merely search results.</li>
</ul>


Google can be directly liable for false claims in AI Overviews, a German court ruled. The Regional Court of Munich found that AI-generated summaries are Google’s own content and not protected as traditional search results are.
The court issued a temporary injunction barring Google from repeating false claims about two Munich publishers, according to The Decoder. This case centered around AI Overviews wrongly linking the publishers to scams, subscription traps, and questionable business practices.
AI Overviews aren‘t search results. AI Overviews do more than help users find third-party content, the court said. They rewrite, combine, and evaluate information “in its own words and according to its own structure.”
In the disputed searches, Google’s AI Overview allegedly presented standalone claims about questionable business practices, along with warnings and red flags. The court found those claims didn’t appear in the linked sources.
Because Google created the feature, controls its presentation, and controls the underlying algorithms, the court treated the statements as Google’s own content.
Search protections didn’t apply. Google argued that German case law limiting liability for traditional search engines and autocomplete should apply. Those rules generally treat search providers as indirect infringers when they surface third-party content.
The court rejected that argument. Unlike traditional search results, which direct users to external pages, AI Overviews generate new substantive statements from multiple sources.
The court also rejected Google’s claim that users could verify the information by reviewing the cited links. It found the AI Overview stood on its own and presented its claims as a complete answer.
Why we care. The ruling doesn’t treat AI Overviews like neutral search links. If an AI-generated summary makes false claims about a company, Google may be directly liable for those statements.
Wrong links, wrong claims. The court found that the AI Overview conflated information about other companies with that of the two publishers and created unsupported connections to the source material.
Because the linked sites didn’t make the disputed claims, the publishers would have had no clear third party to sue if Google were treated only as a search intermediary.
The court said Google can compare AI-generated statements with underlying sources, at least in cases like this.
Google must stop repeating the claims. The injunction bars Google from repeating most of the challenged claims, including allegations involving scams, subscription traps, questionable company ties, phone calls that allegedly never occurred, and lack of availability.
Google must pay 80% of the legal costs. Each publisher must pay 10%.
The court said the risk of repeat violations remains because Google did not provide a cease-and-desist declaration with a penalty clause, and the same algorithms could generate similar claims again.
The ruling is temporary and may still be challenged. However, it gives publishers and brands a path to challenge false AI Overviews as Google’s own statements rather than merely search results.

The injunction targets AI-generated accusations that tied two publishers to scams despite no support in the linked pages.

Google can be directly liable for false AI Overview claims: German court

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable.png" class="attachment-large size-large wp-post-image" alt="How to make SEO reports more actionable" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Turn keyword research, audits, and technical findings into clear recommendations stakeholders can prioritize and execute.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable.png" class="attachment-large size-large wp-post-image" alt="How to make SEO reports more actionable" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-make-SEO-reports-more-actionable-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><a href="https://searchengineland.com/guide/seo-reporting">SEO reports</a> often contain solid research, including keyword data, technical findings, competitor insights, content gaps, and recommendations. The problem starts when <a href="https://searchengineland.com/guide/seo-stakeholders">stakeholders</a> finish reading and still don&#8217;t know what should happen next.</p>



<p>For example, a report might say internal linking should be improved, but not which pages should be linked, who should make the changes, when the work should happen, or what results to expect. It might identify a crawl issue, but not explain whether fixing it matters more than the content or commercial page gaps already blocking growth.</p>



<p>This is where many SEO reports lose impact. The analysis may be correct, but the decisions and action plan are still unclear.</p>



<p>A strong SEO report should help readers understand what matters now, why it matters to the business, and what should happen next. It should reduce the need for another round of interpretation before work can begin.</p>



<h2 class="wp-block-heading" id="h-research-is-useful-but-it-s-not-the-final-output">Research is useful, but it’s not the final output</h2>



<p>Keyword research, SERP analysis, technical crawls, competitor reviews, and content audits are all important SEO activities. They uncover gaps, risks, and opportunities that would otherwise remain hidden.</p>



<p>But those inputs shouldn&#8217;t dominate the final report.</p>



<p>Stakeholders don&#8217;t need to see every export, screenshot, or crawl detail. They need the conclusions that come from the research. They need to understand which findings matter, which improvements can wait, and which actions deserve priority.</p>



<p>For example, a crawl might identify 300 pages with missing meta descriptions. That finding is only useful if the report explains whether those pages matter. Missing descriptions on low-value archive pages may not deserve immediate attention. Missing descriptions on high-intent service pages with strong impressions may be worth fixing quickly.</p>



<p>The same applies to keyword gaps. A list of 200 missed keywords is less useful than identifying the five opportunities that align with commercial intent, existing authority, and realistic execution capacity.</p>



<p>A useful SEO report shows the work behind a recommendation only when it helps stakeholders understand why it deserves priority.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/stop-reporting-traffic-464083">Stop reporting traffic and activity. Start reporting progress.</a></em></strong>



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<h2 class="wp-block-heading" id="h-where-seo-reports-lose-stakeholders">Where SEO reports lose stakeholders</h2>



<p>Most SEO reports lose momentum when the findings are too generic, the recommendations aren&#8217;t actionable, or the priorities aren&#8217;t clear. The following are among the most common reasons reports fail to drive action.</p>



<h3 class="wp-block-heading" id="h-the-takeaway-could-apply-to-any-website">The takeaway could apply to any website</h3>



<p>Many SEO reports lose impact when the main takeaways are too broad.</p>



<p>Phrases like &#8220;improve content quality,&#8221; &#8220;strengthen internal linking,&#8221; and &#8220;target high-intent keywords&#8221; may be correct, but they don&#8217;t tell the reader anything specific about their site compared to others. These recommendations are too broad because they could apply to almost any website.</p>



<p>A stronger takeaway explains what&#8217;s different about the website, the market, or the opportunity.</p>



<p>For example:</p>



<ul class="wp-block-list">
<li><strong>Weak:</strong> &#8220;Create more bottom-funnel content.&#8221;</li>



<li><strong>Strong:</strong> &#8220;Competitors are winning comparison and pricing-intent queries, while your site mostly ranks for educational searches. The next content priority should be three comparison pages that connect to existing high-traffic guides.&#8221;</li>
</ul>



<p>The second version gives stakeholders a clearer reason to act. It explains the gap, the business relevance, and the first move.</p>



<h3 class="wp-block-heading" id="h-the-recommendation-stops-before-the-work-begins">The recommendation stops before the work begins</h3>



<p>A recommendation can sound useful but still be difficult to execute.</p>



<p>&#8220;Improve internal linking to commercial pages&#8221; is a common example. It identifies the right area, but leaves too many open questions:</p>



<ul class="wp-block-list">
<li>Which pages should link?</li>



<li>Which commercial pages matter most?</li>



<li>Who updates the copy?</li>



<li>When should it be done?</li>



<li>How will impact be measured?</li>
</ul>



<p>A more useful recommendation would look like this:</p>



<ul class="wp-block-list">
<li>“<strong>Week 1:</strong> Map the top 10 informational pages by organic clicks to the three commercial pages with the highest lead value.</li>



<li><strong>Week 2:</strong> Add contextual links using descriptive anchor text. SEO owns the mapping and QA. Content owns the copy updates. Review impact after four to six weeks through crawl depth, impressions, and ranking movement.”</li>
</ul>



<p>That level of detail helps work begin without another strategy meeting.</p>



<h3 class="wp-block-heading" id="h-priorities-are-listed-not-sequenced">Priorities are listed, not sequenced</h3>



<p>Many reports list 15 or 20 recommendations at the same level of importance. This creates confusion because stakeholders can&#8217;t tell what should happen now, what comes next, and what can wait.</p>



<p>Priority should account for impact, effort, dependency, and timing.</p>



<p>For example, while fixing broken links might be easy, it may not be the highest-impact task. Consolidating cannibalized pages may take longer, but it could create more commercial value if those pages are competing for the same high-intent queries.</p>



<p>A useful report explains the order of operations. It shows which actions create momentum first, which actions depend on other teams, and which improvements should be treated as later-stage work.</p>



<h3 class="wp-block-heading" id="h-tool-outputs-are-mistaken-for-strategy">Tool outputs are mistaken for strategy</h3>



<p>Automated audits can surface useful issues, but they lack business context.</p>



<p>A tool can flag missing schema, duplicate titles, slow pages, and broken links. However, it can&#8217;t reliably determine which issue matters most for a specific business at a specific moment.</p>



<p>For example, a tool may flag site speed as a critical issue. But if competitors in the niche are equally slow and the bigger gap is weak commercial content coverage, site speed may not deserve top priority.</p>



<p>Tools identify potential issues. SEO judgment determines which issues deserve action.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/why-most-seo-failures-are-organizational-not-technical-468167">Why most SEO failures are organizational, not technical</a></em></strong>



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<h2 class="wp-block-heading" id="h-tailor-reports-to-the-stakeholder">Tailor reports to the stakeholder</h2>



<p>A common reason SEO reports fail to drive action is that they treat every stakeholder the same.</p>



<p>The same finding may need to be explained differently depending on who has to act on it. A CEO, a marketing lead, a developer, and a content manager don&#8217;t need the same level of detail. They need the same truth translated into the context of their decisions.</p>



<p>For a CEO or founder, the report should focus on business opportunity, risk, resource needs, and expected impact.</p>



<ul class="wp-block-list">
<li><strong>Example: </strong>&#8220;The site is losing visibility in commercial comparison searches where competitors are capturing high-intent demand. The top priority is building three comparison pages and linking them from existing high-traffic informational content. The pages can be published within four to six weeks, with early visibility signals reviewed after indexing.&#8221;</li>
</ul>



<p>For a marketing lead, the report should connect SEO work to demand generation, campaigns, and content direction.</p>



<ul class="wp-block-list">
<li><strong>Example: </strong>&#8220;The blog attracts early-stage visitors, but these pages don&#8217;t support product discovery. The next step is adding commercial pathways from the top informational pages into product, demo, or comparison content.&#8221;</li>
</ul>



<p>For a developer or product team, the report should remove ambiguity from the technical requirement.</p>



<ul class="wp-block-list">
<li><strong>Example: </strong>&#8220;Update canonical tags on these 12 filtered category URLs to point to the main category page. Acceptance check: The canonical target returns 200, is indexable, and appears consistently in rendered HTML.&#8221;</li>
</ul>



<p>For a content team, the report should make page-level action clear.</p>



<ul class="wp-block-list">
<li><strong>Example: </strong>&#8220;Update this guide to include a comparison section, add two internal links to commercial pages, and answer the pricing-related query that competitors cover but the page currently misses.&#8221;</li>
</ul>



<p>A strong SEO report presents the findings in a way that each stakeholder can act on.</p>



<h2 class="wp-block-heading" id="h-what-a-decision-ready-seo-report-should-show">What a decision-ready SEO report should show</h2>



<p>A useful SEO report should clearly answer a small number of questions. These questions may vary by stakeholder, but the underlying purpose stays the same: helping people decide what to do next.</p>



<h3 class="wp-block-heading" id="h-start-with-the-opportunity">Start with the opportunity</h3>



<p>Where can SEO create business value? This could be a topic cluster where competitors are weak, a set of underperforming commercial pages, or an existing content asset that attracts demand but doesn&#8217;t support conversion.</p>



<h3 class="wp-block-heading" id="h-identify-the-main-constraint">Identify the main constraint</h3>



<p>Is growth blocked by crawlability? Content depth? Competitor dominance? Something structural in how the site maps to buyer intent?</p>



<p>The answer shapes everything that follows.</p>



<p>For example, a B2B site may have strong informational rankings but weak visibility for comparison queries. In that case, the problem isn&#8217;t simply a content gap – it&#8217;s a missing bridge between educational demand and commercial intent.</p>



<h3 class="wp-block-heading" id="h-define-the-first-move">Define the first move</h3>



<p>The report should explain which action deserves priority and why it should happen before other work. This is where impact, effort, dependency, and timing matter.</p>



<p>For example, instead of saying &#8220;create a buyer guide,&#8221; the report should specify which guide to create, which SERP pattern justifies it, which existing pages should link to it, who owns the draft, when it should go live, and what signals should be reviewed after launch.</p>



<h3 class="wp-block-heading" id="h-establish-how-progress-will-be-measured">Establish how progress will be measured</h3>



<p>Early signals may include indexing, crawl depth, impressions, or ranking movement. Later signals may include qualified traffic, demo requests, assisted conversions, or pipeline influence.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-goals-strategy-planning-453492">SEO execution: Understanding goals, strategy, and planning</a></em></strong>



<h2 class="wp-block-heading" id="h-turn-every-finding-into-a-clear-next-step">Turn every finding into a clear next step</h2>



<p>Every important finding in an SEO report should answer three simple questions:</p>



<ul class="wp-block-list">
<li>What did we find?</li>



<li>Why does it matter?</li>



<li>What should happen next?</li>
</ul>



<p>This helps prevent findings from sitting in the report as observations with no clear path forward.</p>



<p>Consider a report that identifies five high-traffic informational pages with no links to relevant commercial pages. That&#8217;s a useful finding, but without interpretation, stakeholders only know what exists. They don&#8217;t know why it matters or what action it should trigger. The site is already winning organic visibility, but the journey from education to action is weak.</p>



<p>The next step should be specific.</p>



<ul class="wp-block-list">
<li><strong>Example:</strong> &#8220;Add contextual links from those five informational pages to the two most relevant commercial destinations. Content owns the updates. SEO reviews the mapping and anchor text. Measure changes in crawl depth, impressions, clicks, and assisted conversions after four to six weeks.&#8221;</li>
</ul>



<p>This turns a finding into work that can be assigned, implemented, and reviewed.</p>



<p>If a finding can&#8217;t be connected to a next step, it may belong in a supporting document rather than the main report.</p>



<h2 class="wp-block-heading" id="h-what-to-cut-from-seo-reports">What to cut from SEO reports</h2>



<p>SEO reports often become clearer when they remove material that doesn&#8217;t help the reader make a decision.</p>



<p>Tool screenshots are a common example. A screenshot can support a point when it shows a clear pattern, but adding screenshots for every issue usually makes the report harder to read. </p>



<p>The same applies to large keyword exports, crawl tables, and raw audit scores. These details are useful as supporting material, but they shouldn&#8217;t dominate the main report.</p>



<p>Generic best-practice advice should also be removed or rewritten. &#8220;Add schema,&#8221; &#8220;improve page speed,&#8221; and &#8220;optimize title tags&#8221; only help when the report explains why those actions matter for this site right now.</p>



<p>Long methodology sections can usually be shortened. Methodology matters when trust, reproducibility, or stakeholder education is needed. But most senior stakeholders don&#8217;t need to see the entire research process. They need the conclusion that came from it.</p>



<p>Before adding something to the main report, ask whether it helps the reader understand the priority or take the next step. If it doesn&#8217;t, it&#8217;s likely better placed in an appendix or supporting document.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-reporting-black-box-era-463461">SEO in the black box era: Why reports will look more like Mad Men than Search Console</a></em></strong>



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<h2 class="wp-block-heading" id="h-the-best-seo-reports-make-the-next-step-obvious">The best SEO reports make the next step obvious</h2>



<p>SEO reporting should reduce uncertainty. After reading a report, stakeholders should understand what matters most and what work should move forward.</p>



<p>That doesn&#8217;t mean every recommendation needs to come with a perfect forecast. SEO rarely works with that level of certainty. But each recommendation should explain the expected direction of impact and the signals that will be used to evaluate progress.</p>



<p>A useful report connects research to decisions. It shows the business context behind the finding, the rationale for prioritizing the recommendation, and the practical path to execution.</p>



<p>When reports stop at analysis, they create more work for the readers. When reports translate analysis into action, they help teams move faster.</p>



<p>The strongest SEO reports leave stakeholders with a clear priority, a practical next step, and a way to judge whether the work is moving in the right direction.</p>

SEO reports often contain solid research, including keyword data, technical findings, competitor insights, content gaps, and recommendations. The problem starts when stakeholders finish reading and still don’t know what should happen next.
For example, a report might say internal linking should be improved, but not which pages should be linked, who should make the changes, when the work should happen, or what results to expect. It might identify a crawl issue, but not explain whether fixing it matters more than the content or commercial page gaps already blocking growth.
This is where many SEO reports lose impact. The analysis may be correct, but the decisions and action plan are still unclear.
A strong SEO report should help readers understand what matters now, why it matters to the business, and what should happen next. It should reduce the need for another round of interpretation before work can begin.
Research is useful, but it’s not the final output
Keyword research, SERP analysis, technical crawls, competitor reviews, and content audits are all important SEO activities. They uncover gaps, risks, and opportunities that would otherwise remain hidden.
But those inputs shouldn’t dominate the final report.
Stakeholders don’t need to see every export, screenshot, or crawl detail. They need the conclusions that come from the research. They need to understand which findings matter, which improvements can wait, and which actions deserve priority.
For example, a crawl might identify 300 pages with missing meta descriptions. That finding is only useful if the report explains whether those pages matter. Missing descriptions on low-value archive pages may not deserve immediate attention. Missing descriptions on high-intent service pages with strong impressions may be worth fixing quickly.
The same applies to keyword gaps. A list of 200 missed keywords is less useful than identifying the five opportunities that align with commercial intent, existing authority, and realistic execution capacity.
A useful SEO report shows the work behind a recommendation only when it helps stakeholders understand why it deserves priority.
Dig deeper: Stop reporting traffic and activity. Start reporting progress.




  
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Where SEO reports lose stakeholders
Most SEO reports lose momentum when the findings are too generic, the recommendations aren’t actionable, or the priorities aren’t clear. The following are among the most common reasons reports fail to drive action.
The takeaway could apply to any website
Many SEO reports lose impact when the main takeaways are too broad.
Phrases like “improve content quality,” “strengthen internal linking,” and “target high-intent keywords” may be correct, but they don’t tell the reader anything specific about their site compared to others. These recommendations are too broad because they could apply to almost any website.
A stronger takeaway explains what’s different about the website, the market, or the opportunity.
For example:
Weak: “Create more bottom-funnel content.”
Strong: “Competitors are winning comparison and pricing-intent queries, while your site mostly ranks for educational searches. The next content priority should be three comparison pages that connect to existing high-traffic guides.”
The second version gives stakeholders a clearer reason to act. It explains the gap, the business relevance, and the first move.
The recommendation stops before the work begins
A recommendation can sound useful but still be difficult to execute.
“Improve internal linking to commercial pages” is a common example. It identifies the right area, but leaves too many open questions:
Which pages should link?
Which commercial pages matter most?
Who updates the copy?
When should it be done?
How will impact be measured?
A more useful recommendation would look like this:
“Week 1: Map the top 10 informational pages by organic clicks to the three commercial pages with the highest lead value.
Week 2: Add contextual links using descriptive anchor text. SEO owns the mapping and QA. Content owns the copy updates. Review impact after four to six weeks through crawl depth, impressions, and ranking movement.”
That level of detail helps work begin without another strategy meeting.
Priorities are listed, not sequenced
Many reports list 15 or 20 recommendations at the same level of importance. This creates confusion because stakeholders can’t tell what should happen now, what comes next, and what can wait.
Priority should account for impact, effort, dependency, and timing.
For example, while fixing broken links might be easy, it may not be the highest-impact task. Consolidating cannibalized pages may take longer, but it could create more commercial value if those pages are competing for the same high-intent queries.
A useful report explains the order of operations. It shows which actions create momentum first, which actions depend on other teams, and which improvements should be treated as later-stage work.
Tool outputs are mistaken for strategy
Automated audits can surface useful issues, but they lack business context.
A tool can flag missing schema, duplicate titles, slow pages, and broken links. However, it can’t reliably determine which issue matters most for a specific business at a specific moment.
For example, a tool may flag site speed as a critical issue. But if competitors in the niche are equally slow and the bigger gap is weak commercial content coverage, site speed may not deserve top priority.
Tools identify potential issues. SEO judgment determines which issues deserve action.
Dig deeper: Why most SEO failures are organizational, not technical




	
	
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Tailor reports to the stakeholder
A common reason SEO reports fail to drive action is that they treat every stakeholder the same.
The same finding may need to be explained differently depending on who has to act on it. A CEO, a marketing lead, a developer, and a content manager don’t need the same level of detail. They need the same truth translated into the context of their decisions.
For a CEO or founder, the report should focus on business opportunity, risk, resource needs, and expected impact.
Example: “The site is losing visibility in commercial comparison searches where competitors are capturing high-intent demand. The top priority is building three comparison pages and linking them from existing high-traffic informational content. The pages can be published within four to six weeks, with early visibility signals reviewed after indexing.”
For a marketing lead, the report should connect SEO work to demand generation, campaigns, and content direction.
Example: “The blog attracts early-stage visitors, but these pages don’t support product discovery. The next step is adding commercial pathways from the top informational pages into product, demo, or comparison content.”
For a developer or product team, the report should remove ambiguity from the technical requirement.
Example: “Update canonical tags on these 12 filtered category URLs to point to the main category page. Acceptance check: The canonical target returns 200, is indexable, and appears consistently in rendered HTML.”
For a content team, the report should make page-level action clear.
Example: “Update this guide to include a comparison section, add two internal links to commercial pages, and answer the pricing-related query that competitors cover but the page currently misses.”
A strong SEO report presents the findings in a way that each stakeholder can act on.
What a decision-ready SEO report should show
A useful SEO report should clearly answer a small number of questions. These questions may vary by stakeholder, but the underlying purpose stays the same: helping people decide what to do next.
Start with the opportunity
Where can SEO create business value? This could be a topic cluster where competitors are weak, a set of underperforming commercial pages, or an existing content asset that attracts demand but doesn’t support conversion.
Identify the main constraint
Is growth blocked by crawlability? Content depth? Competitor dominance? Something structural in how the site maps to buyer intent?
The answer shapes everything that follows.
For example, a B2B site may have strong informational rankings but weak visibility for comparison queries. In that case, the problem isn’t simply a content gap – it’s a missing bridge between educational demand and commercial intent.
Define the first move
The report should explain which action deserves priority and why it should happen before other work. This is where impact, effort, dependency, and timing matter.
For example, instead of saying “create a buyer guide,” the report should specify which guide to create, which SERP pattern justifies it, which existing pages should link to it, who owns the draft, when it should go live, and what signals should be reviewed after launch.
Establish how progress will be measured
Early signals may include indexing, crawl depth, impressions, or ranking movement. Later signals may include qualified traffic, demo requests, assisted conversions, or pipeline influence.
Dig deeper: SEO execution: Understanding goals, strategy, and planning



Turn every finding into a clear next step
Every important finding in an SEO report should answer three simple questions:
What did we find?
Why does it matter?
What should happen next?
This helps prevent findings from sitting in the report as observations with no clear path forward.
Consider a report that identifies five high-traffic informational pages with no links to relevant commercial pages. That’s a useful finding, but without interpretation, stakeholders only know what exists. They don’t know why it matters or what action it should trigger. The site is already winning organic visibility, but the journey from education to action is weak.
The next step should be specific.
Example: “Add contextual links from those five informational pages to the two most relevant commercial destinations. Content owns the updates. SEO reviews the mapping and anchor text. Measure changes in crawl depth, impressions, clicks, and assisted conversions after four to six weeks.”
This turns a finding into work that can be assigned, implemented, and reviewed.
If a finding can’t be connected to a next step, it may belong in a supporting document rather than the main report.
What to cut from SEO reports
SEO reports often become clearer when they remove material that doesn’t help the reader make a decision.
Tool screenshots are a common example. A screenshot can support a point when it shows a clear pattern, but adding screenshots for every issue usually makes the report harder to read. 
The same applies to large keyword exports, crawl tables, and raw audit scores. These details are useful as supporting material, but they shouldn’t dominate the main report.
Generic best-practice advice should also be removed or rewritten. “Add schema,” “improve page speed,” and “optimize title tags” only help when the report explains why those actions matter for this site right now.
Long methodology sections can usually be shortened. Methodology matters when trust, reproducibility, or stakeholder education is needed. But most senior stakeholders don’t need to see the entire research process. They need the conclusion that came from it.
Before adding something to the main report, ask whether it helps the reader understand the priority or take the next step. If it doesn’t, it’s likely better placed in an appendix or supporting document.
Dig deeper: SEO in the black box era: Why reports will look more like Mad Men than Search Console




  
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The best SEO reports make the next step obvious
SEO reporting should reduce uncertainty. After reading a report, stakeholders should understand what matters most and what work should move forward.
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<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-real-people-actually-prompt-AI-—-and-what-it-means-for-GEO.png" class="attachment-large size-large wp-post-image" alt="How real people actually prompt AI — and what it means for GEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-real-people-actually-prompt-AI-—-and-what-it-means-for-GEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-real-people-actually-prompt-AI-—-and-what-it-means-for-GEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-real-people-actually-prompt-AI-—-and-what-it-means-for-GEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-real-people-actually-prompt-AI-—-and-what-it-means-for-GEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Survey data reveals how users combine short queries with personal context — and why that changes how brands earn visibility in AI search.

Most people aren’t using AI the way GEO discussions often assume. Two surveys of AI users conducted by Stella Rising found that many prompts still look remarkably similar to traditional search queries. (Disclosure: I’m the VP of SEO at Stella Rising.)



One survey focused on a beauty-oriented consumer panel in August 2025, while the other surveyed a broader general-audience population in January 2026. Across both studies, prompts were short, often keyword-driven, and much closer to a Google search than the elaborate prompt templates popular in AI marketing circles.
At the same time, a growing share of users are adding personal context, such as their budget, location, profession, age, health concerns, or preferences. Those details give AI systems far more information than a traditional search query ever could, creating a new layer of personalization that influences recommendations and brand visibility.
The combined findings suggest that GEO strategies need to account for both realities: Many AI searches still resemble classic keyword queries, while the highest-value recommendations increasingly emerge from prompts rich with personal context. That’s where the opportunity — and the measurement challenge — lies.
A lot of people are still typing like it’s 2008
The biggest takeaway across both surveys is that the median AI user is still throwing a keyword over the wall and hoping for the best.
In the general-audience study from January:
Two-thirds of respondents reported writing prompts of 15 words or fewer. 
Only 12% wrote something that would qualify as a “real” prompt by the standards of an AI influencer thread. 
About 60% phrased their queries as questions, while only 9% gave a direct command. 
That mirrors what Pew Research has been seeing more broadly — 34% of all U.S. adults now use ChatGPT, roughly double the 2023 share, and 58% of adults under 30 use it.
When we ran a scenario task asking respondents to write the prompt they’d send if they needed a new pair of shoes, the median answer was eight words. Real examples from the panel included:
“Shoes nearby”
“Tennis shoes”
“Nike”
“Ladies tennis shoes size 7 near me”
“Best price for hiking shoes”
This lines up with Semrush’s clickstream data on ChatGPT’s search mode, which shows the average prompt length is 4.2 to 8.7 words, essentially the same as a Google query.
Longer, structured prompts tend to appear only when users are doing something other than search, such as drafting, coding, or creative work.
For AEO and GEO work, that’s the part to internalize. If you’re optimizing for prompts like “Compare the top five orthopedic-approved walking shoes under $150 for plantar fasciitis with 4.5+ star ratings,” you’re optimizing for the wrong distribution. 
Real prompts run 71% longer than the synthetic ones marketers tend to invent, but the median is still only 12 words, Otterly.AI’s analysis found.

  
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The shift between the two surveys
In the August 2025 survey, we classified roughly 50% of the free-text prompts as “SEO-keyword-shaped,” meaning short, ambiguous, and brand-and-attribute-driven. By the time the January 2026 survey came back, that share had dropped closer to 30%. The remaining 70% had grown longer and more contextualized.
A few findings are worth carrying forward:
24.5% of all prompts include the word “best.” If you’re not appearing in “best [category]” responses, you’re missing one of the highest-intent slots.
28% of prompts mention price or budget constraints. Users aren’t just shopping. They’re shopping with a number in their head.
16% of prompts are explicitly location-based. The “near me” query pattern has successfully migrated from Google to LLMs.
32% of prompts include personal attributes (e.g., size, profession, health condition, life stage, etc.). This is the most important number on the page, and we’ll come back to it.
On location specifically, the 16% figure lines up with what Local Falcon’s 2025 research showed for AI search overall: AI Overviews now appear on 92% of informational local queries, but only on 15% of simple local-pack queries. The intent is moving into LLMs faster than the supply of optimized local content for AI engines.
One caveat: These were two different surveys with two different audiences. The January 2026 general-audience sample was structurally more transactional than the August 2025 beauty-focused panel, which partially explains why fewer prompts looked like keyword-style searches and more looked like full requests. I wouldn’t over-index on the “prompts are evolving” narrative, but I’d absolutely take the directional read.
The user embedding layer is where this gets interesting
The 32% figure (prompts containing real personal context) is the most under-discussed finding in the dataset.
Nearly one-third of users are willingly handing LLMs information that no Google query would normally carry, such as their size, job, training plan, living situation, or kids’ ages. We see prompts in the data like:
“What shoes would you recommend for daily standing at work?”
“Find me a cost-effective pair of running shoes that I can order on Amazon. My size is men’s 10.”
“Please tell me the top five shoes for wide feet in a size eight for women that are comfortable, stylish, under $120, and that younger people won’t make fun of for a Gen X person like me.”
That last one alone packs in gender, foot width, size, budget, style intent, generational identity, and a real social anxiety. No traditional search query was ever going to surface all of that.
This is the user embedding layer at work. When someone interacts with ChatGPT or Gemini repeatedly, the model builds a profile of who they are, which increasingly persists through memory. The user is, in effect, training the assistant on themselves. Once that trust is established, they stop writing surface queries and start writing requests that assume the assistant knows them.
That shift has two implications for how brands should think about visibility:
The prompts that drive purchase decisions are often not the ones that show up in a SERP or keyword tool. A real Gen X woman asking about wide-fit, $120, “won’t-get-made-fun-of” sneakers will never appear as a tracked SERP keyword. But that’s the prompt that decides whether your shoe ends up in the recommendation set.
The value of a brand citation increases substantially when it appears inside a context-rich prompt. If the model is already factoring in user attributes, the brands it surfaces are prefiltered for relevance. That’s a much higher-quality impression than a generic blue link.
Where synthetic prompts fit — and where they don’t
A common tactic in GEO prompt research is to construct synthetic personas (“I’m a 38-year-old product manager training for a half marathon in Boston who prefers brands focused on sustainability…”) and then use those personas to stress-test which brands an LLM surfaces under different scenarios. There’s real merit to the approach. If the user embedding layer is doing the heavy lifting in the answer, the only way to simulate the answer is to simulate the user.
But synthetic prompts don’t capture everything. Real prompts are messy, layered, and influenced by recent conversation history, persistent memory, and signals the model has picked up over weeks of use. You can craft a 50-word persona and still miss the nuance of a user who has been talking to ChatGPT about their day, preferences, and family for 6 months.
Instead, use synthetic prompts to map the personas your brand needs to be visible to, but don’t treat the resulting visibility scores as ground truth. Combine them with real prompt data wherever possible. That can mean customer interviews, social search patterns, support tickets, or regex pulls of question-shaped queries from Google Search Console.
What to actually track
This naturally leads to the next question: Should you track SEO keywords in your AI visibility platform if one-third of real prompts look like SEO keywords?
The answer is yes, with one filter.
Across the last quarter, our team has seen web retrieval rates on tracked prompts climb sharply. On several client accounts, more than 90% of monitored prompts now trigger live web search inside ChatGPT or Google’s AI Mode. 
When that happens, the LLM is effectively running a real-time SERP and synthesizing the result. That means the short, keyword-shaped prompts we identified — roughly 30% of the total — are still very much in play. They behave like AI-flavored Google queries and should be tracked accordingly.
The filter is this: Don’t waste tracking slots on prompts that are pure head terms or single-brand queries. Those are likely to be answered from model weights or short canned responses rather than retrieval, and they won’t give you a useful read on visibility.
Here’s a practical setup:
A synthetic-persona prompt set that exercises the user embedding layer, mapped to the personas your brand actually needs to win. Use this to surface which competitors a model defaults to under different user conditions.
A real-prompt set sourced from question-shaped GSC queries, customer panel inputs, and regex-extracted “who/what/where/can/should” patterns. These are the short, retrieval-triggering prompts most users still write.
A small qualitative library of messy, context-rich real prompts pulled from the type of work we did for the study. Use it to sanity-check whether your content actually answers the question the user is asking, not the question your keyword tool says they’re asking.
At that point, you’re not just tracking AI visibility. You’re tracking it across the full spectrum of how real users get to your content, from a three-word “good walking shoes” query to a 40-word “I’m a 60-year-old with plantar fasciitis…” request.
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What the broader data tells us about AI search
A handful of additional findings from the January 2026 survey help explain why these prompt patterns matter.
Users increasingly trust AI recommendations
Up to 68% of users trust ChatGPT’s recommendations more than Google’s, with most citing detail, lack of ads, and personalization as the reasons. 
NIM’s research has found ChatGPT often produces more efficient, accurate consumer decisions than Google.
A January 2026 study reported that trust in Google weakens as AI usage rises. 
Yext’s 2026 research puts ChatGPT’s share of “top-quality source” responses at 35% among heavy AI users.
AI search is becoming a daily habit
Half of active AI users use these tools daily or several times per day to complete tasks they used to do on Google.
Search Engine Land reported 37% of consumers now start searches with an AI tool instead of Google.
OpenAI’s February 2026 numbers put ChatGPT’s weekly active users at 900 million — more than double a year earlier.
Citations still drive traffic
85% of users click through to cited sources at least some of the time; 21.9% always do. The mention is not the end of the funnel. 
Conductor’s 2026 benchmarks showed AI referral traffic up 357% year-over-year. 
Semrush reported outbound referrals from ChatGPT up 206% in 2025. 
Emarketed saw AI-referred visitors converting at 4.4x the rate of standard organic. 
Volume is still small (Conductor pegs it at around 1.08% of total traffic), but it punches well above its weight class.
Voice may finally be having its moment
34% of users are now using voice chat daily or more often. This is the first dataset I’ve seen that actually delivers on the “voice search will matter” promise we’ve been hearing for a decade. 
It’s worth pairing all of this with Ahrefs’ latest AI Overviews CTR research: The presence of an AI Overview correlates with a 58% lower clickthrough rate for the top-ranking page. The traffic that does come through is qualified. The traffic that doesn’t is gone
AI search is settling into a richer, more personalized form. The intent stack is the same one Google has always served. What’s new is the embedding layer and the tracking demands it entails. That creates a clear set of priorities for SEO and GEO teams.

  
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What changes — and what doesn’t
Here are three things you can do with this information if you’re an SEO lead, content lead, or strategy lead:
Audit your prompt-tracking setup: If it’s all synthetic prompts or all keyword-shaped prompts, you’re missing half the picture. Build the layered framework outlined above.
Map your content to the user embedding layer: For your top categories, list the personas (e.g., age, life stage, profession, condition, budget) most likely to carry real prompts into AI search. Then check whether your PDPs, blog content, and FAQs actually answer those people’s questions.
Don’t abandon the SEO-keyword work: Roughly one-third of real prompts still look like classic search queries. With web retrieval running at 90%+ on many of the prompts we monitor, the gap between an SEO keyword and an AI prompt is narrower than the GEO discourse implies.
The behavior change is real. The sophistication of AI thought leaders’ prompting is partly here and partly oversold. Most people are still doing Google-style searches. They’re just searching inside an interface that knows more about them.
If that’s where the audience is, that’s where we have to optimize.
Methodology
Both studies referenced in this article were conducted by the Stella Rising team. You can read it in “New Data: How Consumers Use LLMs for Search in 2026 (And What It Means for GEO).” 
The August 2025 study surveyed 178 members of Stella’s Glimmer Insights community, 113 of whom were active LLM users. 
The January 2026 study surveyed 524 active LLM users via Centiment, defined as having used ChatGPT, Copilot, or Gemini in the previous 30 days, with a margin of error of approximately ±4.3% at the 95% confidence level. 
Given its smaller size and category-specific composition, the August 2025 panel should be viewed as directional rather than statistically representative of the broader U.S. AI user population.

Survey data reveals how users combine short queries with personal context — and why that changes how brands earn visibility in AI search.

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