<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg" class="attachment-large size-large wp-post-image" alt="Google Shopping Ads - Google Ads" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is testing Merchant Advisor, an AI assistant inside Merchant Center that helps retailers manage setup and optimization tasks.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg" class="attachment-large size-large wp-post-image" alt="Google Shopping Ads - Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is expanding its push into AI assistants for advertisers and retailers, embedding guidance tools directly into Merchant Center to simplify setup, troubleshooting and optimization.</p>



<p id="h-what-s-happening"><strong>What’s happening.</strong> Google has been spotted testing Merchant Advisor, an AI-powered chatbot integrated into Google Merchant Center. The feature is currently in beta and appears designed to provide merchants with personalized recommendations and support inside the platform.</p>



<p id="h-how-it-works"><strong>How it works.</strong> Merchant Advisor proactively surfaces tasks and suggestions, such as setting up a returns policy or completing account configuration steps. The goal is to give merchants an always-on assistant that can help improve feed quality and overall account health.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/05/ppc-news-merchant-advisor-chatbot-1.jpg" alt="" class="wp-image-477401" srcset="https://searchengineland.com/wp-content/seloads/2026/05/ppc-news-merchant-advisor-chatbot-1.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/05/ppc-news-merchant-advisor-chatbot-1-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/ppc-news-merchant-advisor-chatbot-1-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p id="h-how-it-works"><strong>The bigger trend.</strong> The rollout fits into Google’s broader strategy of embedding AI assistants across its marketing products. Merchant Advisor follows earlier launches like Google Ads Advisor and Analytics Advisor, signaling a future where AI copilots become standard across campaign management and measurement tools.</p>



<p id="h-between-the-lines"><strong>Between the lines. </strong>Merchant Center can be technically complex, especially for smaller retailers managing feeds, policies and diagnostics. An embedded AI assistant could reduce friction, speed up onboarding and help merchants identify optimization opportunities they might otherwise miss.</p>



<p id="h-spotted-by"><strong>Spotted by. </strong>The feature was first seen live by <a href="https://momenticmarketing.com/seo-news/1777904060-065729">Tamara Hellgren</a> after earlier references surfaced in a Google Ads Decoded podcast episode focused on retail.</p>



<p id="h-the-bottom-line"><strong>The bottom line.</strong> Google is turning Merchant Center into a more guided, AI-assisted experience, part of a larger shift toward automation and embedded support across its advertising ecosystem.</p>


Google is expanding its push into AI assistants for advertisers and retailers, embedding guidance tools directly into Merchant Center to simplify setup, troubleshooting and optimization.
What’s happening. Google has been spotted testing Merchant Advisor, an AI-powered chatbot integrated into Google Merchant Center. The feature is currently in beta and appears designed to provide merchants with personalized recommendations and support inside the platform.
How it works. Merchant Advisor proactively surfaces tasks and suggestions, such as setting up a returns policy or completing account configuration steps. The goal is to give merchants an always-on assistant that can help improve feed quality and overall account health.




The bigger trend. The rollout fits into Google’s broader strategy of embedding AI assistants across its marketing products. Merchant Advisor follows earlier launches like Google Ads Advisor and Analytics Advisor, signaling a future where AI copilots become standard across campaign management and measurement tools.
Between the lines. Merchant Center can be technically complex, especially for smaller retailers managing feeds, policies and diagnostics. An embedded AI assistant could reduce friction, speed up onboarding and help merchants identify optimization opportunities they might otherwise miss.
Spotted by. The feature was first seen live by Tamara Hellgren after earlier references surfaced in a Google Ads Decoded podcast episode focused on retail.
The bottom line. Google is turning Merchant Center into a more guided, AI-assisted experience, part of a larger shift toward automation and embedded support across its advertising ecosystem.

Google is testing Merchant Advisor, an AI assistant inside Merchant Center that helps retailers manage setup and optimization tasks.

Google tests Merchant Advisor inside Merchant Center

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="6 mistakes that hurt ecommerce campaigns on Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google says Search Query Reports may not show exact searches as AI-driven intent matching expands in Google Ads.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="6 mistakes that hurt ecommerce campaigns on Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google Search Query Reports are becoming less literal and more AI-interpreted, reflecting inferred intent rather than exact user searches.</p>



<p><strong>What’s happening. </strong>Google has clarified that the search terms shown in Search Query Reports may not exactly match what users typed. Instead, the platform may display the “closest approximation” of a query because of the complexity of modern search behavior.</p>



<p><strong>What’s behind it.</strong> The change reflects how heavily AI now influences Google Ads matching systems. Rather than relying solely on exact keywords, Google increasingly interprets user intent, context and behavior signals to determine which ads to show.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/05/google-advertising-search-terms-screenshot.jpg" alt="" class="wp-image-477394" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-advertising-search-terms-screenshot.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/05/google-advertising-search-terms-screenshot-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-advertising-search-terms-screenshot-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p><strong>Why we care. </strong>For advertisers, this means Search Query Reports may become less of a direct mirror of user language and more of a summarized representation of intent. That could make query analysis, negative keyword decisions and match-type strategy more complicated and less reliable.</p>



<p><strong>Discovered by.</strong> The update was spotted by Adsquire founder, <a href="https://www.linkedin.com/posts/anthony-higman-a3656a46_did-you-know-that-in-google-advertising-things-activity-7459995441234083840-RCmy">Anthony Higman</a> on an <a href="https://support.google.com/google-ads/answer/2756257?hl=en">official Google help page covering ad group and asset group prioritization within Google Ads</a>.</p>



<p><strong>The bottom line.</strong> Google Ads is continuing its shift from keyword matching toward AI-driven intent modeling — and advertisers may now have less visibility into the exact searches triggering their ads.</p>


Google Search Query Reports are becoming less literal and more AI-interpreted, reflecting inferred intent rather than exact user searches.
What’s happening. Google has clarified that the search terms shown in Search Query Reports may not exactly match what users typed. Instead, the platform may display the “closest approximation” of a query because of the complexity of modern search behavior.
What’s behind it. The change reflects how heavily AI now influences Google Ads matching systems. Rather than relying solely on exact keywords, Google increasingly interprets user intent, context and behavior signals to determine which ads to show.




Why we care. For advertisers, this means Search Query Reports may become less of a direct mirror of user language and more of a summarized representation of intent. That could make query analysis, negative keyword decisions and match-type strategy more complicated and less reliable.
Discovered by. The update was spotted by Adsquire founder, Anthony Higman on an official Google help page covering ad group and asset group prioritization within Google Ads.
The bottom line. Google Ads is continuing its shift from keyword matching toward AI-driven intent modeling — and advertisers may now have less visibility into the exact searches triggering their ads.

Google says Search Query Reports may not show exact searches as AI-driven intent matching expands in Google Ads.

Google says Search Query Reports may not show actual user searches

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/google-zero.png" class="attachment-large size-large wp-post-image" alt="Google zero" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-zero.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-zero-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-zero-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/google-zero-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>After years of Google algorithm updates and huge SERP changes, Condé Nast now plans its business as if search traffic barely exists.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/google-zero.png" class="attachment-large size-large wp-post-image" alt="Google zero" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-zero.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-zero-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-zero-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/google-zero-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Condé Nast now plans its business “as if search is zero” after years of Google algorithm updates and AI Overviews reducing visits to publisher websites. That&#8217;s according to CEO Roger Lynch, who was interviewed on TBPN, the tech media network that bills itself as “technology’s daily show” and was acquired by OpenAI in April.</p>



<p><strong>What he&#8217;s saying.</strong> Condé Nast doesn’t expect Google traffic to disappear completely, but no longer considers search a reliable channel, Lynch said:</p>



<ul class="wp-block-list">
<li>“Last year, I told our teams: assume there’s no search. You have to have your businesses planned as if search is zero.”</li>



<li>“We don&#8217;t expect it to be zero&#8230; We expect it to be a single-digit percentage of our traffic. Very low.”</li>
</ul>



<p><strong>The context. </strong>Lynch described a multi-year pattern where Google consistently cut publisher visibility more than expected:</p>



<ul class="wp-block-list">
<li>“Each of the last three years, we would do our budgets, and we&#8217;d put some forecasts in of search traffic declining… and then every year it was down more than we forecast.”</li>
</ul>



<p><strong>Why did Condé Nast&#8217;s search traffic decline?</strong> In addition to algorithm updates, he blamed AI Overviews and Google’s increasingly commercial search results.</p>



<ul class="wp-block-list">
<li>Search results from seven or eight years ago showed a few sponsored links then “10 blue links.”</li>



<li>Today, users first see AI Overviews, then “rows and rows and rows of commerce links,” Lynch said.</li>



<li>Organic links appear much farther down the page.</li>
</ul>



<ul class="wp-block-list">
<li>&#8220;It’s been great for Google,&#8221; Lynch said. </li>
</ul>



<p><strong>A different era.</strong> Google&#8217;s changes broke the old model that companies like BuzzFeed used to turn Google and Facebook traffic into money, Lynch said. </p>



<ul class="wp-block-list">
<li>“That era is gone.”</li>
</ul>



<p>Lynch said brands caught “in the middle” are struggling most in the AI and search transition.</p>



<ul class="wp-block-list">
<li>&#8220;Today, you need to be really nailing a specific niche where you have a loyal audience that&#8217;s willing to pay and and &#8230; If you have a brand where you&#8217;re investing in the journalism, if you have to make significant investments in journalism, supporting that just with advertising is a tough place to be.&#8221;</li>
</ul>



<p><strong>Condé Nast’s response.</strong> It has been to prioritize brands with:</p>



<ul class="wp-block-list">
<li>Strong direct audiences.</li>



<li>Subscription potential.</li>



<li>Clear authority in a niche or category.</li>
</ul>



<p>He also said that AI-generated “slop” could ultimately help premium publishers with trusted brands and human-created journalism.</p>



<ul class="wp-block-list">
<li>&#8220;We&#8217;re going to always have human-created content. First of all, I know it&#8217;s what our audiences expect and want. Secondly, we have no competitive advantage over just creating AI-generated content. That doesn&#8217;t leverage any of the advantages we have. And so knowing what your advantages are competitive, and really building upon that, I think is always important in any business.&#8221;</li>
</ul>



<p><strong>Why we care</strong>. Lynch said the old model of turning search and social traffic into profitable media businesses no longer works. Publishers without loyal readers or a strong brand may struggle because Google and other platforms can change the rules at any time.</p>



<p><strong>The interview.</strong> <a href="https://www.youtube.com/watch?v=0AuD76FK3u4">Condé Nast CEO Explains Why Human Journalism Wins in the AI Era</a> (search discussion starts around 30:28)</p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/0AuD76FK3u4?start=1828&#038;feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>


Condé Nast now plans its business “as if search is zero” after years of Google algorithm updates and AI Overviews reducing visits to publisher websites. That’s according to CEO Roger Lynch, who was interviewed on TBPN, the tech media network that bills itself as “technology’s daily show” and was acquired by OpenAI in April.
What he’s saying. Condé Nast doesn’t expect Google traffic to disappear completely, but no longer considers search a reliable channel, Lynch said:
“Last year, I told our teams: assume there’s no search. You have to have your businesses planned as if search is zero.”
“We don’t expect it to be zero… We expect it to be a single-digit percentage of our traffic. Very low.”
The context. Lynch described a multi-year pattern where Google consistently cut publisher visibility more than expected:
“Each of the last three years, we would do our budgets, and we’d put some forecasts in of search traffic declining… and then every year it was down more than we forecast.”
Why did Condé Nast’s search traffic decline? In addition to algorithm updates, he blamed AI Overviews and Google’s increasingly commercial search results.
Search results from seven or eight years ago showed a few sponsored links then “10 blue links.”
Today, users first see AI Overviews, then “rows and rows and rows of commerce links,” Lynch said.
Organic links appear much farther down the page.
“It’s been great for Google,” Lynch said. 
A different era. Google’s changes broke the old model that companies like BuzzFeed used to turn Google and Facebook traffic into money, Lynch said. 
“That era is gone.”
Lynch said brands caught “in the middle” are struggling most in the AI and search transition.
“Today, you need to be really nailing a specific niche where you have a loyal audience that’s willing to pay and and … If you have a brand where you’re investing in the journalism, if you have to make significant investments in journalism, supporting that just with advertising is a tough place to be.”
Condé Nast’s response. It has been to prioritize brands with:
Strong direct audiences.
Subscription potential.
Clear authority in a niche or category.
He also said that AI-generated “slop” could ultimately help premium publishers with trusted brands and human-created journalism.
“We’re going to always have human-created content. First of all, I know it’s what our audiences expect and want. Secondly, we have no competitive advantage over just creating AI-generated content. That doesn’t leverage any of the advantages we have. And so knowing what your advantages are competitive, and really building upon that, I think is always important in any business.”
Why we care. Lynch said the old model of turning search and social traffic into profitable media businesses no longer works. Publishers without loyal readers or a strong brand may struggle because Google and other platforms can change the rules at any time.
The interview. Condé Nast CEO Explains Why Human Journalism Wins in the AI Era (search discussion starts around 30:28)

Watch this video on YouTube

After years of Google algorithm updates and huge SERP changes, Condé Nast now plans its business as if search traffic barely exists.

Condé Nast expects search to become a single-digit of its traffic

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity.png" class="attachment-large size-large wp-post-image" alt="Google TurboQuant entity force" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>TurboQuant may help Google improve instant indexing, semantic search, and AI Overviews — changing how brands earn visibility.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity.png" class="attachment-large size-large wp-post-image" alt="Google TurboQuant entity force" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/google-turboquant-entity-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The release of <a href="https://searchengineland.com/google-turboquant-algorithm-vector-search-472977">TurboQuant</a> will completely change how we think about AI and SEO. This <a href="https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/" target="_blank" rel="noopener">new algorithm</a> from Google will greatly reduce computing power and energy by allowing for the massive compression of LLMs and vector search engines.</p>



<p>Using six times less memory and running eight times faster, all without losing accuracy, the high cost of running AI will be dramatically reduced.&nbsp;</p>



<p>As search moves away from a simple list of links on a SERP and turns into a system reliant on AI Overviews for immediate answers, the SEO industry needs to adapt and understand how to create true meaning and trust, and how they affect searching.</p>



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<h2 class="wp-block-heading" id="h-moving-toward-true-meaning-and-instant-intent-matching">Moving toward true meaning and instant intent matching</h2>



<p>Before the prevalence of AI, SEO relied on basic keywords and topics as stand-ins for what human users were searching for. Mapping out the true meaning across the web has been too expensive and consumed too much energy.&nbsp;</p>



<p>TurboQuant changes how information is processed. Using a high-quality compression method called PolarQuant, data is converted into coordinates that are much easier to manage. This allows Google to handle and process complex ideas more efficiently and for far less than before.&nbsp;</p>



<p>Search engines will be able to match the exact meaning of a search in real time, understanding a user&#8217;s goal based on their past searches and what’s happening in the world at that moment.</p>



<p>Additionally, TurboQuant&#8217;s near-zero indexing lead time will eliminate the delay between publishing and ranking. For trusted publishers, their expertise will be recognized as soon as they share information. It’ll also help prevent manipulation and spam content from surfacing.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/google-widen-seo-playing-field-476975">Google may be about to widen the SEO playing field</a></em></strong>



<h2 class="wp-block-heading" id="h-useless-content-will-end">Useless content will end</h2>



<p>TurboQuant will be the engine that boosts AI Overviews, which are quickly becoming the default answers for searches. Google used to limit these types of answers because of their huge cost on servers, but those limits are disappearing.&nbsp;</p>



<p>Marketers need to prepare for a world where AI summaries are the standard for most questions. Thin content — articles that summarize other sources without adding original value — will become obsolete. If an AI can generate a summary that serves as an overview by consolidating the entire web, users no longer need that type of content.</p>



<p>Original data, unique human viewpoints, and direct experience will matter the most, giving a brand trust and authority that a model can’t simulate.</p>



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<h2 class="wp-block-heading" id="h-building-trust-is-becoming-more-important">Building trust is becoming more important</h2>



<p>With an increased reliance on AI Overviews, your SEO strategy should focus on becoming a trusted entity that AI is confident recommending, rather than only ranking for keywords.&nbsp;</p>



<p>TurboQuant will help Google maintain a more reliable index of facts because the engine can instantly check claims against its own knowledge base in real time.&nbsp;</p>



<p>Trust is becoming more concrete and quantifiable, letting established, high-authority entities that people recognize rise to the top. Who says something is now just as important as what is said.&nbsp;</p>



<p>TurboQuant also lets Google track a brand’s strength across different domains and platforms. Improving your <a href="https://searchengineland.com/guide/knowledge-graph">knowledge graph</a> health will help ensure that the engine sees you as a trusted source.</p>



<h2 class="wp-block-heading" id="h-hyper-personalization-will-increase">Hyper-personalization will increase</h2>



<p>TurboQuant’s ability to handle huge amounts of information without delay will be the most challenging part of this change. Hyper-personalization will happen at a scale we’ve never witnessed.&nbsp;</p>



<p>An AI agent using this technology will be able to remember a user’s journey over months of interactions. Search engines will act as personal assistants, anticipating a user’s next search based on a deep understanding of their preferences.</p>



<p>The traditional process of buying a product may be dramatically shortened. A user might go from learning about a product to buying it entirely within Google’s interface.&nbsp;</p>



<p>TurboQuant lets Google combine multiple signals into one solid understanding of a brand&#8217;s value. Strategy will have to shift toward having a consistent presence everywhere — an omnichannel presence — where a brand is represented consistently across multiple platforms.&nbsp;</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/how-ai-models-understand-your-brand-475993">How AI models ‘understand’ your brand</a></em></strong>



<h2 class="wp-block-heading" id="h-start-building-entity-force">Start building ‘entity force’</h2>



<p>The SEO industry has been obsessed with the idea that more is better for too long. TurboQuant is the final nail in the coffin for the strategy of pumping out content quickly and at a high volume.&nbsp;</p>



<p>The ability to provide instant matching and indexing will allow the new technology to focus on finding high-quality information efficiently, and will also end indexing lag, letting valuable insights surface.</p>



<p>This efficiency demands a complete change in how we think as SEOs. We’ll need to stop making content just for the algorithm and start building &#8220;entity force&#8221; to become trusted entities.&nbsp;</p>



<p>Having a clear, trustworthy voice will determine how a message is distributed and a brand&#8217;s credibility. Google can now cut through the noise and better distinguish between a high-volume spam site and a focused entity than ever before.&nbsp;</p>

The release of TurboQuant will completely change how we think about AI and SEO. This new algorithm from Google will greatly reduce computing power and energy by allowing for the massive compression of LLMs and vector search engines.
Using six times less memory and running eight times faster, all without losing accuracy, the high cost of running AI will be dramatically reduced. 
As search moves away from a simple list of links on a SERP and turns into a system reliant on AI Overviews for immediate answers, the SEO industry needs to adapt and understand how to create true meaning and trust, and how they affect searching.
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Moving toward true meaning and instant intent matching
Before the prevalence of AI, SEO relied on basic keywords and topics as stand-ins for what human users were searching for. Mapping out the true meaning across the web has been too expensive and consumed too much energy. 
TurboQuant changes how information is processed. Using a high-quality compression method called PolarQuant, data is converted into coordinates that are much easier to manage. This allows Google to handle and process complex ideas more efficiently and for far less than before. 
Search engines will be able to match the exact meaning of a search in real time, understanding a user’s goal based on their past searches and what’s happening in the world at that moment.
Additionally, TurboQuant’s near-zero indexing lead time will eliminate the delay between publishing and ranking. For trusted publishers, their expertise will be recognized as soon as they share information. It’ll also help prevent manipulation and spam content from surfacing.
Dig deeper: Google may be about to widen the SEO playing field



Useless content will end
TurboQuant will be the engine that boosts AI Overviews, which are quickly becoming the default answers for searches. Google used to limit these types of answers because of their huge cost on servers, but those limits are disappearing. 
Marketers need to prepare for a world where AI summaries are the standard for most questions. Thin content — articles that summarize other sources without adding original value — will become obsolete. If an AI can generate a summary that serves as an overview by consolidating the entire web, users no longer need that type of content.
Original data, unique human viewpoints, and direct experience will matter the most, giving a brand trust and authority that a model can’t simulate.
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Building trust is becoming more important
With an increased reliance on AI Overviews, your SEO strategy should focus on becoming a trusted entity that AI is confident recommending, rather than only ranking for keywords. 
TurboQuant will help Google maintain a more reliable index of facts because the engine can instantly check claims against its own knowledge base in real time. 
Trust is becoming more concrete and quantifiable, letting established, high-authority entities that people recognize rise to the top. Who says something is now just as important as what is said. 
TurboQuant also lets Google track a brand’s strength across different domains and platforms. Improving your knowledge graph health will help ensure that the engine sees you as a trusted source.
Hyper-personalization will increase
TurboQuant’s ability to handle huge amounts of information without delay will be the most challenging part of this change. Hyper-personalization will happen at a scale we’ve never witnessed. 
An AI agent using this technology will be able to remember a user’s journey over months of interactions. Search engines will act as personal assistants, anticipating a user’s next search based on a deep understanding of their preferences.
The traditional process of buying a product may be dramatically shortened. A user might go from learning about a product to buying it entirely within Google’s interface. 
TurboQuant lets Google combine multiple signals into one solid understanding of a brand’s value. Strategy will have to shift toward having a consistent presence everywhere — an omnichannel presence — where a brand is represented consistently across multiple platforms. 
Dig deeper: How AI models ‘understand’ your brand



Start building ‘entity force’
The SEO industry has been obsessed with the idea that more is better for too long. TurboQuant is the final nail in the coffin for the strategy of pumping out content quickly and at a high volume. 
The ability to provide instant matching and indexing will allow the new technology to focus on finding high-quality information efficiently, and will also end indexing lag, letting valuable insights surface.
This efficiency demands a complete change in how we think as SEOs. We’ll need to stop making content just for the algorithm and start building “entity force” to become trusted entities. 
Having a clear, trustworthy voice will determine how a message is distributed and a brand’s credibility. Google can now cut through the noise and better distinguish between a high-volume spam site and a focused entity than ever before.

TurboQuant may help Google improve instant indexing, semantic search, and AI Overviews — changing how brands earn visibility.

Why TurboQuant could accelerate the shift to entity-driven SEO

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Why-your-brand-isnt-making-the-AI-recommendation-set.png" class="attachment-large size-large wp-post-image" alt="Why your brand isn’t making the AI recommendation set" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Why-your-brand-isnt-making-the-AI-recommendation-set.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-brand-isnt-making-the-AI-recommendation-set-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-brand-isnt-making-the-AI-recommendation-set-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-brand-isnt-making-the-AI-recommendation-set-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Many brands optimize for AI citations and extractability before establishing the clarity and relevance needed to enter the candidate set.


We’ve been flooded with generative engine optimization (GEO) advice over the last couple of years – from checklists for AI citations to signal frameworks and technical guides explaining how to structure content for large language models. 
Most GEO advice converges around the same idea: If you want to be visible in AI-generated answers, you need to be structured, authoritative, and easy to extract.
In my opinion, even though this information is extremely valuable and valid, it’s still incomplete if your brand is already positioning itself for a future where AI-generated answers dominate search. 
What this entire layer of advice assumes is that your brand is already eligible for consideration if it ticks those three boxes. But what most brands ignore is that they’re not even eligible to be considered in the first place.
The invisible layer most GEO advice skips
Traditional SEO has conditioned us to think of visibility as a function of ranking, where the objective is to position a page as high as possible for a given query, under the assumption that higher visibility leads to more clicks and, ultimately, better business outcomes. 
As AI-driven search experiences have evolved, many have adopted this thinking, replacing “ranking” with “being cited” or “being included in answers,” without questioning whether the underlying system still operates the same way.
AI systems do much more than ranking and summarizing information: They filter, reduce, and select entities based on four basic signals. 
Before any comparison of options takes place, the system first determines which entities are eligible for consideration. That layer is almost entirely missing from GEO discussions, and it’s where many brands risk exclusion.
The result is a false optimization sequence: brands invest in extractability before clarity and build credibility signals while their entity identity remains ambiguous. For instance, they write FAQ content for a stage they haven’t qualified for yet.
In practice, this creates two distinct thresholds. 
Qualification, where an entity becomes eligible to enter a candidate set.
Selection, where only a subset of those entities is actually included in the final answer. 

  
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From pages to entities: The measurement of competition has changed
While traditional SEO optimizes pages for ranking, AI systems select entities for inclusion. 
Entities are the named products, ideas, concepts, and brands that form the underpinning for Google’s Knowledge Graph, or the way its search understands the relationships between things. 
Once we accept that entities outweigh pages in AI’s final decision, we can see this is a structural shift, not an incremental one. It changes the unit — or “metric” — of competition.
A page can rank well in search results and still fail to represent a clearly defined, consistently understood entity. From a search engine’s perspective, the page meets the criteria for visibility. From an AI system’s perspective, the entity behind that page may still be ambiguous, weakly associated with a topic, or insufficiently confirmed across the web.
This is why it’s increasingly common to see companies that perform well in Google fail to appear in AI-generated answers for the same queries. 
Let’s look closer at qualification vs. selection and what each threshold requires.
Qualification: Can the system identify and associate you?
At the qualification stage, an AI system is effectively asking two questions: 
Can this entity be clearly identified?
Is this entity strongly associated with the topic?
If a brand is inconsistently defined — using different descriptions across platforms, appearing under slightly different name variants, or only loosely connected to a subject area — it will struggle to pass this first threshold. The system may “know” it exists in some form, but that knowledge is too ambiguous or poorly defined to include in a candidate set.
Clarity: Are you identified as a distinct entity?
Clarity means that any machine — be it a search engine or an LLM — can look at your name and clearly establish a relationship between you/your brand and the business/topic you are associated with. It’s actually an easy problem to fix, but one many brands overlook.
Let me use my own case as an example: I have a common name, shared by hundreds, if not thousands, of other women, most of whom have some online presence and some of whom are relevant in their fields. 
As an SEO and GEO consultant, this was an issue for my brand’s visibility. My issue was never a lack of presence online, but a lack of distinction. With so many people named Mariana Franco, both search engines and AI systems were repeatedly mixing signals from different individuals, making it difficult to consolidate a single, coherent entity.
I noticed, however, that the “Maryanna” spelling variant of my name was uncommon. Thus, changing my professional spelling from Mariana to Maryanna became an unavoidable disambiguation strategy so that my brand could be understood by search engines and LLMs. The change created a clearer, more distinctive identity that could be consistently recognized across systems. 
But apart from changing the spelling of my name, I also had to apply that spelling consistently across my website, profiles, and external references, so that all signals pointed to the same entity rather than competing variations.
The results became visible in seven days for search engines and 10 days for LLMs. The system no longer had to reconcile multiple similar identities, making it easier to associate the correct signals with a single person. Me!
In this case, the limiting factor was clarity. Not content volume, links, or a lack of activity, but the fact that the entity itself was too easy to confuse with others. Once that ambiguity was reduced and the signals became consistent, the system could process and reinforce the entity more effectively.
Relevance: Are you associated with your topic?
Relevance asks whether the system associates your brand with the topic being queried: not whether you have a page about it (typical ranking for keywords), but whether the broader web connects you to it consistently.
This comes from topic clustering — what entities and subjects is your brand mentioned alongside on the web —  content depth — does your brand demonstrate deep knowledge of your topic through specialized articles and web mentions, or are you scattering your content thinly across several sources — and context signals — whether your brand appears consistently alongside recognized names in your field that then transfer relevance to you.
Selection: Can the system confidently recommend you?
Once qualified, a brand enters the candidate set for search engines and LLMs. This is where the GEO advice most people are already following finally applies.
Credibility: Do other sources corroborate you?
Having a powerful About page is the first great asset that can help you to get your brand properly positioned, but how can Google or ChatGPT be certain that you are telling the truth? The answer: credibility.
Credibility asks whether sources beyond your own website confirm what you say about yourself. Any brand can write a compelling About page and make claims about itself, but AI systems need corroboration. They look for multiple independent sources that say consistent things about you.
This is where PR strategy, social media, and SEO converge to produce your brand’s AI visibility. Press coverage, podcast appearances, industry reports, award listings, and analyst mentions became corroboration signals that move you from the recognition set to the selection set.
I’ve found that podcast appearances seem particularly undervalued here. That’s because most podcasts are transcribed and published. That transcript becomes indexed content that mentions your name, your company, and your specialization in a context that signals expertise, independent of anything you published yourself.
Extractability: Can your content be used to generate an answer?
Extractability determines whether you get cited once you’re in the candidate set, or whether a competitor does instead. It basically asks: Can an AI system isolate a piece of your content and produce a confident, useful answer from it?
A lot of brand content is optimized for human engagement with long intros, buried answers, hedged claims, and dense paragraphs that rely on surrounding context. That type of content is hard for AI to contextualize, so AI will instead use non-branded content, which you have much less control over.
The fix for this problem is reformatting your branded content to be more AI-friendly: 
Put the answer first, not after a three-paragraph introduction.
Use proper heading hierarchy to make the structure easy and apparent.
Write short, self-contained paragraphs that make sense when lifted out of context. 
If a sentence could appear word-for-word in an AI response and still make sense, that is extractable. If it only makes sense within the full article, it won’t travel.
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Testing a query in Google and AI
When testing a query containing the word “best” such as “best ecommerce PPC agency UK,” we can clearly see the gap between search and AI-generated replies. In Google, the results typically include a mix of agencies, directories, and editorial content, meaning that a company like Lever Digital can rank high if it has strong landing pages and relevant supporting content. 
However, when testing the same query in an AI tool like Perplexity, the answer is much narrower and only a handful of agencies are mentioned, such as Impression, Genie Goals, or Brainlabs, while Lever Digital, despite its visibility in search, isn’t included.
Google typically distributes visibility across pages that match the query and intent. When the query or intent is ambiguous, Google will explore the topic with the user, showing different brands and types of pages that fulfill different intents. Google distributes visibility and has space for everyone as long as they are indexed and somehow match the search. 
LLMs, on the other hand, select entities that not only match the topic but also match the intent and are verified. 
An AI system will not evaluate the entire web and every page that appears in Google’s indexed pages. Their “thought process” starts with a smaller set of entities that have already passed a threshold of clarity and relevance, and only then applies additional signals before deciding what to include in the final answer. If an entity doesn’t make it into that initial group, it’s never part of the comparison at all. 
Recognition isn’t a recommendation. Our job is to close the gap.
There is a useful distinction that clarifies where most brands currently stand: 
Does AI simply know what your brand does? 
Or does it trust you enough to confidently suggest you on its answers?
AI systems can recognize far more entities than they are willing to recommend. If you ask a system directly about a specific brand, it may provide a reasonable description if it has some level of knowledge (whether this is through its learned data or live search). But when asked a broader question, such as “best ecommerce PPC agency UK,” that requires selecting a set of options, that same brand may not appear at all .
So, while recognition (clarity + relevance) gets you into the system, recommendation (credibility + extractibility) gets you into the answer.
It’s simple to test whether your brand is being recommended. Simply ask the AI, “What is [your brand]?” Then, follow up with, “What is the best [your category] for [your ideal customer]?”
If the first question returns a reasonable answer and the second doesn’t include your brand, you’re recognized but not recommended. The LLM can understand the relationship between your brand and what it does, but you haven’t passed the selection threshold.
The gap between these two states isn’t bridged by producing more content. This is where many brands make a critical mistake that unintentionally decreases their clarity and relevance. They try to tackle too many topics in an attempt to “rank for everything,” which ends up thinning their content. 
Instead of writing more content, brands should align how they are defined, referenced, and structured across the entire web so that when a system asks not just what exists, but what should be recommended, the answer is already clear.
The right optimization sequence from recognition to selection
Most GEO advice treats entity clarity as an afterthought, if it considers it at all. Often, one of the most important clarity resources is handled by the HR of the management team: the About page. And then it’s usually treated as if it’s just a glorified PR press release. When SEO does take it into consideration, it’s usually a low-priority task with little effort behind it. 
The typical sequence goes: fix technical foundations, restructure content for extractability, add schema, and build external mentions. This process just assumes that the system can already clearly identify your brand as a distinct entity. However, for many brands, that assumption is false, and no amount of FAQ schema or press coverage fixes it.
The problem is that selection tactics compound on top of a qualified entity. They do very little if the entity itself is ambiguous or inconsistently defined. The correct sequence is:
Clarity → Relevance → Credibility → Extractability
Clarity and relevance are qualification signals: They determine whether you enter the candidate set at all. If you fail here, you will be filtered out before any comparison happens.
Credibility and extractability are selection signals: They determine how likely you are to be chosen once you’re in the candidate set.
Fix qualification first. After that, every PR effort, schema, and FAQ you add compounds faster once the system can clearly identify and associate your entity. 

LLM responseQualificationSelectionPriority fix
“Never heard” FailN/AClarity, Relevance
“Describes you vaguely” Pass FailCredibility/Extractability
“Recommends you” Pass PassMaintain





The three questions to use to audit your brand visibility
Before investing further in selection tactics, you can run this test across ChatGPT, Perplexity, and Claude. Note, this test is useful for both personal and corporate brands:
“Who/What is [your brand]?” → This tests for brand clarity.
“What does [your brand] do?” → This tests for brand relevance.
“Best [your category] for [your ideal customer]?” → This tests for AI selection and extractibility.
If the first two questions return vague or hedged answers (typically including “possibly,” “might be,” “could refer to”), you have a qualification problem. In this case, start with fixing clarity and relevance before anything else.
If the first two return confident answers but the third doesn’t include you, your qualification is working, but your selection signals need strengthening, which means your brand needs to work on its credibility and extractability.
If all three return strong results, you understand what’s working. Protect it, and track it regularly. 
How to start getting into the selection pool
If you’re not appearing in AI recommendations for your category, the highest-leverage starting points are almost always the same: name consistency, definition, and your About page.
Step 1: Brand name consistency
Audit how your brand name appears across every platform you control: your website, LinkedIn, Google Business Profile, directories, and press mentions. Choose one canonical version and use it consistently everywhere, with both a short and long version. This may sound trivial, but name inconsistency is the most common clarity failure I encounter — and the easiest to fix.
Step 2: An About page that answers basic questions
Once you choose the canonical version of your name and description, write your About Page as a fact sheet. Answer these five questions in plain, structured language: Who you are, what you do, who you serve, where you’re based, and what makes you distinct. Make it the clearest, most machine-readable description of your entity that exists anywhere on the web. 
Tip: You can then run your About page text through a natural language processing (NLP) tool to get the best version possible.
Step 3: Make sure you add schema for proper structure
Add Organization schema with sameAs properties linking to your canonical profiles elsewhere. This formally introduces your entity to AI systems and reduces ambiguity across sources.
These three steps are the basis of clarity and the foundation for your brand qualification. Once this is done, everything else just builds up. 

  
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The future of AI visibility belongs to qualified entities
As AI systems improve, the gap between qualification and selection will likely grow. These systems are getting better at filtering noise, more conservative about what they include, and more dependent on consistent, corroborated signals when generating responses.
Producing content in bulk on your own website may have been — and may still be — important for topical authority, but it won’t succeed in this AI environment, especially without clarity.
Success in this environment will come first from aligning how a brand is understood across the web: clearly defined, consistently referenced, externally confirmed, and structured in a way LLMs can use.

Many brands optimize for AI citations and extractability before establishing the clarity and relevance needed to enter the candidate set.

Why your brand isn’t making the AI recommendation set

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Why-good-content-still-loses-in-Google-Search.png" class="attachment-large size-large wp-post-image" alt="Why good content still loses in Google Search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Why-good-content-still-loses-in-Google-Search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Why-good-content-still-loses-in-Google-Search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Why-good-content-still-loses-in-Google-Search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Why-good-content-still-loses-in-Google-Search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Strong content can still fail when positioning barriers limit visibility. Here’s how to diagnose the issues holding rankings back.

Create helpful, high-quality content, and you’ll rank. That’s been the prevailing wisdom in search for over a decade.
But “create great content” was always an incomplete recommendation. It treated one input in a multi-layer ranking system as the entire strategy. 
Content can be well researched, technically accurate, and aligned with search intent, and still struggle to rank.
The issue is usually positioning. When good content fails to rank, there’s often a barrier underneath it — technical limitations, authority gaps, weak entity recognition, or misaligned competition.
Until you identify which barrier is holding your content back, rewriting is usually wasted effort.
But first, make sure it’s actually good
Before blaming positioning, an honest assessment: Sometimes the content genuinely isn’t good enough.
I see this regularly. Teams publish thin, undifferentiated pages, often AI-generated without meaningful editorial input, and wonder why they don’t rank. 
Google’s helpful content guidance and E-E-A-T framework exist for a reason. Experience, expertise, authoritativeness, and trustworthiness are the baseline signals that separate credible content from noise.
Here’s the filter I use: Does your content offer original insight, match the format Google rewards for that query, and give the reader something they can’t find in the current top three results?
If the answer is “yes,” you likely have a positioning problem. Your content is good, but it still isn’t ranking.
However, if your content could have been written by anyone with access to the same Google search and an AI tool, you have a quality problem. Fix that first.

  
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The 2026 SERP: What your content is actually competing against
Even when your content is strong, the 2026 SERP has added structural competition that didn’t exist two years ago. Before diagnosing content-specific barriers, understand what your page is up against the moment it gets indexed.

SERP elementImpact on organic content
AI OverviewsAnswers the query before the user scrolls; reduces CTR across query types
Sponsored resultsIncreased ad density on Page 1, with subtler visual separation from organic results, pushes organic content further down
Reddit / UGC resultsGoogle increasingly surfaces user-generated discussion across product, troubleshooting, comparison, and opinion queries





The shift has been gradual but significant. AI Overviews now appear on several searches, answering queries before users ever reach organic results. 
Google also increased the volume of ads on Page 1, and the October 2025 ad label redesign made the visual distinction between paid and organic results subtler than before. 
Add Reddit threads and other user-generated content that Google now surfaces across a wide range of query types, and the traditional organic listing is competing for a shrinking share of the page.
Sponsored listings and AI Overviews now dominate the top half of the search results page



The practical implication: Ranking on Page 1 no longer guarantees the visibility or click-through rate it once did. 
Your content isn’t just competing against other articles. It’s competing against an entire page layout designed to answer the query before the user reaches you. That context matters for everything that follows.
Dig deeper: Why content that ranks can still fail AI retrieval



The diagnostic framework
When a client asks why their objectively good content isn’t ranking, this is the diagnostic sequence I use. Start at the top and work down.

Diagnostic factorWhat to checkThe red flag
1. Technical barriersIndexing, JavaScript rendering, canonicals, internal linksPage isn’t indexed or core content is hidden behind unrendered JavaScript
2. Intent mismatchSearch the keyword and compare SERP format to your pageSERPs show listicles and tools; you published a 3,000-word narrative essay
3. Authority gapDomain strength, trust signals, brand mentions, industry citationsTop results are established players with 2–3x your domain authority
4. Topical depth gapWhether you cover the broader topic or just an isolated articleCompetitors have 20+ clustered pages on this topic; you have one
5. User engagement signalsCheck whether top-ranking pages hold position because users find them adequate, not because they’re the best result availableAn established page ranks, users click it, the content is good enough that they don’t bounce, and that satisfaction loop keeps it ranked. Your better content never gets the clicks to prove itself.
6. Content differentiationDoes your page offer something the current top results don’t?Your content is well-written but says the same thing as the top five results
7. Brand/entity recognitionCheck whether Google recognizes your brand as a known entity in your space (branded searches, Knowledge Panel, mentions on authoritative sites)Nobody searches for your brand by name; Google has no entity-level trust signal to associate with your content





The order matters. Technical barriers come first because they’re the fastest to check and the most commonly overlooked. If your page isn’t indexed, none of the other six factors are relevant. 
Intent mismatch comes second because it’s the gate: Google evaluates format fit before it evaluates quality.
Here’s what working through the framework actually looks like:
Intent mismatch
Search your target keyword in an incognito window and compare what Google is showing against what you published. 
If the SERP is dominated by comparison tables and you wrote a narrative guide, your content quality is irrelevant. The format is wrong.
Authority gap
Check the domain ratings of the pages currently ranking. 
If every result on Page 1 has a domain rating two to three times higher than yours, you’re not losing on content. You’re losing on trust signals, and the fix is repositioning to keywords where you can realistically compete.
Engagement loop
Look at how long the current top results have held their positions. 
If the same pages have ranked for 12 or more months despite being mediocre, they’ve built a satisfaction feedback loop that new content can’t break without a different distribution strategy.
Website types and their most common barriers
Which barriers matter most depends on your site type. The framework is the same, but the entry point changes.
SaaS: Primary bottleneck is usually topical depth and authority. You need clustered content and trust signals to compete.
Ecommerce: Primary bottleneck is usually technical (crawl budget, faceted navigation, JavaScript rendering). Content quality rarely matters if Googlebot can’t process your pages.
Local business: Primary bottleneck is usually entity recognition and Google Business Profile signals. Your blog content won’t rank locally if Google doesn’t recognize your brand as a local entity.
If your content passes all seven checks and still isn’t ranking, you likely do have a content quality issue. But in my experience, we rarely get past the third check before finding the bottleneck.
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How this plays out in practice
The framework is more useful as a diagnostic sequence than as a checklist. To envision what that looks like in practice, here’s an example from when I worked with a B2B SaaS company selling contact center software. 
Their content was well-researched, technically accurate, and clearly written. It should have ranked. It didn’t. When I ran the website through the diagnostic framework, I was quickly able to identify the bottleneck.
Technical check: Pages were indexed and rendering was clean. I verified this in Google Search Console’s Page Indexing report within the first 15 minutes. Not the bottleneck.
Intent check: The content format matched the ranking content for their target keywords. They had guides where guides dominated the SERP, and comparison pages where comparisons ranked. Not the issue.
Authority check: This is where the diagnosis landed. Their domain had a modest backlink profile. Their competitors, all established players with domain ratings two to three times higher, had been accumulating trust signals for years. Every non-branded keyword in their space was dominated by these players. 
On top of that, paid ads and AI Overviews were pushing organic results further down the page, making it even harder for a smaller domain to gain visibility.
No amount of content improvement was going to close that authority gap on head terms.The problem was competitive positioning.
Instead of competing where they couldn’t win, we repositioned. We analyzed their product’s strongest differentiators, pulled data on the questions prospects were actually asking, and identified an underserved niche: small- and mid-size-business buyers. 
The larger competitors were serving both segments, but their content skewed heavily toward enterprise use cases, leaving the SMB angle underrepresented.
We built a topic cluster targeting that segment. We also built a pillar page connected to detailed subtopic pages via intentional internal linking, each addressing specific questions SMB buyers asked. 
This addressed multiple barriers simultaneously: 
The authority gap (competing at their weight class).
Topical depth (a cluster rather than isolated pages).
Content differentiation (an SMB-specific angle that nobody else covered).
The pillar page ranked within weeks and still holds a top position today. The content quality didn’t change but the competitive positioning did.
This is the point of the framework. The answer wasn’t “Make better content.” It was “Identify which barrier is actually holding this content back, then solve for that.” Most content teams skip the diagnosis and go straight to rewriting. That’s almost always wasted effort.
The framework becomes more than a one-time diagnostic. As those SMB-focused pages gain traction, they start earning backlinks and brand mentions from the mid-market community. The domain’s overall authority grows. Topics that were out of reach six months earlier become realistic targets. 
You run the framework again: Check intent alignment, reassess the authority gap on slightly broader terms, and find that the gap has narrowed enough to compete. The same sequence that identified the original bottleneck now guides the expansion. Win where you can, build from there, and reassess.
Most content teams try to skip straight to the competitive keywords and wonder why nothing moves. The framework gives you a diagnosis and a sequence.
Dig deeper: Content alone isn’t enough: Why SEO now requires distribution




  
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‘Good’ is the floor, not the ceiling
In 2026, AI-generated content has made “good enough” trivially easy to produce at scale. Any tool can synthesize a competent article on almost any topic. The bar for baseline quality has been obliterated. 
That’s exactly why positioning matters more than ever. When everyone’s content is “good,” the differentiator is rarely the writing itself. It’s the strategic advantages underneath it.
Think about the difference between “How to reduce SaaS churn,” which is a topic any AI tool can cover, and “We reduced churn by 34% in 6 months: Here’s the exact playbook.” 
The second article contains data and insight that only exists because someone did the work. That’s the kind of content that’s increasingly difficult for AI to replicate and increasingly rewarded across every discovery channel.
Experience is the most defensible differentiator in the current landscape. Content that shares original data, first-hand testing, or genuine practitioner insight has something that well-synthesized AI content never will: something that didn’t exist before the author created it.
Key takeaways
Stop diagnosing ranking failures as content quality problems: If your content passes the honest filter (original, intent-matched, differentiated), the bottleneck is one of seven positioning barriers. Identify which one before rewriting.
Check the technical foundation first: Just 15 minutes in Google Search Console can reveal whether your content is even indexed. This is the starting point of any ranking diagnosis.
Match your keyword targets to your actual authority level: Win at your weight class, build topical clusters where you can realistically compete, then expand into more competitive spaces.
Invest in experience and entity recognition: These are the signals that increasingly matter across traditional search, AI Overviews, and LLM citations simultaneously. Brand recognition isn’t separate from SEO. It’s a direct input into the ranking system.
The uncomfortable truth is that content quality has been commoditized. Your most strategic question is no longer “How do I create good content?”, but “Given that good content is the baseline, what else needs to be true for this content to rank?”
Answer that honestly, for your specific site, in your specific competitive landscape, and you’ll know exactly what to fix.

Strong content can still fail when positioning barriers limit visibility. Here’s how to diagnose the issues holding rankings back.

Why good content still loses in Google Search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data.png" class="attachment-large size-large wp-post-image" alt="How to eliminate the skepticism tax in marketing data" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Branded search can overstate marketing impact, revealing larger issues with attribution, AI outputs, and data confidence.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data.png" class="attachment-large size-large wp-post-image" alt="How to eliminate the skepticism tax in marketing data" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-eliminate-the-skepticism-tax-in-marketing-data-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Marketing teams often operate with a hidden skepticism tax. </p>



<p>Because they don’t fully trust their data, they spend enormous amounts of time cleaning spreadsheets, reconciling conflicting reports, and second-guessing both attribution models and AI outputs. </p>



<p>The result is slower execution, weaker alignment across teams, and decisions built on uncertain foundations.</p>



<p>Take branded search. It often gets credit for conversions that were likely to happen anyway, like a revolving door taking credit for everyone who enters a building. That gap between correlation and causation points to a much larger problem in modern marketing: too many teams operate on incomplete, fragmented, or low-confidence data.</p>



<p>The solution isn’t simply collecting more information. It’s building data foundations marketers can actually trust — verified identities, unified reporting, cleaner pipelines, and measurement frameworks designed to separate signal from noise.</p>



<p>Below is a breakdown of the core concepts behind those foundations and the types of data environments they create.</p>



<h2 class="wp-block-heading" id="h-probabilistic-vs-deterministic">Probabilistic vs. deterministic</h2>



<p>Let’s look at a simple example to illustrate probabilistic vs. deterministic: a coffee shop loyalty app. </p>



<p>When a customer logs in and orders, you know it&#8217;s Sarah — that&#8217;s deterministic. But when someone on the same Wi-Fi network browses your menu without logging in, you might guess it’s Sarah based on device and location signals — which is probabilistic. Both are useful, but you wouldn&#8217;t send a &#8220;Happy Birthday, Sarah!&#8221; push notification based on a guess.</p>



<p>It can be effective to show clients data-to-confidence mapping using the identity confidence thermometer:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1338" height="854" src="https://searchengineland.com/wp-content/seloads/2026/05/Identity-confidence-thermometer.png" alt="Identity confidence thermometer" class="wp-image-477277" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Identity-confidence-thermometer.png 1338w, https://searchengineland.com/wp-content/seloads/2026/05/Identity-confidence-thermometer-768x490.png 768w" sizes="(max-width: 1338px) 100vw, 1338px" /><figcaption class="wp-element-caption"><em>Identity confidence thermometer</em></figcaption></figure>
</div>


<p>Deterministic is at the top (100% confidence), while the confidence level grades down through probabilistic levels as you get to the bottom of the thermometer (IP match, device fingerprint, behavioral inference, etc.).</p>



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<h2 class="wp-block-heading" id="h-siloed-vs-holistic">Siloed vs. holistic</h2>



<p>Imagine three people describing the same elephant. Marketing touches the trunk and says, &#8220;It&#8217;s a hose.&#8221; Sales grabs the leg and says, &#8220;It&#8217;s a tree.&#8221; Finance feels the tail and says, &#8220;It&#8217;s a rope.&#8221; That&#8217;s what siloed data does to ROI reporting. A holistic data spine, by contrast, means everyone&#8217;s looking at the whole elephant.</p>



<p>Here’s a more concrete example: A B2B SaaS company is running LinkedIn ads. Marketing counts 5,000 form fills. Sales only sees 2,000 in the CRM because duplicates and junk leads have been filtered out. Finance counts 1,200 closed-won and attributes them to organic because UTMs broke. That’s three different teams, each with a different &#8220;truth&#8221; — zero confidence.</p>



<p>This illustration shows what this looks like in comparison:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1302" height="924" src="https://searchengineland.com/wp-content/seloads/2026/05/Siloed-vs.-holistic-—-the-three-truths-problem.png" alt="" class="wp-image-477279" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Siloed-vs.-holistic-—-the-three-truths-problem.png 1302w, https://searchengineland.com/wp-content/seloads/2026/05/Siloed-vs.-holistic-—-the-three-truths-problem-768x545.png 768w" sizes="(max-width: 1302px) 100vw, 1302px" /><figcaption class="wp-element-caption"><em><em>Siloed vs. holistic — the three-truths problem</em></em></figcaption></figure>
</div>


<p>On the left side, we have three disconnected boxes: Marketing, Sales, and Finance. Notice that each shows a different number for the same campaign. Conversely, the right side shows all three boxes feeding into a single &#8220;Identity spine&#8221; bar that outputs a single agreed-upon number.</p>



<h2 class="wp-block-heading" id="h-third-first-and-zero-party-data">Third, first, and zero-party data</h2>



<p>Consider the process of buying a house.&nbsp;</p>



<ul class="wp-block-list">
<li>Third-party data is a neighbor who says, &#8220;I think they&#8217;re looking to move&#8221; — it&#8217;s just gossip.&nbsp;</li>



<li>First-party data is the realtor who sees them attend three open houses — it&#8217;s observed behavior.&nbsp;</li>



<li>Zero-party data is the buyer filling out a form and saying, &#8220;I want a three-bedroom house in Oakland for under $900,000&#8221; — it’s stated intent.&nbsp;</li>
</ul>



<p>As cookies disappear, marketers are essentially moving from widely available gossip to less frequent but far more valuable direct conversation.</p>



<p>In the three-layer pyramid or funnel below:&nbsp;</p>



<ul class="wp-block-list">
<li><strong>Bottom layer (widest, lowest trust):</strong> Third-party / inferred data.</li>



<li><strong>Middle layer:</strong> First-party / observed data.</li>



<li><strong>Top layer (narrowest, highest trust):</strong> Zero-party / declared data.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1346" height="916" src="https://searchengineland.com/wp-content/seloads/2026/05/Data-trust-pyramid-third-first-and-zero-party.png" alt="Data trust pyramid: third, first, and zero-party" class="wp-image-477280" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Data-trust-pyramid-third-first-and-zero-party.png 1346w, https://searchengineland.com/wp-content/seloads/2026/05/Data-trust-pyramid-third-first-and-zero-party-768x523.png 768w" sizes="(max-width: 1346px) 100vw, 1346px" /><figcaption class="wp-element-caption"><em>Data trust pyramid: third, first, and zero-party</em></figcaption></figure>
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<h2 class="wp-block-heading" id="h-big-data-vs-correct-data">Big data vs. correct data</h2>



<p>The analogy I like to use here is a kitchen where you never throw anything out. The fridge is packed, but half of what’s in it has expired. You often spend 20 minutes digging for the one ingredient you need, and occasionally you cook with something that&#8217;s gone bad. </p>



<p>This mess of a kitchen represents “big data.” Lots of information is easily accessible, but it’s nearly impossible to make sense of or have confidence in its accuracy. </p>



<p>&#8220;Correct data,&#8221; by comparison, is a curated pantry: Fewer items, all fresh, all labeled, and everything within reach is usable.</p>



<p>Here’s a direct example for all of us marketers: Feeding an AI model 500,000 rows of CRM data sounds impressive until you realize 30% are duplicate contacts, 15% have outdated emails, and the revenue field uses three different currency formats. The worst part is that the model doesn&#8217;t get smarter — it confidently sends you in the wrong direction (or spinning in circles).</p>



<p>Here&#8217;s a side-by-side comparison of two data pipelines. </p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1294" height="910" src="https://searchengineland.com/wp-content/seloads/2026/05/Big-data-vs.-correct-data-pipeline.png" alt="Big data vs. correct data pipeline" class="wp-image-477281" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Big-data-vs.-correct-data-pipeline.png 1294w, https://searchengineland.com/wp-content/seloads/2026/05/Big-data-vs.-correct-data-pipeline-768x540.png 768w" sizes="(max-width: 1294px) 100vw, 1294px" /><figcaption class="wp-element-caption"><em>Big data vs. correct data pipeline</em></figcaption></figure>
</div>


<p>The left is a firehose dumping raw data into a &#8220;swamp&#8221; (messy, murky, and opaque). On the right is the same firehose passing through a filter (validation, deduplication, formatting) into a clean reservoir. This filter is the &#8220;confidence layer.&#8221;</p>



<h2 class="wp-block-heading" id="h-correlation-vs-causation">Correlation vs. causation</h2>



<p>You’ve probably heard this juxtaposition a lot, both inside and outside the marketing context. In marketing, the classic example is that branded search always looks like the best-performing channel because people Google your name right before they buy. That&#8217;s like giving the revolving door credit for everyone who enters the building.&nbsp;</p>



<p>Correlation says, &#8220;People who walked through the door became customers.&#8221; Causation asks, &#8220;Would they have come in regardless of the door?&#8221;</p>



<p>Incrementality testing is the fix. </p>



<p>At a high level, you hold out a group from seeing your ads and compare their conversion rate to the exposed group, which should be similar in size and composition (e.g., similar geos). If the holdout group converts at nearly the same rate as the exposed group, your ads were just taking credit, not creating demand.</p>



<p>Here&#8217;s an example of a classic misleading view (branded search with sky-high ROAS) next to the incrementality-adjusted view (branded search deflated and prospecting channels elevated). </p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1414" height="750" src="https://searchengineland.com/wp-content/seloads/2026/05/Correlation-vs-causation-ROAS-.png" alt="Correlation vs. causation ROAS " class="wp-image-477285" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Correlation-vs-causation-ROAS-.png 1414w, https://searchengineland.com/wp-content/seloads/2026/05/Correlation-vs-causation-ROAS--768x407.png 768w" sizes="(max-width: 1414px) 100vw, 1414px" /><figcaption class="wp-element-caption"><em>Correlation vs. causation ROAS</em></figcaption></figure>
</div>


<p>Essentially, this is a side-by-side comparison of what your dashboard says vs. what actually worked.</p>



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<h2 class="wp-block-heading" id="h-building-a-stronger-marketing-confidence-layer">Building a stronger marketing confidence layer</h2>



<p>Those are the main data foundations used to build confidence across teams:</p>



<ul class="wp-block-list">
<li><strong>Identity confidence thermometer:</strong> From probabilistic (low confidence) to deterministic (high confidence).</li>



<li><strong>Siloed vs. holistic:</strong> From siloed data (low confidence) to holistic (high confidence).&nbsp;</li>



<li><strong>Data trust pyramid:</strong> From third-party data (low confidence) to first- and potentially zero-party data (high confidence).&nbsp;</li>



<li><strong>Big data vs. correct data pipeline:</strong> A swamp producing &#8220;confidently wrong&#8221; AI outputs (low confidence) versus an added filter producing reliable outputs (high confidence).</li>



<li><strong>Correlation vs. causation ROAS</strong>: From identifying relationships (low confidence) to establishing cause using a scientific framework (high confidence).</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1386" height="632" src="https://searchengineland.com/wp-content/seloads/2026/05/Correlation-vs.-causation-ROAS.png" alt="The confidence layer maturity spectrum" class="wp-image-477282" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Correlation-vs.-causation-ROAS.png 1386w, https://searchengineland.com/wp-content/seloads/2026/05/Correlation-vs.-causation-ROAS-768x350.png 768w" sizes="(max-width: 1386px) 100vw, 1386px" /><figcaption class="wp-element-caption"><em><em>The confidence layer maturity spectrum</em></em></figcaption></figure>
</div>


<p>AI can handle countless tasks. But strong decision-making still depends on experienced marketers with good judgment. These data foundations help you move closer to that.</p>

Marketing teams often operate with a hidden skepticism tax. 
Because they don’t fully trust their data, they spend enormous amounts of time cleaning spreadsheets, reconciling conflicting reports, and second-guessing both attribution models and AI outputs. 
The result is slower execution, weaker alignment across teams, and decisions built on uncertain foundations.
Take branded search. It often gets credit for conversions that were likely to happen anyway, like a revolving door taking credit for everyone who enters a building. That gap between correlation and causation points to a much larger problem in modern marketing: too many teams operate on incomplete, fragmented, or low-confidence data.
The solution isn’t simply collecting more information. It’s building data foundations marketers can actually trust — verified identities, unified reporting, cleaner pipelines, and measurement frameworks designed to separate signal from noise.
Below is a breakdown of the core concepts behind those foundations and the types of data environments they create.
Probabilistic vs. deterministic
Let’s look at a simple example to illustrate probabilistic vs. deterministic: a coffee shop loyalty app. 
When a customer logs in and orders, you know it’s Sarah — that’s deterministic. But when someone on the same Wi-Fi network browses your menu without logging in, you might guess it’s Sarah based on device and location signals — which is probabilistic. Both are useful, but you wouldn’t send a “Happy Birthday, Sarah!” push notification based on a guess.
It can be effective to show clients data-to-confidence mapping using the identity confidence thermometer:
Identity confidence thermometer



Deterministic is at the top (100% confidence), while the confidence level grades down through probabilistic levels as you get to the bottom of the thermometer (IP match, device fingerprint, behavioral inference, etc.).

  
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Siloed vs. holistic
Imagine three people describing the same elephant. Marketing touches the trunk and says, “It’s a hose.” Sales grabs the leg and says, “It’s a tree.” Finance feels the tail and says, “It’s a rope.” That’s what siloed data does to ROI reporting. A holistic data spine, by contrast, means everyone’s looking at the whole elephant.
Here’s a more concrete example: A B2B SaaS company is running LinkedIn ads. Marketing counts 5,000 form fills. Sales only sees 2,000 in the CRM because duplicates and junk leads have been filtered out. Finance counts 1,200 closed-won and attributes them to organic because UTMs broke. That’s three different teams, each with a different “truth” — zero confidence.
This illustration shows what this looks like in comparison:
Siloed vs. holistic — the three-truths problem



On the left side, we have three disconnected boxes: Marketing, Sales, and Finance. Notice that each shows a different number for the same campaign. Conversely, the right side shows all three boxes feeding into a single “Identity spine” bar that outputs a single agreed-upon number.
Third, first, and zero-party data
Consider the process of buying a house. 
Third-party data is a neighbor who says, “I think they’re looking to move” — it’s just gossip. 
First-party data is the realtor who sees them attend three open houses — it’s observed behavior. 
Zero-party data is the buyer filling out a form and saying, “I want a three-bedroom house in Oakland for under $900,000” — it’s stated intent. 
As cookies disappear, marketers are essentially moving from widely available gossip to less frequent but far more valuable direct conversation.
In the three-layer pyramid or funnel below: 
Bottom layer (widest, lowest trust): Third-party / inferred data.
Middle layer: First-party / observed data.
Top layer (narrowest, highest trust): Zero-party / declared data.
Data trust pyramid: third, first, and zero-party




	
	
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Big data vs. correct data
The analogy I like to use here is a kitchen where you never throw anything out. The fridge is packed, but half of what’s in it has expired. You often spend 20 minutes digging for the one ingredient you need, and occasionally you cook with something that’s gone bad. 
This mess of a kitchen represents “big data.” Lots of information is easily accessible, but it’s nearly impossible to make sense of or have confidence in its accuracy. 
“Correct data,” by comparison, is a curated pantry: Fewer items, all fresh, all labeled, and everything within reach is usable.
Here’s a direct example for all of us marketers: Feeding an AI model 500,000 rows of CRM data sounds impressive until you realize 30% are duplicate contacts, 15% have outdated emails, and the revenue field uses three different currency formats. The worst part is that the model doesn’t get smarter — it confidently sends you in the wrong direction (or spinning in circles).
Here’s a side-by-side comparison of two data pipelines. 
Big data vs. correct data pipeline



The left is a firehose dumping raw data into a “swamp” (messy, murky, and opaque). On the right is the same firehose passing through a filter (validation, deduplication, formatting) into a clean reservoir. This filter is the “confidence layer.”
Correlation vs. causation
You’ve probably heard this juxtaposition a lot, both inside and outside the marketing context. In marketing, the classic example is that branded search always looks like the best-performing channel because people Google your name right before they buy. That’s like giving the revolving door credit for everyone who enters the building. 
Correlation says, “People who walked through the door became customers.” Causation asks, “Would they have come in regardless of the door?”
Incrementality testing is the fix. 
At a high level, you hold out a group from seeing your ads and compare their conversion rate to the exposed group, which should be similar in size and composition (e.g., similar geos). If the holdout group converts at nearly the same rate as the exposed group, your ads were just taking credit, not creating demand.
Here’s an example of a classic misleading view (branded search with sky-high ROAS) next to the incrementality-adjusted view (branded search deflated and prospecting channels elevated). 
Correlation vs. causation ROAS



Essentially, this is a side-by-side comparison of what your dashboard says vs. what actually worked.

  
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Building a stronger marketing confidence layer
Those are the main data foundations used to build confidence across teams:
Identity confidence thermometer: From probabilistic (low confidence) to deterministic (high confidence).
Siloed vs. holistic: From siloed data (low confidence) to holistic (high confidence). 
Data trust pyramid: From third-party data (low confidence) to first- and potentially zero-party data (high confidence). 
Big data vs. correct data pipeline: A swamp producing “confidently wrong” AI outputs (low confidence) versus an added filter producing reliable outputs (high confidence).
Correlation vs. causation ROAS: From identifying relationships (low confidence) to establishing cause using a scientific framework (high confidence).
The confidence layer maturity spectrum



AI can handle countless tasks. But strong decision-making still depends on experienced marketers with good judgment. These data foundations help you move closer to that.

Branded search can overstate marketing impact, revealing larger issues with attribution, AI outputs, and data confidence.

How to eliminate the skepticism tax in marketing data

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search can leave high-ranking brands out of buyer answers. Link builders need to optimize the sources that AI systems retrieve and cite.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513.jpg" alt="
" class="wp-image-477218" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Citation-Labs-20260513-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p>Link building has to evolve.</p>



<p>For years, SEOs measured visibility through keywords, rankings, links, and click-through traffic.</p>



<p>Those things still matter.&nbsp;</p>



<p>But the return signal has weakened, especially at the top of the funnel.</p>



<p>The bigger shift is how your prospective customers solve problems. Buyers* no longer have to compress a question, constraint, fear, or doubt into a keyword. They can ask AI systems in natural language, add context, and explain what they need in order to make the best decision for their situation.</p>



<p>If teams sleep on that shift, they’re going to wake up with visibility nightmares they can’t explain with old SEO metrics.</p>



<p>That changes the job for link builders.</p>



<p>The goal was never <strong>just</strong> more links. Link builders needed to earn visibility on converting pages.&nbsp;</p>



<p>Now, we have to move closer to the decision: what information a buyer needs, whether that information exists, and which sources AI systems can retrieve, trust, and use.</p>



<p>Link building has to evolve into citation optimization.</p>



<p><em>*Buyer is shorthand for the practitioners, stakeholders, and decision participants trying to solve a problem your offering can help address.</em>



<h2 class="wp-block-heading" id="h-ai-search-changes-what-seo-visibility-means">AI search changes what SEO visibility means</h2>



<p>Still hyper-focused on top-of-funnel visibility?</p>



<p>That era isn’t gone, but it doesn’t create the same impact.</p>



<p>Ranking for broad, buyer-relevant topics can still help. So can visibility in the related searches and sources AI systems pull from when a decision-stage prompt needs fresh information.&nbsp;</p>



<p>SEO fundamentals still matter: useful content, trusted references, authority, source consistency, clarity, and strong links.</p>



<p>But the old chain (earn the link, support the ranking, get the click, prove the impact) has weakened.</p>



<p>SEO and link building built an entire operating model around keywords because keywords were the unit of measurement available to us.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="684" height="310" src="https://searchengineland.com/wp-content/seloads/2026/05/image-130.png" alt="" class="wp-image-477219"/></figure>
</div>


<p>But keywords were always a compressed version of the real problem.</p>



<p>A person had a question, a constraint, a fear, a decision to make, or a job to get done. To use search, they had to translate that into a keyword.</p>



<p>AI changes that behavior. People can <a href="https://citationlabs.com/google-ai-mode-buyer-shortlisting-study-highlights/" target="_blank" rel="noopener sponsored nofollow">ask in natural language</a>, add context from prior interactions, and explain what they’re trying to solve, what they already know, and where they’re stuck.</p>



<p>That sounds simple, but it creates a deeper mindset shift for SEO teams. The work has to shift from ranking for the keyword to helping the person solve the underlying problem.</p>



<p><a href="https://citationlabs.com/llm-mentions/" target="_blank" rel="noopener sponsored nofollow">That is the basis for citation optimization</a>: helping AI systems find useful source material for the decision, instead of treating another link as the whole job.</p>



<h2 class="wp-block-heading" id="h-ai-surfaces-the-questions-your-buyers-used-to-ask-sales">AI surfaces the questions your buyers used to ask sales</h2>



<p><a href="https://citationlabs.com/case-studies/b2b-enterprise-improves-AI-visibility-within-7-days/" target="_blank" rel="noopener sponsored nofollow">We’ve seen this with successful enterprise brands</a> that have massive search visibility yet fail to appear in key answers when buyers use AI tools to evaluate solutions.&nbsp;</p>



<p>The business ranks for loads of keywords and gets millions of site visitors.</p>



<p>Then someone within the organization asked a specific question tied to a buyer&#8217;s pain point and service, and the brand didn’t rank among the answers.&nbsp;</p>



<p>Competitors did.</p>



<p>Google’s AI Mode didn’t surface them because it lacked sufficient context to confidently identify, cite, include, and recommend their brand as a leading solution for those specific buyer questions.</p>



<p>These aren’t keyword-based questions. They’re buyer-side questions that used to surface only during sales calls: clarification, fit, use case, proof, and implementation questions that buyers ask once they&#8217;re deep into consideration and due diligence.</p>



<p>Traditionally, that information lived in sales reps’ heads and a few internal sales enablement assets. They’d use context during calls to figure out the buyer&#8217;s specific needs and match them to the service.</p>



<p>Buyers do that research now when shortlisting options (<a href="https://citationlabs.com/funnels-new-gatekeeper-google-search-ai-mode-shortlisting-behavioral-study/">our recent behavioral study confirme</a><a href="https://citationlabs.com/funnels-new-gatekeeper-google-search-ai-mode-shortlisting-behavioral-study/" target="_blank" rel="noopener sponsored nofollow">d</a><a href="https://citationlabs.com/funnels-new-gatekeeper-google-search-ai-mode-shortlisting-behavioral-study/"> that buyer behavior has shifted with AI mode and AIOs</a>).</p>



<p>The link builder’s job (yes, it’s up to us&#8230; we’re still frontline with publishers) is now to pull that information out of the organization and use it in places that AI tools review for answers.&nbsp;</p>



<p>Not just backlinks.&nbsp;</p>



<p>This means link builders need access to key sales and implementation diagnostics insight.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1346" src="https://searchengineland.com/wp-content/seloads/2026/05/image-2.jpeg" alt="" class="wp-image-477221" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-2.jpeg 2048w, https://searchengineland.com/wp-content/seloads/2026/05/image-2-768x505.jpeg 768w, https://searchengineland.com/wp-content/seloads/2026/05/image-2-1536x1010.jpeg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Once those questions surface, keyword coverage alone won’t do it.</p>



<p>It can show demand, but it won’t show what a buyer needs to understand before they trust a recommendation. And it won’t cover the questions buyers don’t know to ask (<a href="https://citationlabs.com/fluqs-answer-hidden-questions-ai-search/" target="_blank" rel="noopener sponsored nofollow">which we call FLUQs</a>).</p>



<p>That missing decision-level information is what AI systems need to find before they include, compare, or cite the brand.</p>



<h2 class="wp-block-heading" id="h-citations-start-before-the-answer">Citations start before the answer</h2>



<p>If keyword coverage misses the buyer’s decision questions, where do AI systems get the material to answer them?</p>



<p><a href="https://xofu.com/bofu" target="_blank" rel="noopener sponsored nofollow">Tracking BOFU prompts</a> helps us inspect that surface.&nbsp;</p>



<p>It won’t show the exact prompts buyers type. No one gets that data. And recent research suggests synthetic prompts can still give a useful signal when they model real buyer intent, <a href="https://citationlabs.com/measuring-ai-visibility-with-visibility-percent-and-recommendation-rank/" target="_blank" rel="noopener sponsored nofollow">but we shouldn’t treat one run &#8211; or 100 &#8211; as the truth</a>.</p>



<p>You start by asking, “When we ask a prompt that represents a buyer problem, what sources does the system reach for?”</p>



<p>That’s where the link-building work changes.&nbsp;</p>



<p>You need to look at the cited pages in those answers and ask whether they give the system enough detail to answer without guessing:</p>



<ul class="wp-block-list">
<li>Do they explain the offer?&nbsp;</li>



<li>Do they compare options?&nbsp;</li>



<li>Do they show the use case?&nbsp;</li>



<li>Do they include the proof?</li>
</ul>



<p>The source mix changes by prompt, industry, and intent.&nbsp;</p>



<p>At the bottom of the funnel we frequently see AI tools cite LinkedIn, YouTube, <a href="https://citationlabs.com/measuring-3rd-party-influence-on-ai-answers/" target="_blank" rel="noopener sponsored nofollow">third-party comparison pages</a>, <a href="https://citationlabs.com/using-off-domain-comparison-assets-to-influence-llm-visibility/" target="_blank" rel="noopener sponsored nofollow">microsites</a>, and TONS of content from competitors or in-market vendors in answers to buyer prompts. In some segments we’re seeing government documentation and guidance</p>



<p>In-market vendors often make up the biggest citation bucket.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1542" src="https://searchengineland.com/wp-content/seloads/2026/05/image-3.jpeg" alt="" class="wp-image-477222" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-3.jpeg 2048w, https://searchengineland.com/wp-content/seloads/2026/05/image-3-768x578.jpeg 768w, https://searchengineland.com/wp-content/seloads/2026/05/image-3-1536x1157.jpeg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>AI systems use what they can retrieve and apply quickly, with minimal compute (much like humans). A page with the table, comparison, framework, or structure already built gives the system something to use.</p>



<p>Your job is to earn links <strong>and</strong> improve the material AI systems may reference <strong>before</strong> they decide which brands belong in the answer. Citation optimization starts before the answer.</p>



<p><strong><em>Important note:</em></strong><em> Don’t over read a single prompt run. </em><a href="https://xofu.com/" target="_blank" rel="noopener nofollow"><em>Track prompts multiple times</em></a><em> to look for repeated gaps. If a brand disappears from a valuable prompt category, that absence gives you a place to investigate.</em>



<h2 class="wp-block-heading" id="h-citation-optimization-the-future-state-of-link-building">Citation optimization: The future state of link building</h2>



<p>Citation optimization means identifying the pages and websites that influence AI answers, then improving how they mention your offering. Hence, the brand appears more consistently, more accurately, and in a better context.</p>



<p>A simple way to operationalize this approach is to <a href="https://citationlabs.com/parse-custom-gpt-building-content-for-buyer-reality/" target="_blank" rel="noopener sponsored nofollow">remember PARSE</a>:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1944" height="1110" src="https://searchengineland.com/wp-content/seloads/2026/05/image-1.jpeg" alt="" class="wp-image-477220" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-1.jpeg 1944w, https://searchengineland.com/wp-content/seloads/2026/05/image-1-768x439.jpeg 768w, https://searchengineland.com/wp-content/seloads/2026/05/image-1-1536x877.jpeg 1536w" sizes="(max-width: 1944px) 100vw, 1944px" /></figure>
</div>


<p>For SEOs and link builders, the starting point is prompt-led source research:</p>



<ol class="wp-block-list">
<li>Track the unbranded prompts that matter to the buyer’s problem.&nbsp;</li>



<li>Run them more than once.&nbsp;</li>



<li>Look at which pages and domains the system cites repeatedly.</li>



<li>Inspect those pages.</li>
</ol>



<p><strong>You should ask</strong>:</p>



<ul class="wp-block-list">
<li>Which sources shape the answer?&nbsp;</li>



<li>Which ones compare options?&nbsp;</li>



<li>Which ones have a table, list, framework, or explanation the system can use?&nbsp;</li>



<li>Which ones mention competitors but leave you out?&nbsp;</li>



<li>Which ones mention you without enough context to explain why you belong?</li>
</ul>



<p>This approach gives link builders a different kind of target list. Your goal isn’t only to secure another backlink. It’s to improve the source material that AI systems may use before they decide which brands belong in the answer.</p>



<p>That can mean adding the brand to a cited page, improving an existing mention, replacing a thin comparison with a clearer one, contributing a table, graphic, short explanation, or other asset chunk that gives the page more useful material to work with.</p>



<p>This still includes links.&nbsp;</p>



<p>We’re not leaving the link behind.</p>



<p>But a brand mention and anchor text alone is too thin.&nbsp;</p>



<p><strong>You need anchor context: </strong>useful material around the link that helps a system understand the value of the mention.</p>



<p>Whether you’re in-house or working with link builders, you need to ask for more than a backlink.&nbsp;</p>



<p>Ask for a backlink plus anchor context: a useful piece of context that can help form an AI citation. At a minimum, that chunk should explain the offer, the use case, who it helps, and why it belongs in the answer.</p>



<p>That’s the first shift from link building to citation optimization and increased search AND AI visibility.</p>






Link building has to evolve.
For years, SEOs measured visibility through keywords, rankings, links, and click-through traffic.
Those things still matter. 
But the return signal has weakened, especially at the top of the funnel.
The bigger shift is how your prospective customers solve problems. Buyers* no longer have to compress a question, constraint, fear, or doubt into a keyword. They can ask AI systems in natural language, add context, and explain what they need in order to make the best decision for their situation.
If teams sleep on that shift, they’re going to wake up with visibility nightmares they can’t explain with old SEO metrics.
That changes the job for link builders.
The goal was never just more links. Link builders needed to earn visibility on converting pages. 
Now, we have to move closer to the decision: what information a buyer needs, whether that information exists, and which sources AI systems can retrieve, trust, and use.
Link building has to evolve into citation optimization.
*Buyer is shorthand for the practitioners, stakeholders, and decision participants trying to solve a problem your offering can help address.



AI search changes what SEO visibility means
Still hyper-focused on top-of-funnel visibility?
That era isn’t gone, but it doesn’t create the same impact.
Ranking for broad, buyer-relevant topics can still help. So can visibility in the related searches and sources AI systems pull from when a decision-stage prompt needs fresh information. 
SEO fundamentals still matter: useful content, trusted references, authority, source consistency, clarity, and strong links.
But the old chain (earn the link, support the ranking, get the click, prove the impact) has weakened.
SEO and link building built an entire operating model around keywords because keywords were the unit of measurement available to us.




But keywords were always a compressed version of the real problem.
A person had a question, a constraint, a fear, a decision to make, or a job to get done. To use search, they had to translate that into a keyword.
AI changes that behavior. People can ask in natural language, add context from prior interactions, and explain what they’re trying to solve, what they already know, and where they’re stuck.
That sounds simple, but it creates a deeper mindset shift for SEO teams. The work has to shift from ranking for the keyword to helping the person solve the underlying problem.
That is the basis for citation optimization: helping AI systems find useful source material for the decision, instead of treating another link as the whole job.
AI surfaces the questions your buyers used to ask sales
We’ve seen this with successful enterprise brands that have massive search visibility yet fail to appear in key answers when buyers use AI tools to evaluate solutions. 
The business ranks for loads of keywords and gets millions of site visitors.
Then someone within the organization asked a specific question tied to a buyer’s pain point and service, and the brand didn’t rank among the answers. 
Competitors did.
Google’s AI Mode didn’t surface them because it lacked sufficient context to confidently identify, cite, include, and recommend their brand as a leading solution for those specific buyer questions.
These aren’t keyword-based questions. They’re buyer-side questions that used to surface only during sales calls: clarification, fit, use case, proof, and implementation questions that buyers ask once they’re deep into consideration and due diligence.
Traditionally, that information lived in sales reps’ heads and a few internal sales enablement assets. They’d use context during calls to figure out the buyer’s specific needs and match them to the service.
Buyers do that research now when shortlisting options (our recent behavioral study confirmed that buyer behavior has shifted with AI mode and AIOs).
The link builder’s job (yes, it’s up to us… we’re still frontline with publishers) is now to pull that information out of the organization and use it in places that AI tools review for answers. 
Not just backlinks. 
This means link builders need access to key sales and implementation diagnostics insight.




Once those questions surface, keyword coverage alone won’t do it.
It can show demand, but it won’t show what a buyer needs to understand before they trust a recommendation. And it won’t cover the questions buyers don’t know to ask (which we call FLUQs).
That missing decision-level information is what AI systems need to find before they include, compare, or cite the brand.
Citations start before the answer
If keyword coverage misses the buyer’s decision questions, where do AI systems get the material to answer them?
Tracking BOFU prompts helps us inspect that surface. 
It won’t show the exact prompts buyers type. No one gets that data. And recent research suggests synthetic prompts can still give a useful signal when they model real buyer intent, but we shouldn’t treat one run – or 100 – as the truth.
You start by asking, “When we ask a prompt that represents a buyer problem, what sources does the system reach for?”
That’s where the link-building work changes. 
You need to look at the cited pages in those answers and ask whether they give the system enough detail to answer without guessing:
Do they explain the offer? 
Do they compare options? 
Do they show the use case? 
Do they include the proof?
The source mix changes by prompt, industry, and intent. 
At the bottom of the funnel we frequently see AI tools cite LinkedIn, YouTube, third-party comparison pages, microsites, and TONS of content from competitors or in-market vendors in answers to buyer prompts. In some segments we’re seeing government documentation and guidance
In-market vendors often make up the biggest citation bucket.




AI systems use what they can retrieve and apply quickly, with minimal compute (much like humans). A page with the table, comparison, framework, or structure already built gives the system something to use.
Your job is to earn links and improve the material AI systems may reference before they decide which brands belong in the answer. Citation optimization starts before the answer.
Important note: Don’t over read a single prompt run. Track prompts multiple times to look for repeated gaps. If a brand disappears from a valuable prompt category, that absence gives you a place to investigate.



Citation optimization: The future state of link building
Citation optimization means identifying the pages and websites that influence AI answers, then improving how they mention your offering. Hence, the brand appears more consistently, more accurately, and in a better context.
A simple way to operationalize this approach is to remember PARSE:




For SEOs and link builders, the starting point is prompt-led source research:
Track the unbranded prompts that matter to the buyer’s problem. 
Run them more than once. 
Look at which pages and domains the system cites repeatedly.
Inspect those pages.
You should ask:
Which sources shape the answer? 
Which ones compare options? 
Which ones have a table, list, framework, or explanation the system can use? 
Which ones mention competitors but leave you out? 
Which ones mention you without enough context to explain why you belong?
This approach gives link builders a different kind of target list. Your goal isn’t only to secure another backlink. It’s to improve the source material that AI systems may use before they decide which brands belong in the answer.
That can mean adding the brand to a cited page, improving an existing mention, replacing a thin comparison with a clearer one, contributing a table, graphic, short explanation, or other asset chunk that gives the page more useful material to work with.
This still includes links. 
We’re not leaving the link behind.
But a brand mention and anchor text alone is too thin. 
You need anchor context: useful material around the link that helps a system understand the value of the mention.
Whether you’re in-house or working with link builders, you need to ask for more than a backlink. 
Ask for a backlink plus anchor context: a useful piece of context that can help form an AI citation. At a minimum, that chunk should explain the offer, the use case, who it helps, and why it belongs in the answer.
That’s the first shift from link building to citation optimization and increased search AND AI visibility.

AI search can leave high-ranking brands out of buyer answers. Link builders need to optimize the sources that AI systems retrieve and cite.

The next era of link building is citation optimization by Citation Labs

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="In Google Ads automation, everything is a signal in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is turning Ads reporting into a prompt-driven experience, letting advertisers analyse performance with AI-powered dashboards.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="In Google Ads automation, everything is a signal in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is bringing Gemini into Google Ads dashboards, aiming to make data analysis more interactive, visual and accessible.</p>



<p><strong>What’s happening</strong>. Google Ads is rolling out a new Dashboards feature that lets advertisers explore performance data using charts, graphs and tables, powered by Gemini.</p>



<p>Users can customise views simply by typing prompts, with the dashboard updating in real time based on their queries.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/05/google-ads-dashboard-metrics.jpg" alt="" class="wp-image-477261" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-ads-dashboard-metrics.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/05/google-ads-dashboard-metrics-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-ads-dashboard-metrics-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p><strong>Why we care</strong>. Data analysis in Google Ads has traditionally required manual setup and navigation across reports.</p>



<p>This update shifts that workflow toward a more conversational model, where advertisers ask questions and get instant visual answers.</p>



<p><strong>Zoom in</strong>. Dashboards will display key metrics like impressions, clicks, video views and cost, alongside visual breakdowns of performance across devices, audiences and campaign types.</p>



<p>The goal is to give advertisers a clearer, faster way to understand what’s happening in their accounts.</p>



<p><strong>What to watch</strong>. How widely advertisers adopt prompt-based reporting, and whether this reduces reliance on custom-built reports and external analytics tools.</p>



<p><strong>What’s next</strong>. Google says more details will be shared at <a href="https://searchengineland.com/google-marketing-live-2026-set-for-may-20-471141">Google Marketing Live</a>.</p>



<p><strong>Bottom line</strong>. <a href="https://x.com/GoogleAds/status/2053895718432068079">Google is turning reporting into a conversation</a> — using AI to help advertisers get answers faster and act on them sooner.</p>


Google is bringing Gemini into Google Ads dashboards, aiming to make data analysis more interactive, visual and accessible.
What’s happening. Google Ads is rolling out a new Dashboards feature that lets advertisers explore performance data using charts, graphs and tables, powered by Gemini.
Users can customise views simply by typing prompts, with the dashboard updating in real time based on their queries.




Why we care. Data analysis in Google Ads has traditionally required manual setup and navigation across reports.
This update shifts that workflow toward a more conversational model, where advertisers ask questions and get instant visual answers.
Zoom in. Dashboards will display key metrics like impressions, clicks, video views and cost, alongside visual breakdowns of performance across devices, audiences and campaign types.
The goal is to give advertisers a clearer, faster way to understand what’s happening in their accounts.
What to watch. How widely advertisers adopt prompt-based reporting, and whether this reduces reliance on custom-built reports and external analytics tools.
What’s next. Google says more details will be shared at Google Marketing Live.
Bottom line. Google is turning reporting into a conversation — using AI to help advertisers get answers faster and act on them sooner.

Google is turning Ads reporting into a prompt-driven experience, letting advertisers analyse performance with AI-powered dashboards.

Google Ads adds Gemini-powered dashboards for real-time insights

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/google-discover-publishers.png" class="attachment-large size-large wp-post-image" alt="Google Discover publishers" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-discover-publishers.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-discover-publishers-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-discover-publishers-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/google-discover-publishers-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The first systematic analysis of Google's invitation-only enhanced publisher profiles on Discover, based on monitoring 46,926 publishers.

Google Discover has publisher profile pages. They live at profile.google.com/cp/ and appear when someone taps a publisher’s name on a Discover card. These pages aren’t new. They launched in August 2025 with the Follow button rollout, and by November 2025 Google’s documentation referred to them as “source overviews.”
For most of the 47,000+ publishers we monitored, the pages are auto-generated: a name, follower count, social links pulled from the Knowledge Graph, recent posts, and a footer label that reads “Profile generated by Google.”
Since March 2026, though, something changed for a small subset of publishers. A group gained access to enhanced profiles: custom banner images, a configurable links shelf, and the ability to pin posts (labeled “Pinned” in the publisher interface, formerly “Featured Posts”).
They also gained control over the order of their social links, website, and content tabs — something standard profiles don’t allow. On standard profiles, social links are sorted algorithmically by follower count, with the website listed last. On claimed profiles, the publisher decides.
The “Profile generated by Google” label also disappeared entirely, replaced by nothing — a quiet signal that the profile had been claimed.
There’s no public documentation explaining how to get access. No Search Console toggle. No application form. Google appears to have hand-selected participants for what is effectively an invitation-only pilot program.
We identified 54 publishers in this cohort. All are U.S.-based. All publish in English. And what they have — and haven’t — done with the feature over two months of monitoring reveals patterns every publisher should watch before the program scales.
How we found the 54
Our Profile Features Monitor tracks 46,926 publishers across seven languages: English, French, German, Italian, Spanish, Dutch, and Portuguese. To isolate the enhanced cohort, we filtered for publishers that showed persistent enhanced-profile signals across multiple snapshots: active links, full banner headers, or both.
The result: 54 domains with stable access to the enhanced profile surface. The composition of that group offers clues about Google’s intentions:

TierPublishersExamples
National15WSJ, Fox News, NY Post, Newsweek, Inquirer
Regional Paper13Boston Globe, SFGate, CT Insider, Times Union
Local TV14KTLA, PIX11, MyFox8, WSMV, Atlanta News First
Lifestyle Brand6Delish, The Dodo, Country Living, House Beautiful
Specialty6Pew Research, The Athletic, Gothamist, Civil Beat





The skew toward local news and community publishers is striking and aligns with Google’s public emphasis on supporting local journalism. Nearly half the cohort — 27 of 54 publishers — consists of regional newspapers and local TV stations. National brands are included too, but they’re not the majority.
The two-tier profile system
Under the hood, Google operates two distinct profile architectures. Understanding the difference matters because this isn’t just a cosmetic upgrade. It’s a structural split.
Standard profile (99.9% of publishers):




Auto-generated from public sources.
“Profile generated by Google” label visible.
No publisher control over content or layout.
Claimed profile (the 54 publishers):




No generation label.
Publisher can configure the banner, links shelf, and pinned post.
Publisher controls the order of social links, website, and content tabs (standard profiles sort them by follower count).
This isn’t Search Console verification, structured data markup, or any existing publisher tool. It’s a separate, invitation-only system.
What the 54 publishers actually did
This is where it gets interesting. Access to a feature and its effective use are different. Here’s what the data shows across each configurable surface.
Banners: professional, deliberate, tier-predictive
Forty-one of the 54 publishers uploaded a banner image. The remaining 13 have the capability — a “prepared” state — but haven’t used it yet.
What stands out is the production quality. There are no amateur banners in the cohort. Every uploaded image reflects clear professional design investment.
Five distinct visual archetypes emerged:
Brand-pattern: No photography, just the wordmark or abstract identity repeated as a tile. Pure prestige.

WSJ
Barron’s
Pew Research
Editorial content: The banner shows what the publisher covers. A food shot, a puppy, a stock chart.

Delish
The Dodo
Fox Business
Local landmark: City skylines, local scenery, and regional identity anchors.

KTLA
Atlanta News First
Boston Globe
Brand-statement: Curated collages with taglines or portfolio displays:

SecretNYC
NY Magazine
Front-page archive: A grid of 12 iconic covers. Tabloid heritage as visual identity. Unique in the cohort.

NY Post
Tier predicts archetype. National publishers cluster around brand-pattern banners. Local outlets lean into civic identity and city imagery. Lifestyle brands showcase their content directly.
One anomaly: The Athletic uploaded a solid black square — 656×656 pixels. Whether that reflects deliberate minimalism aligned with The Athletic’s dark UI or simply a broken upload is unclear. It’s the only non-image banner in the cohort.
The format split is revealing: 71% used square banners — likely Google’s recommended ratio — while 29% used wide landscape formats. None used portrait layouts. Based on CDN serving patterns, the minimum recommended resolution appears to be 512 pixels on the longest side.
Publishers that chose wide formats made deliberate design decisions: SecretNYC uses a manifesto-style collage, the New York Post uses a headline grid, and Barron’s uses a geometric pattern. Square appears to be the default safe option.
Links: local TV dominates, nationals ignore it
Thirty-three of the 54 publishers enabled the links feature. Of those, 31 added at least one link, for a total of 65 configured links across the cohort.
The content is overwhelmingly focused on on-site navigation: 85% of links point to the publisher’s own sections, weather pages, live streams, or app downloads. This functions more like a mini site navigation layer than a promotional surface.
The tier gap is enormous:
Local TV: 31 links across 14 hosts (average 2.2 per publisher). Fox affiliates consistently shelve: Watch Live, Weather, Local News, Sub-region, Contact.
National: 9 links across 15 hosts (average 0.6 per publisher). Most nationals didn’t bother.
Three outliers worth noting:
PIX11 published “How to make PIX11 a preferred source on Google,” meta-promoting Discover follows from within the Discover profile itself.
Gothamist funneled donations through `pledge.wnyc.org` with a purpose-specific utm_campaign=discover-profile tag.
Fox Nation placed a direct subscription conversion link (“Subscribe to Fox Nation”) on what most publishers treat as a navigational surface.
Pinned posts (formerly Featured Posts): capability granted, rarely used
Fifty-two of the 54 publishers enabled the Pinned feature. Only 13 currently use it with an active pinned post.
Lifestyle brands were the strongest adopters: five of six had the feature active. Among national publishers, only 2 of 15 used it. The capability exists across nearly the entire cohort. Adoption does not.
About text: Wikipedia out, self-branding in
On standard profiles, the “About” section is auto-generated by Google, usually sourced from Wikipedia. On claimed profiles, publishers write their own.
Within the cohort, 38 of 54 use a custom-written description, while only 16 retain a Wikipedia-sourced version — a surprisingly low number for publishers of this size and prominence.
The tone splits cleanly by publisher tier.
Local TV stations lean promotional (“Your trusted source for breaking news, accurate weather forecasts and local sports across Greensboro…” ). 
National and digital-native publishers stay more factual (“Gothamist is a website about New York City news, arts, events and food, brought to you by New York Public Radio”).
One publisher takes a mission-driven approach: Delish — “you don’t have to know how to cook, you just have to love to eat!”
The implication for publishers preparing for this feature: once you claim the profile, you take control of the About section. It becomes your pitch on a Google-owned page.
Notably, the most visible publishers in the cohort chose factual descriptions over promotional copy.
UTM tracking: the blind spot
Only three of the 65 configured links include analytics parameters. Gothamist tagged its donation link with utm_campaign=discover-profile, making it the only publisher in the cohort treating the profile as a measurable acquisition channel.
The Philadelphia Inquirer instrumented two links, but one reused an Instagram bio campaign tag (mktg_acq_ig_organic_bio_offer), meaning Discover traffic from that link will be misattributed to Instagram in analytics.
The other 62 links have no tracking at all. In practice, 95% of the cohort has no way to measure whether profile links generate traffic.
Social platform priorities
On claimed profiles, publishers control the display order of social links and content tabs. Standard profiles don’t: Google sorts links algorithmically by follower count and places the website last. That means the ordering we observe on claimed profiles reflects deliberate editorial choices, not algorithmic defaults:
Local TV stations list Facebook first: 86% (12 of 14). Zero list X/Twitter first.
National publishers spread their bets: Facebook 33%, Instagram 20%, X 20%, YouTube 13%.
Specialty/digital-native outlets lean Instagram-first (67%).
Concrete examples: Newsweek places YouTube first and Articles second. Delish leads with Website, followed by Instagram. These are active editorial decisions about which audience channel matters most.
The local TV finding is particularly notable. Despite news media’s historical reliance on X/Twitter, not a single local station in this cohort places it as their primary social link.
Sister-site coordination
For media groups with multiple properties in the cohort, setup patterns reveal whether profile management is centralized or handled locally:
Hearst Connecticut, which has five papers in the cohort, shows near-identical configuration across all profiles. The links structure is the same, including a shared Hearst checkout funnel with publication-specific site IDs. The setup points to a centralized digital team managing profile operations across the group. Even so, each masthead still uses distinct banner art.
Dow Jones, across The Wall Street Journal and jp.wsj.com, uses shared banner artwork: the same wordmark tile, confirmed through perceptual hashing. That points to brand coordination at the asset level.
Everyone else Everyone else — including Fox affiliates, Dotdash Meredith properties, and the Fox News group — shows completely different setups across properties, even within owned-and-operated chains. Profile management appears to be handled locally rather than centrally.
The rollout is still active
Comparing snapshots #9 and #12 — taken 19 days apart — confirms this isn’t a frozen experiment. During that window, four publishers added banners (jp.wsj.com, New York Post, SecretNYC, and Everyday Health), one activated Links for the first time (New York Post), and jp.wsj.com (The Wall Street Journal’s Japanese edition) entered the cohort entirely.
No publishers lost features. The program is still expanding within the cohort, and new participants continue to appear.
The adoption paradox
We scored each publisher on a composite 0–6 scale, assigning one point for each of the following:
Banner uploaded
Links feature active
Featured Posts active
At least one configured link
Four or more social platforms listed
Any UTM tracking present
Nobody scored 6. The distribution:

ScorePublishers%
22241%
31019%
41426%
5815%
600%





National publishers with the largest audiences are the least engaged with the configurable surface, with a mean score of 2.93. Most uploaded a banner and stopped there.
Local TV stations — despite having the smallest Discover footprints — are the most engaged, with a mean score of 3.57. Lifestyle brands score highest overall at 3.83, yet their Discover visibility trajectory is the flattest in the cohort.
And here’s the critical finding: feature adoption shows no correlation with visibility trajectory.
Across the cohort, the 180-day late/early capture ratio ranges from 0.23x for Prevention — down 77% — to 4.27x for NewsNation — up 327%. Variance is massive within every tier.
KTLA scores high on adoption, with seven links, a full banner, and active profile engagement, and grew 3.69x. But Delish also scores high and declined to 0.90x. MyFox8 configured five links and fell to 0.52x.
Publishers that fully utilized the configurable surface show no better visibility trajectory than those who used it minimally.
This feature gives publishers a controlled surface for branding and navigation, not a ranking lever. It’s a profile page, not an algorithm input.



What this means for publishers
The program is U.S.-only and invitation-only for now. Across the six other language markets we monitor — French, German, Italian, Spanish, Dutch, and Portuguese — we found zero enhanced profile deployments: not a single banner or configured link outside the English-language cohort.
But the underlying infrastructure is already in place. All 47,000+ publishers we track already have profile pages with follower counts, social links, and content feeds. The enhanced features sit on top of that existing architecture. Google isn’t rebuilding the system. It’s selectively unlocking capabilities within it.
If — or when — Google scales this, here’s how publishers should prepare:
Audit your structured data now. Profile social links are pulled from your sameAs/JSON-LD markup. Errors there will carry over to your profile. Verify what Google will display before you’re given control.
Design a banner. Use a square format (1:1 ratio) with a minimum resolution of 512px, and treat it as a professional brand asset. The 54 publishers in this cohort set a clear standard: there were no amateur images. Think about which archetype fits your brand: a wordmark tile for prestige brands, local landmarks for regional publishers, or content-driven imagery for vertical and lifestyle outlets.
Plan your link strategy. The data suggests that section navigation and utility content — weather, live streams, and similar recurring destinations — drive the most engagement. Local TV stations treating the profile as a mini site navigation layer are the clearest power users. Decide now which five to seven links represent your most valuable entry points.
Instrument from day one. Almost nobody in the current cohort tracks profile link performance. Adding a dedicated UTM campaign parameter — utm_campaign=discover-profile, for example — would put you ahead of 95% of the pilot group on attribution alone.
If you’re a media group, decide your operating model. Should profile management be centralized or handled newsroom by newsroom? The cohort shows both models. Hearst Connecticut runs one coordinated setup across five papers, while Fox affiliates manage profiles independently at the station level. The important part is that the choice is deliberate — not something decided accidentally when individual newsrooms start receiving invitations.
Methodology
Data comes from the 1492.vision Profile Features Monitor, which tracks roughly 47,000 publishers across seven languages through recurring snapshots of profile metadata. The 54-publisher cohort was identified through persistent enhanced-feature signals observed across multiple snapshots between March and May 2026.
Visibility trajectories are based on proprietary capture data. All findings are descriptive only: the cohort reflects Google’s selection criteria, not a random sample, and this dataset does not support causal claims about feature impact.
The full analysis — including the complete 10-phase timeline, banner image gallery, snapshot-by-snapshot evolution, and tier-by-tier breakdowns — is available at 1492.vision/research/discover-publisher-profiles-en.

The first systematic analysis of Google's invitation-only enhanced publisher profiles on Discover, based on monitoring 46,926 publishers.

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