<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Microsoft Ads" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>The update gives advertisers new ways to reach professional audiences across streaming inventory using LinkedIn data.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Microsoft Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Microsoft Advertising is bringing LinkedIn profile targeting to connected TV campaigns, giving advertisers a new way to blend professional audience data with streaming inventory.</p>



<p>The announcement was made by Product Liaison Navah Hopkins during the SEM Stories event on May 14.</p>



<p id="h-why-we-care"><strong>Why we care.</strong> Microsoft has long differentiated itself through access to LinkedIn audience data. Bringing that targeting capability into connected TV extends those signals into a fast-growing upper funnel format that has historically lacked precise professional targeting.</p>



<p>For B2B advertisers especially, the move could help bridge the gap between brand awareness and performance measurement.</p>



<p id="h-what-s-new-according-to-hopkins-advertisers-can-now-target-connected-tv-audiences-using-linkedin-profile-attributes-tied-to-a-user-s-profession"><strong>What’s new.</strong> According to Hopkins, advertisers can now target connected TV audiences using LinkedIn profile attributes tied to a user’s profession.</p>



<p>That means marketers may be able to reach viewers based on professional characteristics such as:</p>



<ul class="wp-block-list">
<li>Industry</li>



<li>Job function</li>



<li>Company category</li>



<li>Professional identity signals</li>
</ul>



<p>Hopkins framed the feature as a way to build “really meaningful” audience lists that are less dependent on click-based intent signals.</p>



<p id="h-the-bigger-picture"><strong>The bigger picture.</strong> The announcement fits into Microsoft’s broader push toward AI-powered and audience-first advertising experiences.</p>



<p>During the session, Hopkins repeatedly emphasized the convergence of brand and performance marketing, arguing that AI-driven journeys are collapsing traditional funnels.</p>



<p>Connected TV sits squarely in that conversation.</p>



<p>Historically, CTV has been treated as a brand-heavy channel with weaker attribution than search or shopping campaigns. LinkedIn-powered audience targeting could make those campaigns more actionable for performance-minded marketers who need tighter audience controls.</p>



<p>The update also strengthens Microsoft’s positioning against competitors in both the streaming and B2B advertising markets.</p>



<p id="h-what-to-watch"><strong>What to watch.</strong> Key questions remain around, available markets, measurement and attribution capabilities, how granular LinkedIn audience segmentation will become in CTV campaigns and privacy and compliance controls for professional audience targeting.</p>



<p>Still, the announcement gives Microsoft another differentiator in a crowded CTV market where advertisers increasingly want stronger audience precision without sacrificing scale.</p>


Microsoft Advertising is bringing LinkedIn profile targeting to connected TV campaigns, giving advertisers a new way to blend professional audience data with streaming inventory.
The announcement was made by Product Liaison Navah Hopkins during the SEM Stories event on May 14.
Why we care. Microsoft has long differentiated itself through access to LinkedIn audience data. Bringing that targeting capability into connected TV extends those signals into a fast-growing upper funnel format that has historically lacked precise professional targeting.
For B2B advertisers especially, the move could help bridge the gap between brand awareness and performance measurement.
What’s new. According to Hopkins, advertisers can now target connected TV audiences using LinkedIn profile attributes tied to a user’s profession.
That means marketers may be able to reach viewers based on professional characteristics such as:
Industry
Job function
Company category
Professional identity signals
Hopkins framed the feature as a way to build “really meaningful” audience lists that are less dependent on click-based intent signals.
The bigger picture. The announcement fits into Microsoft’s broader push toward AI-powered and audience-first advertising experiences.
During the session, Hopkins repeatedly emphasized the convergence of brand and performance marketing, arguing that AI-driven journeys are collapsing traditional funnels.
Connected TV sits squarely in that conversation.
Historically, CTV has been treated as a brand-heavy channel with weaker attribution than search or shopping campaigns. LinkedIn-powered audience targeting could make those campaigns more actionable for performance-minded marketers who need tighter audience controls.
The update also strengthens Microsoft’s positioning against competitors in both the streaming and B2B advertising markets.
What to watch. Key questions remain around, available markets, measurement and attribution capabilities, how granular LinkedIn audience segmentation will become in CTV campaigns and privacy and compliance controls for professional audience targeting.
Still, the announcement gives Microsoft another differentiator in a crowded CTV market where advertisers increasingly want stronger audience precision without sacrificing scale.

The update gives advertisers new ways to reach professional audiences across streaming inventory using LinkedIn data.

Microsoft Advertising expands LinkedIn profile targeting to CTV

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google Ads API v24.1 adds deeper reporting, expanded AI campaign testing, and new security updates for advertisers.

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2025/04/google-api-data-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google released version 24.1 of the Google Ads API, introducing deeper reporting segmentation, expanded experiment support, and new security features as advertisers continue adapting to increasingly automated campaign environments.</p>



<p>The update also prepares developers for Google’s upcoming data retention policy changes, which take effect next year.</p>



<p id="h-why-it-matters"><strong>Why we care. </strong>The release focuses on three areas that have become increasingly important for advertisers: visibility into performance, creative control, and testing automation.</p>



<p>The update also gives brands more control over how creatives appear in Demand Gen campaigns, an area where automation has often limited customization. </p>



<p>Advertisers and developers that rely heavily on reporting infrastructure will also need to prepare for Google’s upcoming 37-month data retention limit before it starts affecting historical performance analysis in 2026.</p>



<p id="h-mobile-reporting-gets-more-granular"><strong>Mobile reporting gets more granular. </strong>One of the biggest additions is a new mobile device platform segment that lets advertisers break out reporting by operating system.</p>



<p>Using the new <code>segments.mobile_device_platform</code> field, developers can now isolate campaign and customer-level performance across iOS and Android traffic.</p>



<p>That matters because user behavior often varies significantly between operating systems, particularly for app marketers, ecommerce advertisers, and brands optimizing conversion value.</p>



<p id="h-demand-gen-adds-classic-image-support"><strong>Demand Gen adds classic image support. </strong>Google is also giving advertisers more creative control inside Demand Gen campaigns.</p>



<p>The release adds support for static image ads through the <code>classic_display_images</code> field in <code>DemandGenMultiAssetAd</code>, allowing brands to upload creatives that run on the Google Display Network exactly as designed, without AI-generated asset combinations.</p>



<p>The feature appears targeted at advertisers that want stricter control over branding and asset presentation.</p>



<p id="h-passkeys-come-to-google-ads"><strong>Passkeys come to Google Ads.</strong> Security is another major focus in v24.1.</p>



<p>Google introduced a new <code>passkey_enabled</code> field that indicates whether a user has enabled passkey authentication for their Google Ads account.</p>



<p>The update follows Google’s broader push toward passwordless authentication and increased protection around sensitive account actions.</p>



<p id="h-experiment-support-expands"><strong>Experiment support expands.</strong> Google is significantly expanding support for Experiments, one of the platform’s core testing frameworks.</p>



<p>Advertisers can now run and compare experiments across:</p>



<ul class="wp-block-list">
<li>AI Max campaigns</li>



<li>Video campaigns</li>



<li>Demand Gen campaigns</li>



<li>Performance Max campaigns</li>
</ul>



<p>The release also adds more transparent reporting across experimental arms, including metrics such as clicks, conversions, and impressions, making side-by-side analysis easier.</p>



<p>The move reflects Google’s broader strategy of standardizing experimentation across its AI-driven campaign products.</p>



<p id="h-a-major-data-retention-change-is-coming"><strong>A major data retention change is coming.</strong> Google also reiterated that beginning June 1st, Google Ads and related measurement APIs will move to a 37-month retention limit for granular reporting data, including daily, weekly, and hourly statistics.</p>



<p>To support the transition, v24.1 introduces a new error code: <code>DateRangeError.REQUESTED_DATE_GRANULARITY_NOT_SUPPORTED</code>.</p>



<p>Developers querying older granular datasets will need to update reporting workflows before the policy change takes effect.</p>



<p id="h-what-s-next"><strong>What’s next.</strong> <a href="https://ads-developers.googleblog.com/2026/05/announcing-v241-of-google-ads-api.html?m=1">Google says updated client libraries and code samples for v24.1 are already available</a>.</p>



<p>The company is also hosting a live walkthrough of the release across Discord, YouTube Live, and LinkedIn Live.</p>


Google released version 24.1 of the Google Ads API, introducing deeper reporting segmentation, expanded experiment support, and new security features as advertisers continue adapting to increasingly automated campaign environments.
The update also prepares developers for Google’s upcoming data retention policy changes, which take effect next year.
Why we care. The release focuses on three areas that have become increasingly important for advertisers: visibility into performance, creative control, and testing automation.
The update also gives brands more control over how creatives appear in Demand Gen campaigns, an area where automation has often limited customization. 
Advertisers and developers that rely heavily on reporting infrastructure will also need to prepare for Google’s upcoming 37-month data retention limit before it starts affecting historical performance analysis in 2026.
Mobile reporting gets more granular. One of the biggest additions is a new mobile device platform segment that lets advertisers break out reporting by operating system.
Using the new segments.mobile_device_platform field, developers can now isolate campaign and customer-level performance across iOS and Android traffic.
That matters because user behavior often varies significantly between operating systems, particularly for app marketers, ecommerce advertisers, and brands optimizing conversion value.
Demand Gen adds classic image support. Google is also giving advertisers more creative control inside Demand Gen campaigns.
The release adds support for static image ads through the classic_display_images field in DemandGenMultiAssetAd, allowing brands to upload creatives that run on the Google Display Network exactly as designed, without AI-generated asset combinations.
The feature appears targeted at advertisers that want stricter control over branding and asset presentation.
Passkeys come to Google Ads. Security is another major focus in v24.1.
Google introduced a new passkey_enabled field that indicates whether a user has enabled passkey authentication for their Google Ads account.
The update follows Google’s broader push toward passwordless authentication and increased protection around sensitive account actions.
Experiment support expands. Google is significantly expanding support for Experiments, one of the platform’s core testing frameworks.
Advertisers can now run and compare experiments across:
AI Max campaigns
Video campaigns
Demand Gen campaigns
Performance Max campaigns
The release also adds more transparent reporting across experimental arms, including metrics such as clicks, conversions, and impressions, making side-by-side analysis easier.
The move reflects Google’s broader strategy of standardizing experimentation across its AI-driven campaign products.
A major data retention change is coming. Google also reiterated that beginning June 1st, Google Ads and related measurement APIs will move to a 37-month retention limit for granular reporting data, including daily, weekly, and hourly statistics.
To support the transition, v24.1 introduces a new error code: DateRangeError.REQUESTED_DATE_GRANULARITY_NOT_SUPPORTED.
Developers querying older granular datasets will need to update reporting workflows before the policy change takes effect.
What’s next. Google says updated client libraries and code samples for v24.1 are already available.
The company is also hosting a live walkthrough of the release across Discord, YouTube Live, and LinkedIn Live.

Google Ads API v24.1 adds deeper reporting, expanded AI campaign testing, and new security updates for advertisers.

Google expands Ads API testing tools in v24.1 release

<div><img width="1981" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant.png" class="attachment-large size-large wp-post-image" alt="Google Analytics AI assistant" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant.png 1981w, https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant-768x419.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant-1536x837.png 1536w" sizes="(max-width: 1981px) 100vw, 1981px" /></div>You can now see in Google Analytics which AI tools drive visits and whether users from ChatGPT, Claude, or Gemini convert differently.

<div><img width="1981" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant.png" class="attachment-large size-large wp-post-image" alt="Google Analytics AI assistant" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant.png 1981w, https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant-768x419.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-analytics-ai-assistant-1536x837.png 1536w" sizes="(max-width: 1981px) 100vw, 1981px" /></div>
<p>Google Analytics added a new AI Assistant channel that tracks traffic from chatbots like ChatGPT, Gemini, and Claude. The update should help you measure visits from AI assistants without using custom filters or workarounds.</p>



<p><strong>What&#8217;s new. </strong>Google Analytics now automatically labels traffic from supported AI assistants with new traffic source values. When someone clicks on your site from a supported AI chatbot, Google Analytics will automatically assign that visit to one of these new channels.</p>



<ul class="wp-block-list">
<li><strong>Medium:</strong> ai-assistant</li>



<li><strong>Channel Group:</strong> “AI Assistant”</li>



<li><strong>Campaign:</strong> (ai-assistant)</li>
</ul>



<p id="h-why-we-care-this-update-gives-marketers-a-cleaner-way-to-track-that-traffic-directly-inside-standard-ga4-reports"><strong>Why we care. </strong>This update should help you track AI traffic directly inside standard GA4 reports. It should be easier to track things like which AI assistants send the most traffic, whether AI traffic is growing, how AI traffic compares to organic search and other channels, and whether visitors from AI tools convert differently.</p>



<p id="h-why-we-care-this-update-gives-marketers-a-cleaner-way-to-track-that-traffic-directly-inside-standard-ga4-reports"><strong>The announcement.</strong> <a href="https://support.google.com/analytics/answer/9164320?hl=en#05132026" target="_blank" rel="noopener">New AI Assistant traffic measurement</a>


Google Analytics added a new AI Assistant channel that tracks traffic from chatbots like ChatGPT, Gemini, and Claude. The update should help you measure visits from AI assistants without using custom filters or workarounds.
What’s new. Google Analytics now automatically labels traffic from supported AI assistants with new traffic source values. When someone clicks on your site from a supported AI chatbot, Google Analytics will automatically assign that visit to one of these new channels.
Medium: ai-assistant
Channel Group: “AI Assistant”
Campaign: (ai-assistant)
Why we care. This update should help you track AI traffic directly inside standard GA4 reports. It should be easier to track things like which AI assistants send the most traffic, whether AI traffic is growing, how AI traffic compares to organic search and other channels, and whether visitors from AI tools convert differently.
The announcement. New AI Assistant traffic measurement

You can now see in Google Analytics which AI tools drive visits and whether users from ChatGPT, Claude, or Gemini convert differently.

Google Analytics adds AI Assistant channel to measure AI traffic

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-prioritize-technical-SEO-fixes-by-business-impact-SEL.png" class="attachment-large size-large wp-post-image" alt="How to prioritize technical SEO fixes by business impact" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-prioritize-technical-SEO-fixes-by-business-impact-SEL.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-prioritize-technical-SEO-fixes-by-business-impact-SEL-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-prioritize-technical-SEO-fixes-by-business-impact-SEL-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-prioritize-technical-SEO-fixes-by-business-impact-SEL-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Not every critical error deserves immediate attention. Focus your resources on the fixes that actually improve visibility and performance.

You just ran a crawl of your website. The report flags hundreds of technical issues, many marked by your tool of choice as high priority. You map out a plan based on best practices, and you’re already dreading the email to your developers.
But here’s the catch: Many of those “critical errors” don’t actually matter. You can spend weeks resolving “high-priority” technical issues and still see no meaningful impact on traffic or conversions. 
Some fixes look critical and do absolutely nothing. A 404 buried six levels deep in the site architecture? Probably not worth the fire drill it causes.
Meanwhile, a seemingly minor internal linking issue on high-value category pages might be suppressing millions in revenue.
The problem isn’t technical SEO. It’s the persistent myth that all fixes carry equal weight. They don’t.
One of the biggest maturity shifts you can make as an SEO is moving from issue-based SEO to impact-based SEO. Because the goal isn’t to fix everything. It’s to fix what actually moves the needle.
Why critical doesn’t always mean impactful
Technical SEO tools are incredibly useful. But, they’re also incredibly good at creating anxiety. Crawl reports, site health dashboards, and those “critical” red flags often create the illusion that every flagged issue deserves immediate attention. 
But a tool may label something as a “critical issue” because it violates a best practice. That doesn’t automatically mean it’s hurting organic performance. 
This is where we lose time. These tools confuse technical correctness with search impact.
A site can be technically imperfect and still perform exceptionally well in search. Likewise, a site can have an impressive CWV score and still underperform because the wrong problems are being prioritized. Some issues are cosmetic, some matter only at scale, and some are tied to old-school best practices that don’t affect rankings. 
Technical SEO should be measured by outcomes, not arbitrary scores from an array of tools.
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Not all issues affect search in the same way 
A helpful way to prioritize fixes is to understand which layer of performance the issue affects. Is it indexing, or rendering, or user experience? Or a combination of all of the above?
Indexing issues
These affect whether pages can appear in search at all. Some examples include:
Noindex tags.
Robots blocking.
Sitemap omissions.
Canonical conflicts.
These tend to be the highest priority. If search engines can’t access or index the page, rankings are impossible.
Rendering issues
These affect how search engines understand and interpret content across the site. Examples include:
JavaScript-delayed content.
Lazy-loaded content incorrectly rendered.
Blocked JS or CSS resources.
These are especially important for JavaScript-heavy frameworks and dynamic experiences (anything built with React, Angular, a headless CMS, etc.).
UX and performance issues
These influence engagement signals and conversion behavior. Examples may include:
Slow page speed.
Layout shifts.
Intrusive interstitials.
Poor mobile usability.
This type of issue may not directly impact a site’s ability to rank well, but it can affect engagement and conversions, which, in turn, can impact organic visibility. Sometimes SEO is less about rankings and more about protecting the traffic you already earn.
Dig deeper: Where to focus technical SEO when you can’t do it all



A practical framework for prioritization
Before moving a technical SEO fix to the top of your list of priorities, it can be helpful to pressure-test it against a simple decision framework. 
Start by asking three questions: 
Does this issue affect crawlability or indexing? If search engines can’t access, render, or index the page correctly, that’s usually a priority. 
Does it impact high-value pages or sections? A small issue affecting thousands of product pages or top-performing content is rarely small in practice.
Is there evidence this issue is suppressing traffic or rankings? Look for signals in performance data, not just audit tools. Ranking drops, stagnant pages, indexing anomalies, and crawl inefficiencies all tell a more useful story than issue counts alone.
Once you have a better understanding of the answers to these questions, you can use a prioritization matrix to help create a plan of action.





	
	
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High-effort, low-impact fixes
Let’s start with the work that tends to consume a disproportionate amount of our time. These are the fixes that look important in an audit but often produce little measurable lift.
Fixing every 404 on the site
Not all 404s are a problem. Fixing a broken URL may have virtually no impact if it:
Has no backlinks.
Receives no organic traffic.
Is not internally linked.
Isn’t part of a key user journey.
This is especially common on large publisher or ecommerce sites with legacy URLs, expired products, or campaign landing pages.
Teams can spend weeks cleaning these up without affecting visibility. The real question is whether the broken page is still being crawled frequently, holding authority, or disrupting conversion paths.
If not, it’s often maintenance work, not growth work.
Chasing minor Core Web Vitals fluctuations sitewide
Site speed matters. But not every performance fix deserves equal urgency. A minuscule shift in CLS on low-traffic blog posts is rarely as impactful as improving render speed on revenue-driving pages.
Core Web Vitals scores have become a hot topic in general. They certainly do matter, and it’s one of the few concrete metrics Google provides in terms of measuring SEO performance. But too often, teams prioritize significant engineering work because a dashboard score dipped slightly, rather than focusing on where performance intersects with rankings and user behavior.
If your category pages, product pages, or article templates are already performing well, marginal speed gains may not produce meaningful lift.
Image alt text on assets with minimal search value
Alt text is essential for accessibility. That alone makes it worth doing. But in terms of SEO impact, not all alt text work deserves equal priority.
Updating alt text across large volumes of legacy images, especially on low-traffic or outdated pages, is often high-effort with little return. If those images aren’t driving visibility through image search and the pages they live on don’t perform well organically, the SEO upside is minimal.
Where alt text does move the needle is on:
High-traffic pages.
Image-driven content.
Ecommerce product imagery.
Treat alt text as a priority where it supports discoverability or user experience at scale, not just because it shows up in an audit.
Header tags that look wrong (but aren’t hurting anything)
Header tags are one of the most over-policed elements in technical SEO. 
Multiple H1s? Skipped heading levels? Tools love to flag them. And on paper, those flags look serious. But in reality, this is often a high-effort cleanup with little to no impact.
Search engines are much better at understanding page structure than they used to be. They don’t rely solely on perfectly nested HTML headings to interpret content hierarchy. In many cases, visual hierarchy, layout, and contextual signals do just as much heavy lifting.
It’s also common for header styles to be defined by design styles rather than strict semantic markup. A page might have multiple H1s, but still present a clear, logical structure to both users and search engines. So it’s not inherently a problem. 
Where header tags do matter is when they create confusion:
No clear primary topic or heading on the page.
Headings that don’t align with search intent.
Structural issues that make content harder to parse (for users or crawlers).
As with most things in technical SEO, the goal with headers isn’t perfection. It’s clarity and impact. If users and search engines already understand the page, this probably isn’t the fix that moves the needle.
Over-optimizing structured data
Schema markup helps search engines better understand content and can unlock rich results. But adding increasingly granular schema types to every page template often diminishes returns.
Going from no product schema to valid product schema? Huge.
Adding optional niche properties that don’t change the SERP appearance? Minimal.
Sometimes we treat schema like a compliance exercise rather than part of a visibility strategy. But if it doesn’t influence comprehension or SERP presentation, it may not be the highest-value work.
Dig deeper: How soft 404s and indexing issues caused a 90% traffic collapse



Low-effort, high-impact wins
Now for the work that often drives outsized returns. These are the fixes that directly affect crawlability, discoverability, and user experience.
Internal linking to high-value pages
This is one of the most overlooked technical wins. A few strategic internal links from authoritative pages to underperforming high-intent pages can improve:
Crawl frequency.
Page discovery.
Contextual relevance.
Authority flow.
Compared to complex engineering tickets, this is often low lift with measurable impact. Especially for ecommerce category pages, subcategory pages, and seasonal landing pages — these can gain traction quickly with better internal link support.
Duplicate content and canonical issues
Duplicate content coupled with improper canonicals can substantially impact rankings in a negative way. This is especially common with faceted navigation, pagination, filtered product collections, and syndicated content. Issues may range from parameter handling to trailing slash inconsistencies.
When search engines are forced to choose among near-duplicate URLs, ranking signals can fragment.
Fixing canonicals, parameter handling, or indexing rules can dramatically improve performance. This is often a relatively small technical change with major implications.
Resolving accidental noindex or robots directives
This sounds obvious, but it happens more than teams admit. Staging directives make it into production. Template updates accidentally apply noindex rules. Important JS resources get blocked.
These are classic low level of effort, high-impact issues because they directly affect discoverability. And again, when pages can’t be crawled or indexed, nothing else matters. This isn’t Field of Dreams and traffic isn’t guaranteed just because you built the page. 
Rendering and JavaScript issues that hide your content
This is where things can break fast and quietly. If important content isn’t visible in the rendered HTML, search engines (and even LLMs) may not see it at all. That includes:
Client-side rendered pages that rely entirely on JavaScript.
Delayed content hydration.
Critical elements (copy, links, metadata) that only load after initial render.
In these cases, the issue is foundational. Search engines have improved their ability to process JavaScript, but it’s not guaranteed, and it’s not always immediate. If your content depends on perfect rendering to exist, you’re introducing risk to your ability to be indexed and ranked.
Often, the solution isn’t a full rebuild. It’s targeted. Ensure key content is server-side rendered or pre-rendered. Reduce reliance on delayed JS for above-the-fold content and make sure critical elements exist in the initial HTML response. 
Dig deeper: No-JavaScript fallbacks in 2026: Less critical, still necessary



Why impact vary by site type
Best practices often get treated like universal truths when, in reality, context changes everything. The same fix can drive meaningful growth on one site and do absolutely nothing on another. Without understanding the business model, site structure, and how organic search actually drives value, prioritization becomes guesswork.
Publisher sites
For publishers, it’s all about speed, scale, and freshness. These sites live and die by how quickly content gets discovered and indexed. That means technical priorities tend to center around:
Discoverability via internal linking, recirculation, tagging, etc.
XML and dynamic sitemaps.
Pagination and archive structure.
Render speed for content-heavy templates.
If Google can’t quickly crawl new content, it doesn’t matter how well it’s written. It won’t perform well. Rendering issues are especially risky here. If key content is delayed by JavaScript or not immediately visible, you can miss the window where recency or freshness matters most.
Ecommerce sites
Ecommerce is a different game. Here, technical SEO issues affect visibility and revenue at scale. High-impact areas typically include:
Faceted navigation and parameter handling.
Duplicate URLs across product and category variations.
Product availability and lifecycle management.
Internal linking across category and subcategory structures.
Crawl waste caused by filters, sorting, and pagination.
For an ecommerce site, a single mistake can impact thousands of product detail pages at once. This is where small technical issues become big business problems.
Lead gen and service sites
Lead gen sites tend to be smaller, but the stakes are just as high. Here, the focus shifts to:
Clean indexing (making sure the right pages are eligible to rank).
Clear location and service page architecture.
Page speed and UX on high-conversion pages.
Strong local signals, where applicable.
You don’t need millions of pages to drive impact, but the pages you do have need to perform.
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There’s no universal SEO priority list
And there never will be. What matters is context: how an issue intersects with your site’s structure and content model, and how your business actually generates value from search. While the same fix can be critical for one site, it can be completely irrelevant for another.
A crawl report full of thousands of errors doesn’t mean you’ve found thousands of opportunities. Sometimes, it means you’ve found noise. And sometimes, one fix — a canonical correction, a rendering issue, a blocked page — outweighs everything else on the list.
Real SEO expertise is knowing the difference.

Not every critical error deserves immediate attention. Focus your resources on the fixes that actually improve visibility and performance.

How to prioritize technical SEO fixes by business impact

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond.png" class="attachment-large size-large wp-post-image" alt="Your client sent ChatGPT SEO advice- Here’s how to respond" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The key isn’t arguing with AI outputs. It’s guiding stakeholders through what’s useful, generic, or simply incorrect.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond.png" class="attachment-large size-large wp-post-image" alt="Your client sent ChatGPT SEO advice- Here’s how to respond" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Your-client-sent-ChatGPT-SEO-advice-Heres-how-to-respond-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>“Hi Frank, I had ChatGPT look at our SEO and it has a bunch of recommendations. Can you take care of this for us?”</p>



<p>We’re all getting some version of this email from clients and bosses. Responding is fraught with challenges.</p>



<p>How do you avoid sounding defensive or dismissive? How do you avoid looking territorial while still explaining that some of these recommendations are generic, flawed, or completely wrong?</p>



<p>It’s one thing to know SEO. It’s another to know how to respond tactfully when AI-generated recommendations are suddenly part of the conversation.</p>



<h2 class="wp-block-heading" id="h-resist-the-urge-to-respond-chatgpt-is-wrong">Resist the urge to respond, ‘ChatGPT is wrong’</h2>



<p>It might feel good to tell them the AI output they sent is wrong and that they should leave the SEO to you. But that response usually backfires.&nbsp;</p>



<p>It makes you sound defensive, and it shifts the conversation away from SEO and toward whether you’re being territorial.</p>



<p>Don’t debate ChatGPT. Show the person who sent the recommendations that you can evaluate AI output objectively and professionally.</p>



<p>The first step is acknowledging the effort behind the recommendations before you start evaluating what’s actually useful.</p>



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<h2 class="wp-block-heading" id="h-validate-the-effort">Validate the effort</h2>



<p>Don’t jump directly into your analysis. Start by thanking them for sending it.</p>



<p>Most people forwarding ChatGPT output think they are being helpful. They are trying to contribute ideas, move things forward, or make sure nothing is missed.</p>



<p>If your first move is to attack the recommendations, they will hear you attacking the effort.</p>



<p>Here’s how we opened a recent client response:</p>



<ul class="wp-block-list">
<li>“Hi Dr. _______! Thanks for sending this over. There are a few ideas worth taking a look at. I also have some ideas on data we can give the model so it has more context. I’ll follow up with you afterward with more details.”</li>
</ul>



<p>That response does a few important things:</p>



<ul class="wp-block-list">
<li>It acknowledges the effort behind the recommendations.</li>



<li>It signals that you’re evaluating them objectively.</li>



<li>And it gives you room to separate the useful ideas from the flawed ones later.</li>
</ul>



<p>You’re not admitting that AI uncovered major issues you missed. You’re showing willingness to review the recommendations professionally before making decisions.</p>



<h2 class="wp-block-heading" id="h-follow-up-with-what-s-worth-exploring-nbsp">Follow up with what’s worth exploring&nbsp;</h2>



<p>Don’t lead with everything ChatGPT got wrong. Start with the ideas worth exploring first. That shows you evaluated the recommendations objectively instead of dismissing them outright.</p>



<p>This is where you demonstrate expertise. Don’t dismiss recommendations simply because they came from AI. Assess whether the underlying observation is valid, whether it matters, and whether it’s worth acting on.</p>



<p>For example, I recently reviewed AI-generated feedback on a page our team was working on.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1048" src="https://searchengineland.com/wp-content/seloads/2026/05/image-145.png" alt="" class="wp-image-477380" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-145.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/image-145-768x393.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/image-145-1536x786.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Had a client sent this over, an appropriate response would start with something along the lines of:</p>



<ul class="wp-block-list">
<li>“Thanks for sending this over. I took a look and there is some room to get some more Philadelphia-relevant content/language into this page while keeping it natural. I’ve assigned this to one of our copywriters to get started.”</li>
</ul>



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<h2 class="wp-block-heading" id="h-let-the-sender-come-to-the-conclusion-that-chatgpt-is-wrong">Let the sender come to the conclusion that ChatGPT is wrong</h2>



<p>Once you’ve acknowledged the recommendations worth exploring, you can start addressing the weaker ones. The key is to walk stakeholders through the reasoning rather than simply declaring the AI output is wrong.</p>



<p>For example, we received an AI-generated analysis from a plastic surgeon client claiming that several competitors had “focused their SEO” around a single procedure:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1274" height="872" src="https://searchengineland.com/wp-content/seloads/2026/05/image-146.png" alt="" class="wp-image-477381" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-146.png 1274w, https://searchengineland.com/wp-content/seloads/2026/05/image-146-768x526.png 768w" sizes="(max-width: 1274px) 100vw, 1274px" /></figure>
</div>


<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“Hi Dr. ___________,</p>



<p>Positioning you as the surgeon in your market for a specific procedure goes beyond SEO. This is a fundamental aspect of branding and positioning that could not only drive better user signals, resulting in better rankings, but higher conversion rates as well.&nbsp;</p>



<p>I would note that if you visit these websites, however, you’ll see that they rank well for facelift queries even though they list many other procedures. I can’t figure out why the model is claiming that their SEO is focused on facelift.</p>



<p>They are producing content beyond that procedure, as well:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1244" src="https://searchengineland.com/wp-content/seloads/2026/05/image-143.png" alt="" class="wp-image-477378" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-143.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/image-143-768x467.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/image-143-1536x933.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>So if you decide to go all-in on positioning yourself around a specific procedure, it doesn’t mean we can’t list other procedures on the website, nor does it mean we&#8217;d be limited to writing only about that procedure.&nbsp;</p>



<p>It would largely direct efforts on social media, outdoor, and areas outside of SEO and the website.”</p>
</blockquote>



<p>Notice what this approach does. It:</p>



<ul class="wp-block-list">
<li>Acknowledges the valid strategic point behind the recommendation.</li>



<li>Introduces contradictory evidence calmly.</li>



<li>Allows the stakeholder to recognize the flaw in the AI’s reasoning independently.</li>
</ul>



<p>That is far more persuasive than simply saying “ChatGPT is wrong.”</p>



<h2 class="wp-block-heading" id="h-focus-on-improving-the-analysis-not-debating-the-output">Focus on improving the analysis, not debating the output</h2>



<p>At some point, you need to explain what is really happening: AI outputs are only as good as the prompt and context they are given.</p>



<p>In this case, our client did not provide any context, data, or guidance. He simply asked the model for SEO recommendations for his website.</p>



<p>Continuing the client email:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“&#8230;the model is recommending we add procedure pages in excess of 3,000+ words:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1409" src="https://searchengineland.com/wp-content/seloads/2026/05/image-144.png" alt="" class="wp-image-477379" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-144.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/image-144-768x528.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/image-144-1536x1057.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Luckily, we already have all of these pages up, though our word counts do not exceed 3000.</p>



<p>I checked this against the top-ranking results for these queries and found that almost all have word counts that are much lower than this, which reflects my experience that raw word count does not drive rankings:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1768" height="654" src="https://searchengineland.com/wp-content/seloads/2026/05/image-142.png" alt="" class="wp-image-477377" srcset="https://searchengineland.com/wp-content/seloads/2026/05/image-142.png 1768w, https://searchengineland.com/wp-content/seloads/2026/05/image-142-768x284.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/image-142-1536x568.png 1536w" sizes="(max-width: 1768px) 100vw, 1768px" /></figure>
</div>


<p>I think we should rerun this analysis and make a few changes to the prompt, including asking it to ignore word count.</p>



<p>We should also ask the model to analyze these pages against ours and point out subtopics they cover that our pages have missed, entities they include that we don’t, and how the information density of our content compares to theirs. What do you think?”</p>
</blockquote>



<p>Notice the shift in the conversation. You’re not arguing about whether ChatGPT is “right” or “wrong.” You’re improving the quality of the analysis itself.</p>



<p>This is a much more productive conversation that positions you as collaborative, analytical, and confident in your expertise instead of defensive about it.</p>



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<h2 class="wp-block-heading" id="h-these-emails-aren-t-going-away-learn-how-to-answer-them">These emails aren’t going away. Learn how to answer them.</h2>



<p>You’re going to get more and more of these emails from clients, executives, and internal stakeholders.</p>



<p>Learning how to respond to them effectively will become an increasingly important part of SEO and marketing leadership.</p>



<p>The challenge isn’t just evaluating AI-generated recommendations. It’s doing so in a way that:</p>



<ul class="wp-block-list">
<li>Keeps stakeholders engaged.</li>



<li>Reinforces your expertise.</li>



<li>Doesn’t consume unnecessary time and energy.</li>
</ul>



<p>The next time you feel tempted to send AI-generated recommendations to your accountant, doctor, or IT department, remember what it feels like to be on the receiving end of them.</p>

“Hi Frank, I had ChatGPT look at our SEO and it has a bunch of recommendations. Can you take care of this for us?”
We’re all getting some version of this email from clients and bosses. Responding is fraught with challenges.
How do you avoid sounding defensive or dismissive? How do you avoid looking territorial while still explaining that some of these recommendations are generic, flawed, or completely wrong?
It’s one thing to know SEO. It’s another to know how to respond tactfully when AI-generated recommendations are suddenly part of the conversation.
Resist the urge to respond, ‘ChatGPT is wrong’
It might feel good to tell them the AI output they sent is wrong and that they should leave the SEO to you. But that response usually backfires. 
It makes you sound defensive, and it shifts the conversation away from SEO and toward whether you’re being territorial.
Don’t debate ChatGPT. Show the person who sent the recommendations that you can evaluate AI output objectively and professionally.
The first step is acknowledging the effort behind the recommendations before you start evaluating what’s actually useful.

  
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Get started with
Validate the effort
Don’t jump directly into your analysis. Start by thanking them for sending it.
Most people forwarding ChatGPT output think they are being helpful. They are trying to contribute ideas, move things forward, or make sure nothing is missed.
If your first move is to attack the recommendations, they will hear you attacking the effort.
Here’s how we opened a recent client response:
“Hi Dr. _______! Thanks for sending this over. There are a few ideas worth taking a look at. I also have some ideas on data we can give the model so it has more context. I’ll follow up with you afterward with more details.”
That response does a few important things:
It acknowledges the effort behind the recommendations.
It signals that you’re evaluating them objectively.
And it gives you room to separate the useful ideas from the flawed ones later.
You’re not admitting that AI uncovered major issues you missed. You’re showing willingness to review the recommendations professionally before making decisions.
Follow up with what’s worth exploring 
Don’t lead with everything ChatGPT got wrong. Start with the ideas worth exploring first. That shows you evaluated the recommendations objectively instead of dismissing them outright.
This is where you demonstrate expertise. Don’t dismiss recommendations simply because they came from AI. Assess whether the underlying observation is valid, whether it matters, and whether it’s worth acting on.
For example, I recently reviewed AI-generated feedback on a page our team was working on.




Had a client sent this over, an appropriate response would start with something along the lines of:
“Thanks for sending this over. I took a look and there is some room to get some more Philadelphia-relevant content/language into this page while keeping it natural. I’ve assigned this to one of our copywriters to get started.”
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Let the sender come to the conclusion that ChatGPT is wrong
Once you’ve acknowledged the recommendations worth exploring, you can start addressing the weaker ones. The key is to walk stakeholders through the reasoning rather than simply declaring the AI output is wrong.
For example, we received an AI-generated analysis from a plastic surgeon client claiming that several competitors had “focused their SEO” around a single procedure:





“Hi Dr. ___________,
Positioning you as the surgeon in your market for a specific procedure goes beyond SEO. This is a fundamental aspect of branding and positioning that could not only drive better user signals, resulting in better rankings, but higher conversion rates as well. 
I would note that if you visit these websites, however, you’ll see that they rank well for facelift queries even though they list many other procedures. I can’t figure out why the model is claiming that their SEO is focused on facelift.
They are producing content beyond that procedure, as well:




So if you decide to go all-in on positioning yourself around a specific procedure, it doesn’t mean we can’t list other procedures on the website, nor does it mean we’d be limited to writing only about that procedure. 
It would largely direct efforts on social media, outdoor, and areas outside of SEO and the website.”
Notice what this approach does. It:
Acknowledges the valid strategic point behind the recommendation.
Introduces contradictory evidence calmly.
Allows the stakeholder to recognize the flaw in the AI’s reasoning independently.
That is far more persuasive than simply saying “ChatGPT is wrong.”
Focus on improving the analysis, not debating the output
At some point, you need to explain what is really happening: AI outputs are only as good as the prompt and context they are given.
In this case, our client did not provide any context, data, or guidance. He simply asked the model for SEO recommendations for his website.
Continuing the client email:
“…the model is recommending we add procedure pages in excess of 3,000+ words:




Luckily, we already have all of these pages up, though our word counts do not exceed 3000.
I checked this against the top-ranking results for these queries and found that almost all have word counts that are much lower than this, which reflects my experience that raw word count does not drive rankings:




I think we should rerun this analysis and make a few changes to the prompt, including asking it to ignore word count.
We should also ask the model to analyze these pages against ours and point out subtopics they cover that our pages have missed, entities they include that we don’t, and how the information density of our content compares to theirs. What do you think?”
Notice the shift in the conversation. You’re not arguing about whether ChatGPT is “right” or “wrong.” You’re improving the quality of the analysis itself.
This is a much more productive conversation that positions you as collaborative, analytical, and confident in your expertise instead of defensive about it.

  
complete picture of your search visibility.
      
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Get started with
These emails aren’t going away. Learn how to answer them.
You’re going to get more and more of these emails from clients, executives, and internal stakeholders.
Learning how to respond to them effectively will become an increasingly important part of SEO and marketing leadership.
The challenge isn’t just evaluating AI-generated recommendations. It’s doing so in a way that:
Keeps stakeholders engaged.
Reinforces your expertise.
Doesn’t consume unnecessary time and energy.
The next time you feel tempted to send AI-generated recommendations to your accountant, doctor, or IT department, remember what it feels like to be on the receiving end of them.

The key isn’t arguing with AI outputs. It’s guiding stakeholders through what’s useful, generic, or simply incorrect.

Your client sent ChatGPT SEO advice: Here’s how to respond

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine.png" class="attachment-large size-large wp-post-image" alt="3 ways to turn LinkedIn into a B2B AI discovery engine" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From employee profiles to thought leadership content, these LinkedIn strategies can help B2B brands gain more visibility in AI search.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine.png" class="attachment-large size-large wp-post-image" alt="3 ways to turn LinkedIn into a B2B AI discovery engine" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/3-ways-to-turn-LinkedIn-into-a-B2B-AI-discovery-engine-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><a href="https://searchengineland.com/linkedin-advertising-a-comprehensive-guide-439831">LinkedIn</a> has always been a key driver for B2B discovery, but over the past few years, a new layer of upper-funnel clout has developed: the platform’s influence on AI search citations.</p>



<p>LLMs are increasingly influential in how B2B buyers discover products and services, and LinkedIn has become a top source of this information. This means that if your brand effectively optimizes its LinkedIn presence and content flow for AI search ingestion, you’ll likely get a corresponding bump in AEO-based discovery.</p>



<p>In our work with B2B clients (mostly of the high-growth SaaS variety), we’ve divided this LinkedIn AEO initiative into three segments:</p>



<ul class="wp-block-list">
<li>Optimize earned media.</li>



<li>Feed LLMs strategic content.</li>



<li>Invest in post-engagement that strengthens LLM signals.</li>
</ul>



<p>Here’s how to approach each segment and the outcomes you can expect.</p>



<h2 class="wp-block-heading" id="h-1-optimize-earned-media-website-company-pages-and-high-profile-employee-pages">1. Optimize earned media: website, company pages, and high-profile employee pages</h2>



<p>If you need reasons to keep your website optimized and your LinkedIn pages (both your company page and the pages of your high-profile employees, such as content contributors and thought leaders) up to date, here you go: Doing so feeds LLMs signals that your brand is trustworthy and an authentic source of information.</p>



<p>Much like Google adheres to <a href="https://searchengineland.com/guide/google-e-e-a-t-for-seo">E-E-A-T</a> for traditional SEO, LLMs pull signals from a brand’s earned media to gauge credibility and trustworthiness. Content published on behalf of a brand by its employees and leaders can also contribute to the brand’s reputation, provided those authors are optimizing their owned media.</p>



<h3 class="wp-block-heading" id="h-on-websites-nbsp">On websites&nbsp;</h3>



<p>Make sure your business address, contact information, product descriptions, about pages, and author profile pages are fully built out with good, accurate information.&nbsp;</p>



<h3 class="wp-block-heading" id="h-on-linkedin-company-pages">On LinkedIn company pages</h3>



<p>Pay attention to top-level positioning, your “About” section, and the products and services you offer, providing good, detailed descriptions for each.&nbsp;</p>



<p>This may seem basic, but it’s common for companies to go for excessively long periods without updating their LinkedIn pages, beyond just posting. Take 30 seconds to gauge whether your page is truly up to date or missing messaging that’s integral to appearing in relevant LLM prompts. (For example, if your products and services are particularly relevant to a specific industry, call out that industry in your intro text.)</p>



<p><strong>One more thing: </strong>Make sure your company’s executives and thought leaders also have your company and positioning reflected in their profiles. Better yet, they should be posting on behalf of the company if they’re willing to use their profile on its behalf – that’s just more material telling the LLMs that your company is a real, authentic, trustworthy source of expertise.</p>



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<h2 class="wp-block-heading" id="h-2-feed-the-llms-strategic-content">2. Feed the LLMs strategic content</h2>



<p>Just as a 100-word blog would be a huge outlier to move the needle in organic discovery, meatier content on LinkedIn has proven more influential for AEO visibility (according to recent research our agency’s LinkedIn rep shared with us).</p>



<p>Specifically, 800 to 1,200 words of high-quality, original, differentiated content seems to be a great target for driving AEO mentions. LinkedIn articles and newsletters are perfect forums for this length, since users open them expecting deep dives and won’t instinctively bounce like a Facebook user clicking “&#8230;more” on a post only to see a mass of text below.&nbsp;</p>



<p>Yes, carousels and videos are great for building engagement, and there’s every reason to embed them in newsletters and articles. But early signs are that LLMs really, really like good, richly written content.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/linkedin-ads-on-a-budget-how-one-playbook-drove-sub-10-cpl-472440">LinkedIn Ads on a budget: How one playbook drove sub-$10 CPL</a></em></strong>



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<h2 class="wp-block-heading" id="h-3-invest-in-building-post-engagement">3. Invest in building post-engagement</h2>



<p>More research from our LinkedIn rep: LinkedIn posts with at least 10 quality comments and/or 60 reactions are particularly influential for LLMs. That makes sense, as social proof is a strong signal of authority, and it’s important to note that achieving this level of engagement doesn’t require a ton of added budget.</p>



<p>Yes, you can boost company posts and use Thought Leader Ads (TLAs) and follower ads to build bigger user bases. I almost always <a href="https://searchengineland.com/b2b-linkedin-ads-tests-run-471267">recommend brands test TLAs</a> when having employees do the work of putting up good, relevant content</p>



<p>It’s a good practice to do this anyway — LLMs or not — for posts that get good organic traction and effectively speak to a company’s products, services, or positioning.&nbsp;</p>



<p>Our rep didn’t have any precise data that indicated a correlation between TLAs/boosted posts and greater visibility on LLMs. However, as TLAs and boosted posts are essentially promoted organic posts, they serve as a foundation for stronger organic traction.</p>



<p>Another LinkedIn threshold to note for AEO is that engagement from profiles with less than 3,000 followers (again, this is from our LinkedIn rep) tends to carry more clout with LLMs because those profiles are seen as relatively authoritative and trustworthy.&nbsp;</p>



<p>If you have any employees (including executives) who are over that threshold, empower them to post on behalf of the business by helping them share insights, proprietary data, and any effective tests or methodologies that have driven good results. (While a lot of companies prefer not to tip their hand on the latter, doing so is a great way to build a broad reputation for expertise.)</p>



<p>Don’t stop at employees, either: Consider follower ads to build your company’s follower base, and see if you can form partnerships with verified industry experts (guest blogs and video interviews are great for this) who will amplify your brand’s content. Just ensure that content follows the thought leadership thread of the section above; overly promotional content and straight-up brand messaging won’t get much traction with any audience, machine, or human.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/linkedin-playbook-creators-b2b-marketing-456518">LinkedIn’s new playbook taps creators as the future of B2B marketing</a></em></strong>



<h2 class="wp-block-heading" id="h-ai-search-is-expanding-linkedin-s-influence-in-b2b">AI search is expanding LinkedIn’s influence in B2B</h2>



<p>AEO must now be a careful consideration in your approach to every channel, including Reddit and YouTube. If you’re in B2B and sticking close to the in-platform data you see from LinkedIn, zoom out and carve out some resources to address the initiatives above. The impact of AEO is hard to measure, but it’s only growing as B2B users flock to the LLMs.&nbsp;</p>

LinkedIn has always been a key driver for B2B discovery, but over the past few years, a new layer of upper-funnel clout has developed: the platform’s influence on AI search citations.
LLMs are increasingly influential in how B2B buyers discover products and services, and LinkedIn has become a top source of this information. This means that if your brand effectively optimizes its LinkedIn presence and content flow for AI search ingestion, you’ll likely get a corresponding bump in AEO-based discovery.
In our work with B2B clients (mostly of the high-growth SaaS variety), we’ve divided this LinkedIn AEO initiative into three segments:
Optimize earned media.
Feed LLMs strategic content.
Invest in post-engagement that strengthens LLM signals.
Here’s how to approach each segment and the outcomes you can expect.
1. Optimize earned media: website, company pages, and high-profile employee pages
If you need reasons to keep your website optimized and your LinkedIn pages (both your company page and the pages of your high-profile employees, such as content contributors and thought leaders) up to date, here you go: Doing so feeds LLMs signals that your brand is trustworthy and an authentic source of information.
Much like Google adheres to E-E-A-T for traditional SEO, LLMs pull signals from a brand’s earned media to gauge credibility and trustworthiness. Content published on behalf of a brand by its employees and leaders can also contribute to the brand’s reputation, provided those authors are optimizing their owned media.
On websites 
Make sure your business address, contact information, product descriptions, about pages, and author profile pages are fully built out with good, accurate information. 
On LinkedIn company pages
Pay attention to top-level positioning, your “About” section, and the products and services you offer, providing good, detailed descriptions for each. 
This may seem basic, but it’s common for companies to go for excessively long periods without updating their LinkedIn pages, beyond just posting. Take 30 seconds to gauge whether your page is truly up to date or missing messaging that’s integral to appearing in relevant LLM prompts. (For example, if your products and services are particularly relevant to a specific industry, call out that industry in your intro text.)
One more thing: Make sure your company’s executives and thought leaders also have your company and positioning reflected in their profiles. Better yet, they should be posting on behalf of the company if they’re willing to use their profile on its behalf – that’s just more material telling the LLMs that your company is a real, authentic, trustworthy source of expertise.

  
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2. Feed the LLMs strategic content
Just as a 100-word blog would be a huge outlier to move the needle in organic discovery, meatier content on LinkedIn has proven more influential for AEO visibility (according to recent research our agency’s LinkedIn rep shared with us).
Specifically, 800 to 1,200 words of high-quality, original, differentiated content seems to be a great target for driving AEO mentions. LinkedIn articles and newsletters are perfect forums for this length, since users open them expecting deep dives and won’t instinctively bounce like a Facebook user clicking “…more” on a post only to see a mass of text below. 
Yes, carousels and videos are great for building engagement, and there’s every reason to embed them in newsletters and articles. But early signs are that LLMs really, really like good, richly written content.
Dig deeper: LinkedIn Ads on a budget: How one playbook drove sub-$10 CPL




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
3. Invest in building post-engagement
More research from our LinkedIn rep: LinkedIn posts with at least 10 quality comments and/or 60 reactions are particularly influential for LLMs. That makes sense, as social proof is a strong signal of authority, and it’s important to note that achieving this level of engagement doesn’t require a ton of added budget.
Yes, you can boost company posts and use Thought Leader Ads (TLAs) and follower ads to build bigger user bases. I almost always recommend brands test TLAs when having employees do the work of putting up good, relevant content
It’s a good practice to do this anyway — LLMs or not — for posts that get good organic traction and effectively speak to a company’s products, services, or positioning. 
Our rep didn’t have any precise data that indicated a correlation between TLAs/boosted posts and greater visibility on LLMs. However, as TLAs and boosted posts are essentially promoted organic posts, they serve as a foundation for stronger organic traction.
Another LinkedIn threshold to note for AEO is that engagement from profiles with less than 3,000 followers (again, this is from our LinkedIn rep) tends to carry more clout with LLMs because those profiles are seen as relatively authoritative and trustworthy. 
If you have any employees (including executives) who are over that threshold, empower them to post on behalf of the business by helping them share insights, proprietary data, and any effective tests or methodologies that have driven good results. (While a lot of companies prefer not to tip their hand on the latter, doing so is a great way to build a broad reputation for expertise.)
Don’t stop at employees, either: Consider follower ads to build your company’s follower base, and see if you can form partnerships with verified industry experts (guest blogs and video interviews are great for this) who will amplify your brand’s content. Just ensure that content follows the thought leadership thread of the section above; overly promotional content and straight-up brand messaging won’t get much traction with any audience, machine, or human.
Dig deeper: LinkedIn’s new playbook taps creators as the future of B2B marketing



AI search is expanding LinkedIn’s influence in B2B
AEO must now be a careful consideration in your approach to every channel, including Reddit and YouTube. If you’re in B2B and sticking close to the in-platform data you see from LinkedIn, zoom out and carve out some resources to address the initiatives above. The impact of AEO is hard to measure, but it’s only growing as B2B users flock to the LLMs.

From employee profiles to thought leadership content, these LinkedIn strategies can help B2B brands gain more visibility in AI search.

3 ways to turn LinkedIn into a B2B AI discovery engine

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine.png" class="attachment-large size-large wp-post-image" alt="Inside AI engine" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>New research reveals how ChatGPT fetches pages, selects sources, and summarizes content — with practical prompts to test your own site.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine.png" class="attachment-large size-large wp-post-image" alt="Inside AI engine" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/inside-ai-engine-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>When OpenAI switched default models on March 4, the number of websites cited per response dropped by a fifth, and never recovered. But the citation drop is only part of the story. </p>



<p>We also reverse-engineered ChatGPT&#8217;s internal browsing tools, ran a honeypot experiment, reconstructed its system prompt, and released a new version of our ChatGPT Search Capture plugin.</p>



<h2 class="wp-block-heading" id="h-what-happened">What happened</h2>



<p>On March 4, ChatGPT switched its default model from GPT-4o/5.2 to GPT-5.3 Instant. The result: the average number of unique domains cited per response dropped from 19 to 15, a decline of more than 20%. </p>



<p>Unique URLs per response followed the same trajectory, falling from 24 to 19. We tracked 400 daily prompts over 14 weeks, using monitoring data provided by Meteoria. </p>



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



<p>ChatGPT has 900 million weekly active users. The citation surface in each response hasn&#8217;t changed, but fewer websites are sharing it. Same pie, fewer slices. </p>



<p>This likely reflects a structural shift toward higher-authority sources, but it also means fewer winners overall. Sites that don&#8217;t make the cut are losing visibility that was previously within reach.</p>



<h2 class="wp-block-heading" id="h-the-bigfoot-effect">The Bigfoot Effect</h2>



<p>We named this phenomenon after the &#8220;<a href="https://moz.com/blog/the-bigfoot-update-aka-dr-pete-goes-crazy" target="_blank" rel="noopener">Bigfoot update</a>&#8221; (identified by Dr. Peter J. Meyers of Moz in 2012), when Google would sometimes let a single domain occupy the entire first page of results.</p>



<p>ChatGPT now retrieves fewer domains per response, but the URL-to-domain ratio has remained stable at 1.26. Crawl depth per domain hasn&#8217;t changed. What has changed is how many distinct websites get a seat at the table.</p>



<p>GPT-5.4 Thinking amplifies the concentration further. The model uses &#8220;site:&#8221; operators to restrict searches to trusted domains and distributes its queries across often more than 10 &#8220;fan-out queries&#8221; per response, each targeting a specific source.</p>



<p>Independent log analysis by Jérôme Salomon (Oncrawl) confirms the trend. ChatGPT-User bot crawl volume has settled at a lower level since the switch to 5.3. Some pages simply aren&#8217;t being crawled anymore. </p>



<p>The cause goes beyond model updates: more than 90% of ChatGPT&#8217;s weekly users are on the free plan, and the default experience triggers fewer web searches, uses fewer queries, and produces fewer citations.</p>



<h2 class="wp-block-heading" id="h-how-chatgpt-search-actually-works">How ChatGPT Search actually works</h2>



<p>Our study also includes a full reverse engineering of ChatGPT&#8217;s internal search system, called <code>web.run</code>. Before 5.3, the model sent compact text commands separated by pipes (<code>fast|query|recency</code>). After 5.3, it sends structured JSON objects with typed parameters. </p>



<p>This isn&#8217;t just a format change. It reflects a different architecture in how the model formulates and distributes its web operations.</p>



<p>The web tool now supports 12 operations, up from 4 (plus a separate widget system called <code>genui</code>). These include: </p>



<ul class="wp-block-list">
<li><code>search_query</code></li>



<li><code>open</code></li>



<li><code>find</code></li>



<li><code>click</code></li>



<li><code>screenshot</code></li>



<li><code>product_query</code> </li>



<li>Specialized widgets for sports, finance, weather, and more. </li>
</ul>



<p>GPT-5.4 can chain 5 to more than 10 rounds of search per response, refining queries based on previous results. GPT-5.3 Instant typically runs 2 or 3.</p>



<p>Google&#8217;s fingerprints are still visible: Google tracking markers (<code>strlid</code>) appear in product URLs, and SearchAPI ID-to-token matches reveal the backend&#8217;s reliance on third-party search providers — and Google behind the scenes.</p>



<h2 class="wp-block-heading" id="h-a-new-type-of-fan-out-for-product-queries">A new type of fan-out for product queries</h2>



<p>We uncovered a previously undocumented fan-out type: <code>browse_rewritten_queries</code>. It appears exclusively on product queries, on 5.4 Instant, and is visible in conversation code.</p>



<p>When a user asks something like [best 3D printer to buy in 2026] ChatGPT first runs a single rewrite fan-out to build the full list of candidate products. Then it launches a separate shopping fan-out for each individual product, fetching specs, reviews, and pricing one by one. </p>



<p>Before 5.3, product searches were bundled into a single call. Each product now gets its own dedicated retrieval command.</p>



<h2 class="wp-block-heading" id="h-chatgpt-user-is-the-retrieval-agent">ChatGPT-User is the retrieval agent</h2>



<p>Our honeypot experiment confirmed an important detail. When ChatGPT browses the web following a search during a conversation, the <code>ChatGPT-User</code> crawler — not OAI-SearchBot — fetches the page content. </p>



<p>OpenAI describes OAI-SearchBot as the agent that builds ChatGPT&#8217;s search index, but in practice, the model relies on third-party scraping APIs for search results, then sends ChatGPT-User to retrieve the actual content from selected URLs.</p>



<h2 class="wp-block-heading" id="h-the-namespace-blind-spot">The namespace blind spot</h2>



<p>This may be our most surprising finding.</p>



<p>The trail started with classic reverse engineering. We decompiled the ChatGPT mobile app, dissected the web client source code, and sniffed network packets on both platforms. That gave us the names of internal tools and some calling conventions. </p>



<p>Armed with these specifics, we were able to ask ChatGPT the right questions, and discovered the model answered without any restrictions.</p>



<p>OpenAI has real safeguards around its system prompts. But the internal tool configuration layer has none. </p>



<p>ChatGPT&#8217;s <code>namespaces</code> — the groups of internal tools the model can call during a conversation — are freely describable. As long as you avoid the words &#8220;system prompt,&#8221; the model will disclose tool schemas, operation lists, output channels, and namespace structures with perfect consistency.</p>



<p>We published ready-to-use prompts that anyone can paste into ChatGPT to audit its internal environment. To verify that the model wasn&#8217;t hallucinating these descriptions, we ran a participatory study with dozens of users across separate sessions. Every participant got exactly the same tool names, parameter schemas, and operation lists. The model consistently and reliably describes its own tooling.</p>



<p>The study also includes a reconstructed system prompt extracted progressively, along with several notable findings: </p>



<ul class="wp-block-list">
<li>Reddit is the only domain exempted from copyright word limits.</li>



<li>There is a granular list of banned products.</li>



<li>A &#8220;verbosity score&#8221; operates on a 1–10 scale.</li>



<li>A full advertising policy paragraph governs ad display by subscription tier.</li>
</ul>



<h2 class="wp-block-heading" id="h-practical-use-running-your-own-crawlability-audit">Practical use: running your own crawlability audit</h2>



<p>The web.run syntax we documented isn&#8217;t just a technical curiosity. It works, and it opens a direct path for testing how ChatGPT interacts with your content.</p>



<p>Here&#8217;s a concrete example. You can force ChatGPT to search your domain and read specific pages by pasting JSON commands directly into a conversation. First, trigger a targeted search on your site, then force it to fetch the first two results, then ask it to return the title, main topic, and key points from each page.</p>



<p><code>"Search for this query, then open the first two results and summarize what you find on each page.</code>



<p><code>Step 1: Search:</code>



<p>{ &#8220;search_query&#8221;: [ { &#8220;q&#8221;: &#8220;site:abondance.com seo&#8221; } ], &#8220;response_length&#8221;: &#8220;short&#8221; }</p>



<p><code>Step 2: Open the first two results:</code>



<p>{ &#8220;open&#8221;: [ { &#8220;ref_id&#8221;: &#8220;turn0search0&#8221; }, { &#8220;ref_id&#8221;: &#8220;turn0search1&#8221; } ] }</p>



<p><code>Step 3: Give me a structured recap of what you found on each URL. For each page: the title, the main topic, and 3–5 key points."</code>



<p>What you get is a view of your content through ChatGPT&#8217;s eyes: what it can actually reach, what it extracts, and how it interprets your pages. </p>



<p>If ChatGPT can&#8217;t access a page, returns garbled content, or completely misses your main messages, that&#8217;s a signal to act on.</p>



<h2 class="wp-block-heading" id="h-same-model-family-different-citations">Same model family, different citations</h2>



<p>GPT-5.2, 5.3, and 5.4 share the same knowledge cutoff (August 2025) and belong to the same GPT-5 family. Yet the same prompt sent to each produces different fan-out queries, retrieves different sources, and surfaces different passages in the final response.</p>



<p>Multiple layers of divergence come into play after pre-training: RLHF reward shaping, supervised fine-tuning data, system prompt configurations, and inference-time compute budgets. GPT-5.4 Pro explicitly gets more compute to &#8220;think harder,&#8221; and that alone can change which sources are cited.</p>



<p>This is why we recommend testing model by model. A single prompt can produce radically different citations depending on whether the user is on GPT-5.3 Instant, 5.4 Thinking, or 5.4 Extended. Free-plan users may also be silently routed to a lighter model.</p>



<h2 class="wp-block-heading" id="h-two-types-of-ai-visibility">Two types of AI visibility</h2>



<p>Our study introduces a framework <span>that s</span>eparates <strong>parametric visibility</strong> (what the model learns from training data with search disabled) from <strong>dynamic visibility</strong> (what it retrieves in real time with search enabled).</p>



<ul class="wp-block-list">
<li><strong>Parametric visibility: E-E-A-T for LLMs.</strong> Parametric visibility is the E-E-A-T equivalent for large language models. It&#8217;s authority encoded across billions of training examples, shaped by press coverage, Wikipedia presence, other high-authority sites, and the overall training corpus. It&#8217;s stable and measurable through one-shot API audits.</li>



<li><strong>Dynamic visibility: shifting ground.</strong> Dynamic visibility is volatile. It&#8217;s model-dependent and requires continuous monitoring. It&#8217;s closer to traditional SEO, and can collapse overnight with a model update, as the Bigfoot Effect shows.</li>



<li><strong>The link between the two matters.</strong> The model formulates its web queries by targeting sources it already knows. A brand absent from parametric memory won&#8217;t even be considered as a search candidate. Being unknown to the model means being invisible before the search even starts.</li>
</ul>



<p>Knowledge cutoff updates are the &#8220;Google Dance&#8221; of LLMs. When the cutoff date changes, parametric rankings are redistributed in bulk. But this only happens roughly once a year, because retraining at that scale is extremely expensive. The strategic window for influencing what the model knows about your brand sits between two cutoff dates.</p>



<p>Dan Petrovic&#8217;s (DEJAN) AI Brand Authority Index illustrates parametric measurement at scale. Our study complements it with a lighter, reproducible testing framework based on five prompts run multiple times for a one-shot audit.</p>



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



<p>The full study — including reverse-engineered documentation, the honeypot experiment, DIY audit prompts, and the reconstructed system prompt — is available at <a href="https://think.resoneo.com/chatgpt/5.3-5.4/" target="_blank" rel="noopener">think.resoneo.com/chatgpt/5.3-5.4/</a>.</p>



<h2 class="wp-block-heading" id="h-bottom-line">Bottom line</h2>



<p>ChatGPT Search is no longer a black box. This study maps its internal architecture, from the web.run tool that powers every search to the fan-out logic that decides which domains are fetched and which are ignored.</p>



<p>The 20% drop in cited domains after the switch to 5.3 shows how fast the citation landscape can shift with a single model update. But the deeper issue is structural: ChatGPT is concentrating citations on fewer websites and applying source selection logic shaped by training data, post-training fine-tuning, and system prompt rules that change from one model to the next.</p>



<p>Tracking visibility in ChatGPT means understanding two distinct layers (parametric and dynamic), testing across multiple models, and monitoring a system whose internal tools are documentable but whose behavior can change overnight.</p>



<p>The full study provides the data, methodology, and tools to get started.</p>


When OpenAI switched default models on March 4, the number of websites cited per response dropped by a fifth, and never recovered. But the citation drop is only part of the story. 
We also reverse-engineered ChatGPT’s internal browsing tools, ran a honeypot experiment, reconstructed its system prompt, and released a new version of our ChatGPT Search Capture plugin.
What happened
On March 4, ChatGPT switched its default model from GPT-4o/5.2 to GPT-5.3 Instant. The result: the average number of unique domains cited per response dropped from 19 to 15, a decline of more than 20%. 
Unique URLs per response followed the same trajectory, falling from 24 to 19. We tracked 400 daily prompts over 14 weeks, using monitoring data provided by Meteoria. 
Why we care
ChatGPT has 900 million weekly active users. The citation surface in each response hasn’t changed, but fewer websites are sharing it. Same pie, fewer slices. 
This likely reflects a structural shift toward higher-authority sources, but it also means fewer winners overall. Sites that don’t make the cut are losing visibility that was previously within reach.
The Bigfoot Effect
We named this phenomenon after the “Bigfoot update” (identified by Dr. Peter J. Meyers of Moz in 2012), when Google would sometimes let a single domain occupy the entire first page of results.
ChatGPT now retrieves fewer domains per response, but the URL-to-domain ratio has remained stable at 1.26. Crawl depth per domain hasn’t changed. What has changed is how many distinct websites get a seat at the table.
GPT-5.4 Thinking amplifies the concentration further. The model uses “site:” operators to restrict searches to trusted domains and distributes its queries across often more than 10 “fan-out queries” per response, each targeting a specific source.
Independent log analysis by Jérôme Salomon (Oncrawl) confirms the trend. ChatGPT-User bot crawl volume has settled at a lower level since the switch to 5.3. Some pages simply aren’t being crawled anymore. 
The cause goes beyond model updates: more than 90% of ChatGPT’s weekly users are on the free plan, and the default experience triggers fewer web searches, uses fewer queries, and produces fewer citations.
How ChatGPT Search actually works
Our study also includes a full reverse engineering of ChatGPT’s internal search system, called web.run. Before 5.3, the model sent compact text commands separated by pipes (fast|query|recency). After 5.3, it sends structured JSON objects with typed parameters. 
This isn’t just a format change. It reflects a different architecture in how the model formulates and distributes its web operations.
The web tool now supports 12 operations, up from 4 (plus a separate widget system called genui). These include: 
search_query
open
find
click
screenshot
product_query 
Specialized widgets for sports, finance, weather, and more. 
GPT-5.4 can chain 5 to more than 10 rounds of search per response, refining queries based on previous results. GPT-5.3 Instant typically runs 2 or 3.
Google’s fingerprints are still visible: Google tracking markers (strlid) appear in product URLs, and SearchAPI ID-to-token matches reveal the backend’s reliance on third-party search providers — and Google behind the scenes.
A new type of fan-out for product queries
We uncovered a previously undocumented fan-out type: browse_rewritten_queries. It appears exclusively on product queries, on 5.4 Instant, and is visible in conversation code.
When a user asks something like [best 3D printer to buy in 2026] ChatGPT first runs a single rewrite fan-out to build the full list of candidate products. Then it launches a separate shopping fan-out for each individual product, fetching specs, reviews, and pricing one by one. 
Before 5.3, product searches were bundled into a single call. Each product now gets its own dedicated retrieval command.
ChatGPT-User is the retrieval agent
Our honeypot experiment confirmed an important detail. When ChatGPT browses the web following a search during a conversation, the ChatGPT-User crawler — not OAI-SearchBot — fetches the page content. 
OpenAI describes OAI-SearchBot as the agent that builds ChatGPT’s search index, but in practice, the model relies on third-party scraping APIs for search results, then sends ChatGPT-User to retrieve the actual content from selected URLs.
The namespace blind spot
This may be our most surprising finding.
The trail started with classic reverse engineering. We decompiled the ChatGPT mobile app, dissected the web client source code, and sniffed network packets on both platforms. That gave us the names of internal tools and some calling conventions. 
Armed with these specifics, we were able to ask ChatGPT the right questions, and discovered the model answered without any restrictions.
OpenAI has real safeguards around its system prompts. But the internal tool configuration layer has none. 
ChatGPT’s namespaces — the groups of internal tools the model can call during a conversation — are freely describable. As long as you avoid the words “system prompt,” the model will disclose tool schemas, operation lists, output channels, and namespace structures with perfect consistency.
We published ready-to-use prompts that anyone can paste into ChatGPT to audit its internal environment. To verify that the model wasn’t hallucinating these descriptions, we ran a participatory study with dozens of users across separate sessions. Every participant got exactly the same tool names, parameter schemas, and operation lists. The model consistently and reliably describes its own tooling.
The study also includes a reconstructed system prompt extracted progressively, along with several notable findings: 
Reddit is the only domain exempted from copyright word limits.
There is a granular list of banned products.
A “verbosity score” operates on a 1–10 scale.
A full advertising policy paragraph governs ad display by subscription tier.
Practical use: running your own crawlability audit
The web.run syntax we documented isn’t just a technical curiosity. It works, and it opens a direct path for testing how ChatGPT interacts with your content.
Here’s a concrete example. You can force ChatGPT to search your domain and read specific pages by pasting JSON commands directly into a conversation. First, trigger a targeted search on your site, then force it to fetch the first two results, then ask it to return the title, main topic, and key points from each page.
"Search for this query, then open the first two results and summarize what you find on each page.



Step 1: Search:



{ “search_query”: [ { “q”: “site:abondance.com seo” } ], “response_length”: “short” }
Step 2: Open the first two results:



{ “open”: [ { “ref_id”: “turn0search0” }, { “ref_id”: “turn0search1” } ] }
Step 3: Give me a structured recap of what you found on each URL. For each page: the title, the main topic, and 3–5 key points."



What you get is a view of your content through ChatGPT’s eyes: what it can actually reach, what it extracts, and how it interprets your pages. 
If ChatGPT can’t access a page, returns garbled content, or completely misses your main messages, that’s a signal to act on.
Same model family, different citations
GPT-5.2, 5.3, and 5.4 share the same knowledge cutoff (August 2025) and belong to the same GPT-5 family. Yet the same prompt sent to each produces different fan-out queries, retrieves different sources, and surfaces different passages in the final response.
Multiple layers of divergence come into play after pre-training: RLHF reward shaping, supervised fine-tuning data, system prompt configurations, and inference-time compute budgets. GPT-5.4 Pro explicitly gets more compute to “think harder,” and that alone can change which sources are cited.
This is why we recommend testing model by model. A single prompt can produce radically different citations depending on whether the user is on GPT-5.3 Instant, 5.4 Thinking, or 5.4 Extended. Free-plan users may also be silently routed to a lighter model.
Two types of AI visibility
Our study introduces a framework that separates parametric visibility (what the model learns from training data with search disabled) from dynamic visibility (what it retrieves in real time with search enabled).
Parametric visibility: E-E-A-T for LLMs. Parametric visibility is the E-E-A-T equivalent for large language models. It’s authority encoded across billions of training examples, shaped by press coverage, Wikipedia presence, other high-authority sites, and the overall training corpus. It’s stable and measurable through one-shot API audits.
Dynamic visibility: shifting ground. Dynamic visibility is volatile. It’s model-dependent and requires continuous monitoring. It’s closer to traditional SEO, and can collapse overnight with a model update, as the Bigfoot Effect shows.
The link between the two matters. The model formulates its web queries by targeting sources it already knows. A brand absent from parametric memory won’t even be considered as a search candidate. Being unknown to the model means being invisible before the search even starts.
Knowledge cutoff updates are the “Google Dance” of LLMs. When the cutoff date changes, parametric rankings are redistributed in bulk. But this only happens roughly once a year, because retraining at that scale is extremely expensive. The strategic window for influencing what the model knows about your brand sits between two cutoff dates.
Dan Petrovic’s (DEJAN) AI Brand Authority Index illustrates parametric measurement at scale. Our study complements it with a lighter, reproducible testing framework based on five prompts run multiple times for a one-shot audit.
Dig deeper
The full study — including reverse-engineered documentation, the honeypot experiment, DIY audit prompts, and the reconstructed system prompt — is available at think.resoneo.com/chatgpt/5.3-5.4/.
Bottom line
ChatGPT Search is no longer a black box. This study maps its internal architecture, from the web.run tool that powers every search to the fan-out logic that decides which domains are fetched and which are ignored.
The 20% drop in cited domains after the switch to 5.3 shows how fast the citation landscape can shift with a single model update. But the deeper issue is structural: ChatGPT is concentrating citations on fewer websites and applying source selection logic shaped by training data, post-training fine-tuning, and system prompt rules that change from one model to the next.
Tracking visibility in ChatGPT means understanding two distinct layers (parametric and dynamic), testing across multiple models, and monitoring a system whose internal tools are documentable but whose behavior can change overnight.
The full study provides the data, methodology, and tools to get started.

New research reveals how ChatGPT fetches pages, selects sources, and summarizes content — with practical prompts to test your own site.

Inside ChatGPT Search: how web.run and fan-out queries shape AI visibility

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514.jpg 1672w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-1200x675.jpg 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Learn how to install, deploy, and brand AI skills to turn generic chatbots into your agency’s custom, scalable operating system.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514.jpg 1672w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-1200x675.jpg 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<figure class="wp-block-image size-full"><img decoding="async" width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514.jpg" alt="" class="wp-image-477312" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514.jpg 1672w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-1200x675.jpg 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></figure>



<p><em>Disclosure: I&#8217;m the co-founder of Optmyzr. I&#8217;ll use one of our open-source skills as the example below, but the frameworks here apply to anything you install or build.</em>



<p>If you&#8217;ve used Claude, ChatGPT, or Gemini for marketing work in the last six months, you&#8217;ve probably hit the same wall I have. The chat is great until you need the same thing done the same way every week. Then you&#8217;re back to copying a prompt template into a fresh window, hoping you didn&#8217;t forget a step, wondering why a tool this powerful still feels this manual.</p>



<p>Skills are what bring that wall down.</p>



<p>I&#8217;ve written before about <a href="https://searchengineland.com/claude-skills-ppc-scalable-systems-474221" target="_blank" rel="noopener sponsored nofollow">skills as scalable systems for PPC</a> and <a href="https://searchengineland.com/ai-agents-cant-help-if-they-cant-see-your-marketing-data-475804" target="_blank" rel="noopener sponsored nofollow">why agents are useless without access to your marketing data</a>. This piece zooms out.</p>



<ul class="wp-block-list">
<li>What is a skill, actually?&nbsp;</li>



<li>Where do you find them?&nbsp;</li>



<li>How do they work across the three big AI platforms?&nbsp;</li>



<li>But perhaps most importantly for agency owners: how do you take an existing skill and brand it as your own?</li>
</ul>



<h2 class="wp-block-heading" id="h-what-are-skills-in-ai">What are &#8216;skills&#8217; in AI</h2>



<p>A skill is a small bundle of files that teaches an AI assistant how to do one specific job well, every time.</p>



<p>In Claude, a skill is literally a folder containing a SKILL.md file with instructions, alongside optional code scripts and reference files the AI can process. Install the folder once, and from then on, when a task matches the skill, the assistant loads the playbook and follows it.</p>



<p>It&#8217;s the difference between telling a new hire &#8220;audit this account&#8221; and handing them your agency&#8217;s documented audit process. The output gets much more consistent.</p>



<p>While the concept is universal, implementation varies by platform.&nbsp;</p>



<ul class="wp-block-list">
<li>Claude offers the most seamless experience, allowing you to install and use skills directly within the interface.&nbsp;</li>



<li>ChatGPT makes similar capabilities available though generally only on paid Business or Enterprise plans.&nbsp;</li>



<li>Gemini remains the most developer-focused, often requiring the Gemini CLI or specialized environments, which makes it less accessible for the average marketer.&nbsp;</li>
</ul>



<p>Because of its ease of use, I mostly use skills with Claude, and that’s where we’ll focus.</p>



<h2 class="wp-block-heading" id="h-where-to-find-prebuilt-skills-for-ppc">Where to find prebuilt skills for PPC</h2>



<p>Most account managers prefer copy-pasting scripts over writing their own. They’ll also like grabbing skills someone else already built. But finding them can be tricky. There’s no single App Store for skills, and most of the good ones are on GitHub.</p>



<p>For Claude, the Anthropic team ships official skills for working with things like PDFs, and Microsoft Office programs. Beyond that, you&#8217;ll find growing collections on GitHub from individual developers and software vendors.&nbsp;</p>



<p>A lot of companies are publishing their own. Ours live at <a href="http://github.com/optmyzr-skills" target="_blank" rel="noopener sponsored nofollow">github.com/optmyzr-skills</a>.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1035" src="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-2-scaled.png" alt="" class="wp-image-477318" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-2-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-2-768x388.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-2-1536x777.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption"><em>Optmyzr&#8217;s free Google Ads audit skill. Screenshot by author of Github.com. May 2026</em></figcaption></figure>
</div>


<p>A practical rule I’ve landed on: a skill is only as trustworthy as the team that built it. A skill from a known software vendor with its methodology is different from a one-off prompt repackaged as a skill by someone you’ve never heard of.</p>



<h2 class="wp-block-heading" id="h-ensuring-skill-usage-is-consistent-across-your-organization">Ensuring skill usage is consistent across your organization</h2>



<p>This is where it gets interesting for agencies and in-house teams.</p>



<p>On a solo plan, you install a skill in your own account and you&#8217;re the only one using it. Fine for a freelancer. Painful for a team, because everyone has to install everything separately, and versions drift the moment one person updates and another doesn&#8217;t.</p>



<p>On Team and Enterprise plans, an admin can deploy skills across the whole organization. Claude has org-level skill management on Claude for Work and Enterprise.&nbsp;</p>



<p>The practical benefit is that, with a five-person PPC team, you install a shared audit skill at the org level once, and every account manager gets the same version on day one. When you improve it, everyone gets the improvement automatically. No more &#8220;which version are you running&#8221; on team calls.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1556" height="500" src="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-3.png" alt="" class="wp-image-477319" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-3.png 1556w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-3-768x247.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-3-1536x494.png 1536w" sizes="(max-width: 1556px) 100vw, 1556px" /><figcaption class="wp-element-caption"><em>How to install a skill in Claude. Screenshot by author of claude.ai. May 2026</em></figcaption></figure>
</div>


<p>I think of it like the moment ad scripts stopped living in each individual ad account and moved to Enhanced Scripts from Optmyzr, which lets advertisers deploy a generic script code to all accounts and shifts script versioning and settings into a centralized management system maintained by Optmyzr. Easy, maintainable, and scalable; all things that matter a lot to account teams who promise a standard of quality to their stakeholders.</p>



<h2 class="wp-block-heading" id="h-the-hidden-white-labeling-engine-why-forkable-skills-are-an-agency-s-best-friend">The hidden white-labeling engine: Why forkable skills are an agency’s best friend</h2>



<p>Here&#8217;s the part that should perk up agency owners. Most well-built skills are folders, and the open-source ones live on GitHub. Which means you can fork them, edit them, brand them, and use your modified version with your own clients.</p>



<p>Let me walk you through an example for a 15-person agency. I find an open-source Google Ads audit skill (we&#8217;ll get to the one I&#8217;m thinking of in a second). The default report it generates has &#8220;Google Ads Audit&#8221; at the top in plain text. Useful, but generic and probably not shareable with the client.</p>



<p>What I can do in about an hour:&nbsp;</p>



<ul class="wp-block-list">
<li>Clone or download the skill folder.&nbsp;</li>



<li>Open SKILL.md and edit the report-generation instructions to swap in my agency&#8217;s name, reference my logo, and use my brand colors.&nbsp;</li>



<li>Drop my logo into the skill folder so the assistant can use it when generating PDFs or HTML reports.&nbsp;</li>



<li>Add or remove checks based on what I actually care about — if I run only ecommerce accounts, I can tell the skill to weight Performance Max, Merchant Center, and feed health more heavily.&nbsp;</li>



<li>Repackage and install for my team.</li>
</ul>



<p>What comes out the other side is a branded, agency-specific audit tool that produces client-ready PDFs with my name on them. I didn&#8217;t have to build the underlying methodology. The original author already did that. I just added the last 10% that makes the output feel like mine.</p>



<p>Scripts were powerful because you could tweak them. Skills have the same power. Agencies are no longer capped by what software vendors choose to white-label. If a skill is open source, you can white-label it yourself in an afternoon.</p>



<h2 class="wp-block-heading" id="h-a-worked-example-the-google-ads-audit-skill">A worked example: the Google Ads Audit skill</h2>



<p>Since I keep alluding to it: we recently released a free, open-source Google Ads audit skill at github.com/optmyzr-skills/google-ads-audit. Apache 2.0 license, no Optmyzr login required.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1408" height="1134" src="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-4.jpg" alt="" class="wp-image-477320" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-4.jpg 1408w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-4-768x619.jpg 768w" sizes="(max-width: 1408px) 100vw, 1408px" /><figcaption class="wp-element-caption"><em>A sample of the audit score the Optmyzr audit skill produces. Screenshot by author, May 2026.</em></figcaption></figure>
</div>


<p>Briefly, what it does. It runs through 14 categories and roughly 42 best-practice checks:&nbsp;</p>



<ul class="wp-block-list">
<li>Account settings</li>



<li>Conversion tracking</li>



<li>Campaign structure</li>



<li>Performance Max</li>



<li>Budgets</li>



<li>Bidding</li>



<li>Targeting</li>



<li>Audiences</li>



<li>Keywords</li>



<li>Quality Score</li>



<li>Search terms&nbsp;</li>



<li>RSAs</li>



<li>Extensions</li>



<li>Landing pages</li>



<li>Industry benchmarks</li>



<li>Competitor analysis</li>
</ul>



<p>It asks three calibration questions at the start (primary goal, target CPA or ROAS, account maturity) so the scoring matches the kind of account it&#8217;s looking at.&nbsp;</p>



<p>If you don&#8217;t want to connect anything, there&#8217;s a four-paste flow: pull four CSVs from the Google Ads UI, paste them into Claude, and the skill runs the diagnostic. The output is a top-5 findings list with monthly dollar impact, an A/B/C grade with per-category breakdowns, a 7-day action plan, and a wasted-spend estimate.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1422" height="740" src="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-5.jpg" alt="" class="wp-image-477321" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-5.jpg 1422w, https://searchengineland.com/wp-content/seloads/2026/05/Optmyzr-20260514-5-768x400.jpg 768w" sizes="(max-width: 1422px) 100vw, 1422px" /><figcaption class="wp-element-caption"><em>A sample of the next steps the Optmyzr audit skill suggests to address key shortcomings of a Google Ads account. Screenshot by author, May 2026.</em></figcaption></figure>
</div>


<p>All the principles I described above apply to it. You can install it for free. You can deploy it across your agency. You can fork it, brand it, and have it generate client PDFs with your logo and your methodology framing. The Apache license explicitly allows that.</p>



<p>If you want it to also pull live account data instead of CSVs, run multi-account portfolio rollups, and trigger automated remediation, that&#8217;s where <a href="https://help.optmyzr.com/en/articles/14043758-optmyzr-sidekick-mcp-user-guide" target="_blank" rel="noopener sponsored nofollow">Optmyzr&#8217;s MCP</a> server comes in — and that&#8217;s the paid layer. But the audit logic itself is yours to use, modify, and brand.</p>



<h2 class="wp-block-heading" id="h-what-to-do-with-this">What to do with this</h2>



<p>Pick one repeatable workflow your team does manually right now. Audits, search term reviews, ad copy generation, weekly report drafting — anything that runs the same shape every time is a candidate. Find or build a skill for it.</p>



<p>Then move it from individual installs to team deployment. That single change kills a surprising amount of version drift across a team.</p>



<p>Brand at least one skill as your own, even if you never ship the branded version to clients. Going through the fork-and-modify process once changes how you think about what counts as &#8220;tooling&#8221; for your agency. It&#8217;s lighter than you&#8217;d expect.</p>



<p>Skills are how a generic chatbot starts to behave like your team&#8217;s documented operating system. The agencies that get fluent with installing, deploying, and forking them over the next 12 months are going to operate noticeably differently than the ones still copying prompts into chat windows.</p>



<p>The Optmyzr audit is one example. There will be hundreds.</p>



<h2 class="wp-block-heading" id="h-how-to-install-the-audit-skill">How to install the audit skill</h2>



<p>There are two paths; pick the one that fits your team:</p>



<p>If you have someone technical on the team — an in-house dev, a power-user analyst, anyone comfortable with a GitHub URL — install it as a Claude plugin straight from the repo. One command.&nbsp;</p>



<p>The skill stays in sync. When we ship a new check, tighten a benchmark, or add a category, your install picks it up automatically. For an agency running this across a team, this is the right path. Everyone runs the same version.</p>



<p>If you&#8217;re a marketer who just wants the thing to work — no GitHub, no command line — download the zip from the repo&#8217;s releases page and upload it via <em>Settings > Capabilities > Skills</em> inside Claude Desktop or <a href="http://claude.ai" target="_blank" rel="noopener sponsored nofollow">claude.ai</a>. Up and running in under a minute.</p>



<p>The tradeoff: it&#8217;s a snapshot. When we improve the skill, you&#8217;ll need to grab a new zip and re-upload. For a solo practitioner running monthly audits, that&#8217;s usually fine.</p>



<p>Either way, the repo is at <a href="http://github.com/optmyzr-skills/google-ads-audit" target="_blank" rel="noopener sponsored nofollow">github.com/optmyzr-skills/google-ads-audit</a>. Once it&#8217;s installed, type /audit in any Claude conversation, answer a few guiding questions, and then receive the audit.</p>


Disclosure: I’m the co-founder of Optmyzr. I’ll use one of our open-source skills as the example below, but the frameworks here apply to anything you install or build.



If you’ve used Claude, ChatGPT, or Gemini for marketing work in the last six months, you’ve probably hit the same wall I have. The chat is great until you need the same thing done the same way every week. Then you’re back to copying a prompt template into a fresh window, hoping you didn’t forget a step, wondering why a tool this powerful still feels this manual.
Skills are what bring that wall down.
I’ve written before about skills as scalable systems for PPC and why agents are useless without access to your marketing data. This piece zooms out.
What is a skill, actually? 
Where do you find them? 
How do they work across the three big AI platforms? 
But perhaps most importantly for agency owners: how do you take an existing skill and brand it as your own?
What are ‘skills’ in AI
A skill is a small bundle of files that teaches an AI assistant how to do one specific job well, every time.
In Claude, a skill is literally a folder containing a SKILL.md file with instructions, alongside optional code scripts and reference files the AI can process. Install the folder once, and from then on, when a task matches the skill, the assistant loads the playbook and follows it.
It’s the difference between telling a new hire “audit this account” and handing them your agency’s documented audit process. The output gets much more consistent.
While the concept is universal, implementation varies by platform. 
Claude offers the most seamless experience, allowing you to install and use skills directly within the interface. 
ChatGPT makes similar capabilities available though generally only on paid Business or Enterprise plans. 
Gemini remains the most developer-focused, often requiring the Gemini CLI or specialized environments, which makes it less accessible for the average marketer. 
Because of its ease of use, I mostly use skills with Claude, and that’s where we’ll focus.
Where to find prebuilt skills for PPC
Most account managers prefer copy-pasting scripts over writing their own. They’ll also like grabbing skills someone else already built. But finding them can be tricky. There’s no single App Store for skills, and most of the good ones are on GitHub.
For Claude, the Anthropic team ships official skills for working with things like PDFs, and Microsoft Office programs. Beyond that, you’ll find growing collections on GitHub from individual developers and software vendors. 
A lot of companies are publishing their own. Ours live at github.com/optmyzr-skills.
Optmyzr’s free Google Ads audit skill. Screenshot by author of Github.com. May 2026



A practical rule I’ve landed on: a skill is only as trustworthy as the team that built it. A skill from a known software vendor with its methodology is different from a one-off prompt repackaged as a skill by someone you’ve never heard of.
Ensuring skill usage is consistent across your organization
This is where it gets interesting for agencies and in-house teams.
On a solo plan, you install a skill in your own account and you’re the only one using it. Fine for a freelancer. Painful for a team, because everyone has to install everything separately, and versions drift the moment one person updates and another doesn’t.
On Team and Enterprise plans, an admin can deploy skills across the whole organization. Claude has org-level skill management on Claude for Work and Enterprise. 
The practical benefit is that, with a five-person PPC team, you install a shared audit skill at the org level once, and every account manager gets the same version on day one. When you improve it, everyone gets the improvement automatically. No more “which version are you running” on team calls.
How to install a skill in Claude. Screenshot by author of claude.ai. May 2026



I think of it like the moment ad scripts stopped living in each individual ad account and moved to Enhanced Scripts from Optmyzr, which lets advertisers deploy a generic script code to all accounts and shifts script versioning and settings into a centralized management system maintained by Optmyzr. Easy, maintainable, and scalable; all things that matter a lot to account teams who promise a standard of quality to their stakeholders.
The hidden white-labeling engine: Why forkable skills are an agency’s best friend
Here’s the part that should perk up agency owners. Most well-built skills are folders, and the open-source ones live on GitHub. Which means you can fork them, edit them, brand them, and use your modified version with your own clients.
Let me walk you through an example for a 15-person agency. I find an open-source Google Ads audit skill (we’ll get to the one I’m thinking of in a second). The default report it generates has “Google Ads Audit” at the top in plain text. Useful, but generic and probably not shareable with the client.
What I can do in about an hour: 
Clone or download the skill folder. 
Open SKILL.md and edit the report-generation instructions to swap in my agency’s name, reference my logo, and use my brand colors. 
Drop my logo into the skill folder so the assistant can use it when generating PDFs or HTML reports. 
Add or remove checks based on what I actually care about — if I run only ecommerce accounts, I can tell the skill to weight Performance Max, Merchant Center, and feed health more heavily. 
Repackage and install for my team.
What comes out the other side is a branded, agency-specific audit tool that produces client-ready PDFs with my name on them. I didn’t have to build the underlying methodology. The original author already did that. I just added the last 10% that makes the output feel like mine.
Scripts were powerful because you could tweak them. Skills have the same power. Agencies are no longer capped by what software vendors choose to white-label. If a skill is open source, you can white-label it yourself in an afternoon.
A worked example: the Google Ads Audit skill
Since I keep alluding to it: we recently released a free, open-source Google Ads audit skill at github.com/optmyzr-skills/google-ads-audit. Apache 2.0 license, no Optmyzr login required.
A sample of the audit score the Optmyzr audit skill produces. Screenshot by author, May 2026.



Briefly, what it does. It runs through 14 categories and roughly 42 best-practice checks: 
Account settings
Conversion tracking
Campaign structure
Performance Max
Budgets
Bidding
Targeting
Audiences
Keywords
Quality Score
Search terms 
RSAs
Extensions
Landing pages
Industry benchmarks
Competitor analysis
It asks three calibration questions at the start (primary goal, target CPA or ROAS, account maturity) so the scoring matches the kind of account it’s looking at. 
If you don’t want to connect anything, there’s a four-paste flow: pull four CSVs from the Google Ads UI, paste them into Claude, and the skill runs the diagnostic. The output is a top-5 findings list with monthly dollar impact, an A/B/C grade with per-category breakdowns, a 7-day action plan, and a wasted-spend estimate.
A sample of the next steps the Optmyzr audit skill suggests to address key shortcomings of a Google Ads account. Screenshot by author, May 2026.



All the principles I described above apply to it. You can install it for free. You can deploy it across your agency. You can fork it, brand it, and have it generate client PDFs with your logo and your methodology framing. The Apache license explicitly allows that.
If you want it to also pull live account data instead of CSVs, run multi-account portfolio rollups, and trigger automated remediation, that’s where Optmyzr’s MCP server comes in — and that’s the paid layer. But the audit logic itself is yours to use, modify, and brand.
What to do with this
Pick one repeatable workflow your team does manually right now. Audits, search term reviews, ad copy generation, weekly report drafting — anything that runs the same shape every time is a candidate. Find or build a skill for it.
Then move it from individual installs to team deployment. That single change kills a surprising amount of version drift across a team.
Brand at least one skill as your own, even if you never ship the branded version to clients. Going through the fork-and-modify process once changes how you think about what counts as “tooling” for your agency. It’s lighter than you’d expect.
Skills are how a generic chatbot starts to behave like your team’s documented operating system. The agencies that get fluent with installing, deploying, and forking them over the next 12 months are going to operate noticeably differently than the ones still copying prompts into chat windows.
The Optmyzr audit is one example. There will be hundreds.
How to install the audit skill
There are two paths; pick the one that fits your team:
If you have someone technical on the team — an in-house dev, a power-user analyst, anyone comfortable with a GitHub URL — install it as a Claude plugin straight from the repo. One command. 
The skill stays in sync. When we ship a new check, tighten a benchmark, or add a category, your install picks it up automatically. For an agency running this across a team, this is the right path. Everyone runs the same version.
If you’re a marketer who just wants the thing to work — no GitHub, no command line — download the zip from the repo’s releases page and upload it via Settings > Capabilities > Skills inside Claude Desktop or claude.ai. Up and running in under a minute.
The tradeoff: it’s a snapshot. When we improve the skill, you’ll need to grab a new zip and re-upload. For a solo practitioner running monthly audits, that’s usually fine.
Either way, the repo is at github.com/optmyzr-skills/google-ads-audit. Once it’s installed, type /audit in any Claude conversation, answer a few guiding questions, and then receive the audit.

Learn how to install, deploy, and brand AI skills to turn generic chatbots into your agency’s custom, scalable operating system.

AI skills: The next layer of marketing automation by Optmyzr

<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>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.

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