<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="In Google Ads automation, everything is a signal in 2026" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Advertisers are questioning AI Max’s lack of granular landing page control compared to DSA, as Google confirms some gaps still exist.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="In Google Ads automation, everything is a signal in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Advertisers are starting to push back on gaps in AI Max capabilities — particularly around landing page control — as Google continues its shift away from legacy Dynamic Search Ads (DSA).</p>



<p><strong>What’s happening</strong>. In a LinkedIn exchange, digital marketing expert Gabriele Benedetti raised concerns about AI Max lacking the same level of URL-based targeting controls that DSA campaigns offered.</p>



<p>His point: DSA allowed advertisers to structure campaigns around website architecture — using categories, URL paths and page rules to guide where traffic lands. That level of control, he argued, is not yet fully replicated in AI Max.</p>



<p><strong>Why we care. </strong>For many advertisers — especially those managing large or structured websites — aligning campaign structure with site architecture is key to performance. Losing granular control over landing destinations could impact relevance, user experience and ultimately conversion rates.</p>



<p>This highlights a broader tension in Google Ads today: automation vs control.</p>



<p><strong>Google respon</strong>ds. Google Ads Liaison, Ginny Marvin responded, clarifying that AI Max does support several URL-based controls, including:</p>



<ul class="wp-block-list">
<li>URL rules and combinations</li>



<li>Page feeds with custom labels</li>



<li>URL inclusions at ad group level and exclusions at campaign level</li>
</ul>



<p>However, she acknowledged that not all DSA targeting rules are currently supported — such as “page contains” conditions.</p>



<p><strong>Between the lines</strong>. Google is not removing control entirely — but it is reshaping how that control works. Instead of granular rule-building, advertisers are being pushed toward structured inputs like page feeds and labels that AI can interpret.</p>



<p><strong>Migration reality check</strong>. For advertisers moving from DSA to AI Max, existing URL rules will carry over — but with limitations. Unsupported rules will remain active as read-only, meaning they’ll continue to function but cannot be edited.</p>



<p>That’s a temporary bridge, not a long-term solution.</p>



<p><strong>What’s next</strong>. Google says it plans to expand controls further, including bringing content and title-based exclusions to the account level later this year.</p>



<p>This would complement AI Max’s existing “inventory-aware” features, which already exclude out-of-stock items automatically.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1044" height="420" src="https://searchengineland.com/wp-content/seloads/2026/05/AI-Max-update.png" alt="" class="wp-image-476832" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AI-Max-update.png 1044w, https://searchengineland.com/wp-content/seloads/2026/05/AI-Max-update-768x309.png 768w" sizes="(max-width: 1044px) 100vw, 1044px" /></figure>



<p><strong>Bottom line</strong>. AI Max is evolving, but it’s not yet a full replacement for DSA when it comes to granular control — and advertisers are making that clear.</p>



<p><strong>Dig deeper. </strong><a href="https://www.linkedin.com/posts/gabriele-benedetti-0ab02815_googleads-dsa-search-share-7455567056537780224-cej-?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAJTV04Bw5M-1qqJtnImkpsEOFw9bHvfSSs">Full discussion on LinkedIn</a>. </p>


Advertisers are starting to push back on gaps in AI Max capabilities — particularly around landing page control — as Google continues its shift away from legacy Dynamic Search Ads (DSA).
What’s happening. In a LinkedIn exchange, digital marketing expert Gabriele Benedetti raised concerns about AI Max lacking the same level of URL-based targeting controls that DSA campaigns offered.
His point: DSA allowed advertisers to structure campaigns around website architecture — using categories, URL paths and page rules to guide where traffic lands. That level of control, he argued, is not yet fully replicated in AI Max.
Why we care. For many advertisers — especially those managing large or structured websites — aligning campaign structure with site architecture is key to performance. Losing granular control over landing destinations could impact relevance, user experience and ultimately conversion rates.
This highlights a broader tension in Google Ads today: automation vs control.
Google responds. Google Ads Liaison, Ginny Marvin responded, clarifying that AI Max does support several URL-based controls, including:
URL rules and combinations
Page feeds with custom labels
URL inclusions at ad group level and exclusions at campaign level
However, she acknowledged that not all DSA targeting rules are currently supported — such as “page contains” conditions.
Between the lines. Google is not removing control entirely — but it is reshaping how that control works. Instead of granular rule-building, advertisers are being pushed toward structured inputs like page feeds and labels that AI can interpret.
Migration reality check. For advertisers moving from DSA to AI Max, existing URL rules will carry over — but with limitations. Unsupported rules will remain active as read-only, meaning they’ll continue to function but cannot be edited.
That’s a temporary bridge, not a long-term solution.
What’s next. Google says it plans to expand controls further, including bringing content and title-based exclusions to the account level later this year.
This would complement AI Max’s existing “inventory-aware” features, which already exclude out-of-stock items automatically.




Bottom line. AI Max is evolving, but it’s not yet a full replacement for DSA when it comes to granular control — and advertisers are making that clear.
Dig deeper. Full discussion on LinkedIn.

Advertisers are questioning AI Max’s lack of granular landing page control compared to DSA, as Google confirms some gaps still exist.

AI Max vs DSA: Advertisers question control as Google responds

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>This is due to the new Google search penalty for back button hijacking.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is dropping the back button trigger for AdSense vignette ads on June 15, 2026 due to the new Google search penalty for <a href="https://searchengineland.com/google-search-to-penalize-back-button-hijacking-schemes-474167">back button hijacking</a>. Google <a href="https://support.google.com/adsense/answer/17048667">wrote</a>, &#8220;Starting June 15, 2026, the browser back button will no longer trigger a vignette ad.&#8221;</p>



<p><strong>What is changing. </strong>Google explained that the back button trigger will no longer work after June 15th. The &#8220;change will apply automatically for all publishers who have opted in to &#8220;Allow additional triggers for vignette ads&#8221; and will take effect across all supported browsers (including Chrome, Edge, and Opera).&#8221; Google added.</p>



<p>A Google spokesperson told me these same updates will apply to Ad Manager as well.</p>



<p><strong>Why the change. </strong>Google explained that the Google Search team &#8220;recently introduced <a href="https://developers.google.com/search/blog/2026/04/back-button-hijacking" target="_blank" rel="noopener">a new policy against &#8220;back button hijacking&#8221;</a> — a practice where websites or scripts interfere with a user&#8217;s ability to navigate back to their previous page. To ensure our publishers remain compliant with these latest user experience and search quality guidelines, we are removing the trigger that shows a vignette ad when the user navigates backward from the suite of <a href="https://support.google.com/adsense/answer/16531962#additional-triggers">vignette ad triggers</a>.&#8221;</p>



<p>This comes after the <a href="https://www.seroundtable.com/google-adsense-vignette-ads-back-button-penalty-41238.html">search community called this out</a> to Google and Google is making the right change here.  Of course, some publishers will not be happy because that trigger may have earned them a lot of money.</p>



<p><strong>Why we care. </strong>If you currently have the allow additional triggers for vignette ads setting on with AdSense, keep in mind, one of the triggers, the back button trigger, will be disabled on June 15th. It may impact your earnings, but it will ensure that your site does not get penalized by the <a href="https://searchengineland.com/google-search-to-penalize-back-button-hijacking-schemes-474167">back button hijacking</a> penalty. </p>


Google is dropping the back button trigger for AdSense vignette ads on June 15, 2026 due to the new Google search penalty for back button hijacking. Google wrote, “Starting June 15, 2026, the browser back button will no longer trigger a vignette ad.”
What is changing. Google explained that the back button trigger will no longer work after June 15th. The “change will apply automatically for all publishers who have opted in to “Allow additional triggers for vignette ads” and will take effect across all supported browsers (including Chrome, Edge, and Opera).” Google added.
A Google spokesperson told me these same updates will apply to Ad Manager as well.
Why the change. Google explained that the Google Search team “recently introduced a new policy against “back button hijacking” — a practice where websites or scripts interfere with a user’s ability to navigate back to their previous page. To ensure our publishers remain compliant with these latest user experience and search quality guidelines, we are removing the trigger that shows a vignette ad when the user navigates backward from the suite of vignette ad triggers.”
This comes after the search community called this out to Google and Google is making the right change here.  Of course, some publishers will not be happy because that trigger may have earned them a lot of money.
Why we care. If you currently have the allow additional triggers for vignette ads setting on with AdSense, keep in mind, one of the triggers, the back button trigger, will be disabled on June 15th. It may impact your earnings, but it will ensure that your site does not get penalized by the back button hijacking penalty.

This is due to the new Google search penalty for back button hijacking.

Google AdSense removes browser back button trigger for vignette ads

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg" class="attachment-large size-large wp-post-image" alt="When Google’s AI bidding breaks – and how to take control" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is aiming to help advertisers capture untapped demand and optimise spend in real time whilst reducing manual work.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg" class="attachment-large size-large wp-post-image" alt="When Google’s AI bidding breaks – and how to take control" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is rolling out new AI-driven bidding and budgeting features across Search, Shopping and Performance Max — aimed at helping advertisers capture more demand without increasing manual effort.</p>



<p><strong>What’s happening</strong>. Google is expanding its automation stack with updates like Journey-aware Bidding, Smart Bidding Exploration and demand-led budget pacing. Together, these changes are designed to help campaigns respond more dynamically to shifting consumer behaviour.</p>



<p>The focus: letting AI identify and act on opportunities advertisers may not see themselves.</p>



<p><strong>Why we care.</strong> These updates aim to capture more conversions without increasing manual work, using AI to find new demand and optimise spend in real time. By improving how bids respond to full-funnel signals and how budgets adapt to peak demand, campaigns can become more efficient and less reliant on constant adjustments. </p>



<p>Ultimately, it’s about getting more value from the same budget while staying competitive in a fast-changing search landscape.</p>



<p><strong>Smarter bidding gets more context</strong>. Journey-aware Bidding (beta) allows advertisers to feed more of the customer journey into optimisation, including non-biddable conversions. This gives Google AI a fuller picture of what leads to actual sales — not just initial actions like form fills.</p>



<p>At the same time, Smart Bidding Exploration is expanding beyond Search. Already delivering an average 27% increase in unique converting users, it will soon roll out to Performance Max and Shopping campaigns, helping advertisers tap into less obvious, incremental queries.</p>



<p><strong>Budgets that follow demand</strong>. On the budgeting side, Google is building on its campaign total budgets feature, which allows advertisers to set spend across a defined period instead of relying on daily limits.</p>



<p>The next step is demand-led pacing — where AI automatically adjusts spend based on real-time demand, increasing budgets on high-opportunity days and pulling back during slower periods, without exceeding overall limits.</p>



<p>Advertisers using total budgets have already seen a reported 66% reduction in manual budget adjustments.</p>



<p><strong>Why this is a big deal</strong>. Budget management has historically been one of the most manual parts of campaign optimisation. By automating pacing, Google is reducing the need for constant monitoring while aiming to improve efficiency.</p>



<p><strong>What to watch:</strong>



<ul class="wp-block-list">
<li>How much control advertisers are willing to give up for automation</li>



<li>Whether incremental gains from exploration translate into profitable growth</li>



<li>How transparent these systems remain as they scale</li>
</ul>



<p><strong>Bottom line</strong>. <a href="https://blog.google/products/ads-commerce/bidding-budgeting-google-marketing-live-2026">Google is directing advertisers to AI to handle both bidding and budgeting</a> — shifting the advertiser role from manual optimisation to guiding inputs and trusting the system to find growth.</p>


Google is rolling out new AI-driven bidding and budgeting features across Search, Shopping and Performance Max — aimed at helping advertisers capture more demand without increasing manual effort.
What’s happening. Google is expanding its automation stack with updates like Journey-aware Bidding, Smart Bidding Exploration and demand-led budget pacing. Together, these changes are designed to help campaigns respond more dynamically to shifting consumer behaviour.
The focus: letting AI identify and act on opportunities advertisers may not see themselves.
Why we care. These updates aim to capture more conversions without increasing manual work, using AI to find new demand and optimise spend in real time. By improving how bids respond to full-funnel signals and how budgets adapt to peak demand, campaigns can become more efficient and less reliant on constant adjustments. 
Ultimately, it’s about getting more value from the same budget while staying competitive in a fast-changing search landscape.
Smarter bidding gets more context. Journey-aware Bidding (beta) allows advertisers to feed more of the customer journey into optimisation, including non-biddable conversions. This gives Google AI a fuller picture of what leads to actual sales — not just initial actions like form fills.
At the same time, Smart Bidding Exploration is expanding beyond Search. Already delivering an average 27% increase in unique converting users, it will soon roll out to Performance Max and Shopping campaigns, helping advertisers tap into less obvious, incremental queries.
Budgets that follow demand. On the budgeting side, Google is building on its campaign total budgets feature, which allows advertisers to set spend across a defined period instead of relying on daily limits.
The next step is demand-led pacing — where AI automatically adjusts spend based on real-time demand, increasing budgets on high-opportunity days and pulling back during slower periods, without exceeding overall limits.
Advertisers using total budgets have already seen a reported 66% reduction in manual budget adjustments.
Why this is a big deal. Budget management has historically been one of the most manual parts of campaign optimisation. By automating pacing, Google is reducing the need for constant monitoring while aiming to improve efficiency.
What to watch:




How much control advertisers are willing to give up for automation
Whether incremental gains from exploration translate into profitable growth
How transparent these systems remain as they scale
Bottom line. Google is directing advertisers to AI to handle both bidding and budgeting — shifting the advertiser role from manual optimisation to guiding inputs and trusting the system to find growth.

Google is aiming to help advertisers capture untapped demand and optimise spend in real time whilst reducing manual work.

Google adds AI-powered bidding and demand-led budgeting to Search and Shopping

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png" class="attachment-large size-large wp-post-image" alt="5 JavaScript SEO lessons from top ecommerce sites" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>See how Chewy, Harrods, Under Armour, and more brands handle rendering, navigation, structured data, and scripts without hurting SEO.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png" class="attachment-large size-large wp-post-image" alt="5 JavaScript SEO lessons from top ecommerce sites" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>JavaScript SEO should be a solved problem by now. It isn&#8217;t.</p>



<p>Ecommerce sites keep hitting the same crawling, rendering, and indexing issues they were five years ago, now stacked on top of headless builds, AI-powered recommendations, and frameworks that can hide critical content from Google.</p>



<p>These top ecommerce players have figured out how to ship fast, modern JavaScript without sacrificing organic visibility. Here are five lessons worth stealing.</p>



<h2 class="wp-block-heading" id="h-1-chewy-uses-javascript-for-ux">1. Chewy uses JavaScript for UX</h2>



<p>Chewy is one of the largest online retailers of pet food and supplies in the U.S. They use Next.js, a React framework for building websites with built-in support for server rendering, static generation, and full-stack development features.</p>



<p>That means you can put important content in the initial HTML response <a href="https://searchengineland.com/no-javascript-fallbacks-474605">without relying on client-side JavaScript</a>.</p>



<p>Let&#8217;s look at a product page like the Benebone Wishbone Chew Toy.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1175" src="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-scaled.png" alt="Chewy product page" class="wp-image-476728" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-768x440.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-1536x881.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Navigate to <em>View Page Source</em> and you&#8217;ll see the product title, description, pricing, reviews, Q&amp;A, and breadcrumb navigation all present in the initial HTML. Googlebot can access it on the first pass, <a href="https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics#how-googlebot-processes-javascript" target="_blank" rel="noopener">without waiting for rendering</a>.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="197" src="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-scaled.png" alt="Chewy page source" class="wp-image-476730" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-768x74.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-1536x148.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>That&#8217;s important because if a web crawler like Googlebot encounters issues rendering your page, the important content can still be parsed on the first crawl. With the rise of AI chatbots, some of which still <a href="https://vercel.com/i/how-ai-is-changing-seo" target="_blank" rel="noopener">don&#8217;t render JavaScript</a>, this has become even more important.</p>



<p>Not everything needs to be in the initial HTML, though. Without client-side JavaScript, the page would feel static and clunky.</p>



<p>Take the &#8220;Compare Similar Items&#8221; carousel. It&#8217;s loaded client-side, primarily there for shoppers. The internal links could offer some SEO benefit, but they&#8217;re not critical for indexing this page the way the title, description, and pricing are. </p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1092" src="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-scaled.png" alt="Chewy similar items carousel" class="wp-image-476731" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-768x410.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-1536x819.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Chewy gets this balance right. The content that matters most for indexing is available on initial load. Client-side JavaScript enhances the experience rather than delivering the content that needs to be indexed.</p>



<p><a href="https://www.semrush.com/lp/semrush-one/en/?utm_campaign=ic_semrush_one&amp;utm_source=searchengineland.com&amp;utm_medium=overlay&amp;onboarding=off" target="_blank">
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        The SEO toolkit you know, plus the AI visibility data you need.
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<h2 class="wp-block-heading" id="h-2-myprotein-makes-navigation-crawlable">2. Myprotein makes navigation crawlable</h2>



<p>Myprotein sells supplements, nutrition products, and some fitness apparel.</p>



<p>Their site is built on Astro, a content-first framework using Islands Architecture to ship zero JavaScript by default while supporting components from React, Vue, or Svelte.</p>



<p>Myprotein&#8217;s navigation is the part worth studying. It&#8217;s an important SEO area for ecommerce sites, and they get it right.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="321" src="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-scaled.png" alt="Myprotein navigation" class="wp-image-476732" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-768x120.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-1536x241.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>View the source on any Myprotein page and the navigation links (categories, dropdown items, and footer links) are all in the initial HTML response. Astro makes this possible through its <a href="https://docs.astro.build/en/concepts/islands/" target="_blank" rel="noopener">island architecture</a>.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="194" src="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-scaled.png" alt="Myprotein source code" class="wp-image-476733" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-768x73.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-1536x146.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>The navigation ships as an interactive island, meaning Astro will hydrate it with JavaScript as soon as the browser is ready. But JavaScript makes the flyout menus interactive. It doesn&#8217;t create them.</p>



<p>These links are also proper <code>&lt;a&gt;</code> elements with href attributes, which is what crawlers like Googlebot need to <a href="https://developers.google.com/search/docs/crawling-indexing/links-crawlable" target="_blank" rel="noopener">discover and follow links</a>. Avoid using JavaScript click handlers to simulate navigation, such as:</p>



<p><code>&lt;div onclick="navigate(item.slug)"&gt;Clear Protein Drinks&lt;/div&gt;</code>



<p>A crawler won&#8217;t follow that. Use a standard anchor element instead:</p>



<p><code>&lt;a href="https://us.myprotein.com/c/nutrition/protein/clear-protein-drinks/"&gt;Clear Protein Drinks&lt;/a&gt;</code>



<p>Not every site gets this right. When navigation depends entirely on client-side rendering, there&#8217;s a window where it&#8217;s invisible or empty.</p>



<p>Googlebot <a href="https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics#how-googlebot-processes-javascript">processes JavaScript</a> in a separate rendering pass that can lag behind the initial crawl, which can mean delayed discovery of internal links critical for crawl efficiency and link equity distribution.</p>



<h2 class="wp-block-heading" id="h-3-harrods-embeds-structured-data-in-the-html">3. Harrods embeds structured data in the HTML</h2>



<p>Harrods is a luxury department store selling fashion, beauty, and homeware.</p>



<p>Their site is built on Nuxt, a Vue framework for building websites with built-in routing, server rendering, and static generation, plus an opinionated project structure.</p>



<p>Their structured data is delivered in the initial HTML response. View the source on any <a href="https://searchengineland.com/ecommerce-product-pages-ai-first-world-458629">product page</a> and you&#8217;ll find structured data inside a <code>&lt;script type="application/ld+json"&gt;</code> element. The Product schema includes the product name, images, description, brand, and an Offer with price, currency, availability, and seller.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="324" src="https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-scaled.png" alt="Harrods page source" class="wp-image-476734" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-768x121.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-1536x243.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>JSON-LD is the <a href="https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data#supported-formats" target="_blank" rel="noopener">format Google recommends</a> for structured data, and because it&#8217;s in the HTML response, Google can parse it on the first crawl pass without needing to render the page.</p>



<p>On JavaScript-powered sites, structured data can easily become a client-side dependency. If a framework fetches product data in the browser and generates JSON-LD from the response, that structured data only exists after JavaScript executes. The same is true for structured data injected <span>through <a href="https://developers.google.com/search/docs/appearance/structured-data/generate-structured-data-with-javascript#use-google-tag-manager" target="_blank">Google</a></span><a href="https://developers.google.com/search/docs/appearance/structured-data/generate-structured-data-with-javascript#use-google-tag-manager" target="_blank" rel="noopener"> Tag Manager</a>.</p>



<p>If markup is only added after the page loads, Google has to render the page to find it. <a href="https://developers.google.com/search/docs/appearance/structured-data/generate-structured-data-with-javascript">Google has noted</a> that dynamically generated Product markup can make Shopping crawls less frequent and less reliable, which matters when prices and availability change often.</p>



<p>By serving that structured data in the HTML directly, Harrods avoids this risk entirely.</p>



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<h2 class="wp-block-heading" id="h-4-under-armour-handles-faceted-navigation-with-javascript">4. Under Armour handles faceted navigation with JavaScript</h2>



<p>Under Armour is a global sportswear brand selling athletic apparel, footwear, and accessories. Their site is built on Next.js, the same React framework Chewy uses.</p>



<p>A good place to see their JavaScript SEO in action is on category pages, where filters need to feel fast and interactive for shoppers, and be crawler-friendly.</p>



<p>Let&#8217;s look at the men&#8217;s shoes category page. When you apply a filter, say, selecting size 10, the product grid updates instantly without a full page reload. That&#8217;s client-side JavaScript updating the grid.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1708" src="https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-scaled.png" alt="Under Armour porduct page" class="wp-image-476735" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-768x640.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-1536x1281.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>But the URL updates too. After selecting the filter, the URL becomes:</p>



<ul class="wp-block-list">
<li><code>https://www.underarmour.com/en-us/c/mens/shoes/?prefn1=size&amp;prefv1=10</code></li>
</ul>



<p>A shopper can copy that URL, send it to a friend, or bookmark it, and land right back on the same filtered view.</p>



<p>Notice what the URL isn&#8217;t:</p>



<ul class="wp-block-list">
<li>Not a hash fragment (#size=10), which doesn&#8217;t get sent to the server and is ignored by Google.</li>



<li>Not a mess of bracketed query strings (?filters[0][size]=10).</li>



<li>Not a dynamic route artifact like /shoes/[category]/ leaking into the live URL.</li>
</ul>



<p>It&#8217;s a clean, readable query string with named parameters.</p>



<p>Under Armour is using the Next.js router to update the URL as filters change. Under the hood, it wraps the browser&#8217;s <a href="https://developer.mozilla.org/en-US/docs/Web/API/History_API" target="_blank" rel="noopener">History API</a> and uses the <a href="https://developer.mozilla.org/en-US/docs/Web/API/History/pushState" target="_blank" rel="noopener">pushState() method</a> to update the address bar without a reload.</p>



<p>When someone visits that same URL directly, the page loads with the filter already applied.</p>



<h2 class="wp-block-heading" id="h-5-manors-golf-loads-third-party-scripts">5. Manors Golf loads third-party scripts</h2>



<p>Manors Golf sells golf apparel. Their site runs on Hydrogen, Shopify&#8217;s React-based framework for headless storefronts.</p>



<p>Hydrogen defers its own application scripts automatically since they load as ES modules. However, third-party scripts are the developer&#8217;s responsibility. On an ecommerce site, that can be a long list: reviews, chat, personalization, pixels, recommendations, payment scripts.</p>



<p>That matters for SEO in two ways. Render-blocking scripts hurt Core Web Vitals, most directly Largest Contentful Paint (LCP). They also give Googlebot more work to render the page, so it may get processed less reliably.</p>



<p>An external script (<code>&lt;script src="..."></code>) without async or defer <a href="https://shopify.dev/docs/storefronts/themes/tools/theme-check/checks/parser-blocking-javascript" target="_blank" rel="noopener">blocks HTML parsing</a>. <a href="https://developer.mozilla.org/en-US/docs/Web/HTML/Reference/Elements/script" target="_blank" rel="noopener">Async</a> fetches in the background and runs when ready. Defer waits until parsing finishes.</p>



<p>Manors loads external scripts from 12 third-party domains, including Klaviyo, TikTok, Microsoft Clarity, and Gorgias.</p>



<p>A look at the <em>Elements</em> panel shows them all loading with async:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="175" src="https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-scaled.png" alt="Manors async attribute" class="wp-image-476736" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-768x66.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-1536x131.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>By loading third-party scripts with async, Manors keeps them from blocking the initial render. That protects LCP and reduces the work Google’s Web Rendering Service (WRS) has to do.</p>



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<h2 class="wp-block-heading" id="h-the-balance-between-interactivity-and-crawlability">The balance between interactivity and crawlability</h2>



<p>The issue isn&#8217;t that you&#8217;re using JavaScript. It&#8217;s what you&#8217;re using it for. </p>



<p>Googlebot can process JavaScript, but it&#8217;s slower and less reliable than reading HTML. The more your core content, structure, and navigation depend on JavaScript, the more room there is for things to go wrong.</p>



<p>The sites in this article all use JavaScript to enhance the experience rather than deliver it. Do that, and you won&#8217;t have to choose between a good user experience and good SEO.</p>

JavaScript SEO should be a solved problem by now. It isn’t.
Ecommerce sites keep hitting the same crawling, rendering, and indexing issues they were five years ago, now stacked on top of headless builds, AI-powered recommendations, and frameworks that can hide critical content from Google.
These top ecommerce players have figured out how to ship fast, modern JavaScript without sacrificing organic visibility. Here are five lessons worth stealing.
1. Chewy uses JavaScript for UX
Chewy is one of the largest online retailers of pet food and supplies in the U.S. They use Next.js, a React framework for building websites with built-in support for server rendering, static generation, and full-stack development features.
That means you can put important content in the initial HTML response without relying on client-side JavaScript.
Let’s look at a product page like the Benebone Wishbone Chew Toy.




Navigate to View Page Source and you’ll see the product title, description, pricing, reviews, Q&A, and breadcrumb navigation all present in the initial HTML. Googlebot can access it on the first pass, without waiting for rendering.




That’s important because if a web crawler like Googlebot encounters issues rendering your page, the important content can still be parsed on the first crawl. With the rise of AI chatbots, some of which still don’t render JavaScript, this has become even more important.
Not everything needs to be in the initial HTML, though. Without client-side JavaScript, the page would feel static and clunky.
Take the “Compare Similar Items” carousel. It’s loaded client-side, primarily there for shoppers. The internal links could offer some SEO benefit, but they’re not critical for indexing this page the way the title, description, and pricing are. 




Chewy gets this balance right. The content that matters most for indexing is available on initial load. Client-side JavaScript enhances the experience rather than delivering the content that needs to be indexed.

  
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2. Myprotein makes navigation crawlable
Myprotein sells supplements, nutrition products, and some fitness apparel.
Their site is built on Astro, a content-first framework using Islands Architecture to ship zero JavaScript by default while supporting components from React, Vue, or Svelte.
Myprotein’s navigation is the part worth studying. It’s an important SEO area for ecommerce sites, and they get it right.




View the source on any Myprotein page and the navigation links (categories, dropdown items, and footer links) are all in the initial HTML response. Astro makes this possible through its island architecture.




The navigation ships as an interactive island, meaning Astro will hydrate it with JavaScript as soon as the browser is ready. But JavaScript makes the flyout menus interactive. It doesn’t create them.
These links are also proper <a> elements with href attributes, which is what crawlers like Googlebot need to discover and follow links. Avoid using JavaScript click handlers to simulate navigation, such as:
<div onclick="navigate(item.slug)">Clear Protein Drinks</div>



A crawler won’t follow that. Use a standard anchor element instead:
<a href="https://us.myprotein.com/c/nutrition/protein/clear-protein-drinks/">Clear Protein Drinks</a>



Not every site gets this right. When navigation depends entirely on client-side rendering, there’s a window where it’s invisible or empty.
Googlebot processes JavaScript in a separate rendering pass that can lag behind the initial crawl, which can mean delayed discovery of internal links critical for crawl efficiency and link equity distribution.
3. Harrods embeds structured data in the HTML
Harrods is a luxury department store selling fashion, beauty, and homeware.
Their site is built on Nuxt, a Vue framework for building websites with built-in routing, server rendering, and static generation, plus an opinionated project structure.
Their structured data is delivered in the initial HTML response. View the source on any product page and you’ll find structured data inside a <script type="application/ld+json"> element. The Product schema includes the product name, images, description, brand, and an Offer with price, currency, availability, and seller.




JSON-LD is the format Google recommends for structured data, and because it’s in the HTML response, Google can parse it on the first crawl pass without needing to render the page.
On JavaScript-powered sites, structured data can easily become a client-side dependency. If a framework fetches product data in the browser and generates JSON-LD from the response, that structured data only exists after JavaScript executes. The same is true for structured data injected through Google Tag Manager.
If markup is only added after the page loads, Google has to render the page to find it. Google has noted that dynamically generated Product markup can make Shopping crawls less frequent and less reliable, which matters when prices and availability change often.
By serving that structured data in the HTML directly, Harrods avoids this risk entirely.
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4. Under Armour handles faceted navigation with JavaScript
Under Armour is a global sportswear brand selling athletic apparel, footwear, and accessories. Their site is built on Next.js, the same React framework Chewy uses.
A good place to see their JavaScript SEO in action is on category pages, where filters need to feel fast and interactive for shoppers, and be crawler-friendly.
Let’s look at the men’s shoes category page. When you apply a filter, say, selecting size 10, the product grid updates instantly without a full page reload. That’s client-side JavaScript updating the grid.




But the URL updates too. After selecting the filter, the URL becomes:
https://www.underarmour.com/en-us/c/mens/shoes/?prefn1=size&prefv1=10
A shopper can copy that URL, send it to a friend, or bookmark it, and land right back on the same filtered view.
Notice what the URL isn’t:
Not a hash fragment (#size=10), which doesn’t get sent to the server and is ignored by Google.
Not a mess of bracketed query strings (?filters[0][size]=10).
Not a dynamic route artifact like /shoes/[category]/ leaking into the live URL.
It’s a clean, readable query string with named parameters.
Under Armour is using the Next.js router to update the URL as filters change. Under the hood, it wraps the browser’s History API and uses the pushState() method to update the address bar without a reload.
When someone visits that same URL directly, the page loads with the filter already applied.
5. Manors Golf loads third-party scripts
Manors Golf sells golf apparel. Their site runs on Hydrogen, Shopify’s React-based framework for headless storefronts.
Hydrogen defers its own application scripts automatically since they load as ES modules. However, third-party scripts are the developer’s responsibility. On an ecommerce site, that can be a long list: reviews, chat, personalization, pixels, recommendations, payment scripts.
That matters for SEO in two ways. Render-blocking scripts hurt Core Web Vitals, most directly Largest Contentful Paint (LCP). They also give Googlebot more work to render the page, so it may get processed less reliably.
An external script (<script src="...">) without async or defer blocks HTML parsing. Async fetches in the background and runs when ready. Defer waits until parsing finishes.
Manors loads external scripts from 12 third-party domains, including Klaviyo, TikTok, Microsoft Clarity, and Gorgias.
A look at the Elements panel shows them all loading with async:




By loading third-party scripts with async, Manors keeps them from blocking the initial render. That protects LCP and reduces the work Google’s Web Rendering Service (WRS) has to do.

  
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The balance between interactivity and crawlability
The issue isn’t that you’re using JavaScript. It’s what you’re using it for. 
Googlebot can process JavaScript, but it’s slower and less reliable than reading HTML. The more your core content, structure, and navigation depend on JavaScript, the more room there is for things to go wrong.
The sites in this article all use JavaScript to enhance the experience rather than deliver it. Do that, and you won’t have to choose between a good user experience and good SEO.

See how Chewy, Harrods, Under Armour, and more brands handle rendering, navigation, structured data, and scripts without hurting SEO.

5 JavaScript SEO lessons from top ecommerce sites

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026.png" class="attachment-large size-large wp-post-image" alt="8 GEO metrics to track in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search is creating new visibility gaps for brands. These metrics help you track presence, influence, and inclusion across platforms.

Search visibility no longer starts and ends with rankings. AI-driven search has changed where discovery happens — across Google, ChatGPT, Perplexity, and beyond. 
Generative engine optimization (GEO) is how brands adapt, shaping how they’re retrieved and represented inside those systems.
Traditional SEO metrics miss a growing share of that visibility. Pages are now summarized, excerpted, and cited in environments where clicks are optional, and attribution is fragmented. When an AI-generated summary appears, users click traditional search results far less often — in one analysis, just 8% of the time.
That creates a measurement gap. Assessing this gap is where GEO metrics come in.
What visibility means in generative search
GEO focuses on whether AI systems can find, understand, and select your content when generating answers. In generative search, visibility is more than about being indexed or ranked. Your content must be used — cited, summarized, or incorporated — into AI responses.
GEO builds on SEO and AEO, shifting the focus from where content ranks to how clearly it can be interpreted and trusted in context.
In practice, that means optimizing for:
Extractability: Can this be easily summarized?
Credibility: Is this a trustworthy source to cite?
Relevance: Does this directly resolve the query?
That’s where GEO metrics become useful.

  
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8 core GEO metrics brands need to track in 2026
GEO performance shows up across a distinct set of signals that reflect presence, usage, and downstream impact.
1. AI citation frequency
AI citation frequency measures how often your brand, website, content, or experts are cited in AI-generated answers.
This is one of the clearest GEO metrics because it shows whether generative systems consider your content useful enough to reference.
Track citation frequency across:
Google AI Overviews.
Google AI Mode.
Perplexity.
ChatGPT search.
Gemini.
Copilot.
Claude, where source visibility is available.
Industry-specific AI tools and assistants.
Citation frequency should be tracked at the topic level, not only the domain level. A SaaS company, for example, may want to know whether it’s cited for “customer onboarding software,” “product adoption metrics,” and “best tools for reducing churn” separately.
The goal is repeatable citation across high-value topics.
2. Share of Model Voice (SOMV)
Share of Model Voice measures how often your brand appears in AI-generated answers compared with competitors.
Traditional share of voice tells you how visible a brand is across search, media, or advertising. Share of Model Voice applies that idea to AI responses.
A simple way to calculate it:
SOMV = Brand appearances across a prompt set ÷ Total answers generated for that prompt set
For example:
You analyze 100 relevant prompts.
Your brand appears in 28 of the resulting AI-generated answers.
Your Share of Model Voice is 28%.
This metric is especially useful for competitive categories because AI answers often compress the consideration set. A user doesn’t see 10 blue links. They may see three recommended vendors, two cited articles, or one synthesized answer.
That’s why relative presence matters more than absolute visibility.
3. Answer inclusion rate
Answer inclusion rate measures how often your owned content is used to generate an AI answer, regardless of whether the user clicks.
This differs from citation frequency. A brand may be mentioned without its content being cited. And a page may be used as supporting material even when the brand is not the central recommendation.
Track inclusion across informational, comparison, and decision-stage prompts.
For example, a B2B SaaS company in the SEO or analytics space might track prompts like:
Informational: “What is generative engine optimization?”
Exploratory: “How should brands measure AI search visibility?”
Comparison: “SEO vs GEO vs AEO”
Category-level: “Best GEO tools for B2B SaaS”
Decision-stage: “How do I evaluate GEO platforms?”
This metric helps identify which content formats are easiest for AI systems to retrieve and summarize. 
In many cases, clear definitions, comparison tables, statistics pages, glossaries, and answer-first explainers perform better than broad thought leadership pages because they’re easier to extract and reuse.
4. Entity recognition and authority
Entity recognition measures how well AI systems understand who your brand is, what it does, and what topics it should be associated with.
This matters because generative systems don’t only match keywords. They interpret entities, relationships, topical authority, and corroborating signals.
Strong entity recognition means AI systems can accurately connect your brand to:
Your company name.
Products and services.
Founders or executives.
Authors and subject-matter experts.
Industry categories.
Locations.
Use cases.
Awards, partnerships, and third-party mentions.
Knowledge graph data.
Structured data.
Google’s guidance for AI features emphasizes that the same fundamentals still apply: make content accessible, maintain a strong page experience, and use structured data to help systems interpret what’s on the page.
In practice, inconsistencies across these signals make it harder for AI systems to reliably connect your brand to the right topics.
5. Sentiment in AI responses
Sentiment measures how AI systems describe your brand.
Tracking mentions isn’t enough. Brands also need to know whether AI-generated responses frame them as credible, outdated, expensive, risky, innovative, niche, enterprise-grade, beginner-friendly, or anything else.
You can monitor:
Positive, neutral, and negative descriptions.
Recurring adjectives or claims.
Incorrect comparisons.
Outdated product details.
Missing differentiators.
Reputation issues.
Hallucinated features or limitations.
This is where GEO overlaps with PR and brand management. AI-generated answers can shape perception before the user ever reaches your site.
6. Prompt coverage
Prompt coverage measures how many relevant prompts surface your brand. This is the GEO version of keyword coverage, but prompts are more conversational, specific, and intent-rich.
A strong prompt set should include:
Informational prompts.
Comparison prompts.
“Best” and “top” prompts.
Problem-aware prompts.
Solution-aware prompts.
Buyer-stage prompts.
Role-specific prompts.
Use-case prompts.
Local or industry-specific prompts.
Follow-up prompts.
For a cybersecurity company, “best cybersecurity platforms” is only part of the picture. Relevant prompts also look like:
“How do mid-market companies reduce phishing risk?”
“What tools help security teams manage vendor risk?”
“Compare managed detection and response providers.”
“What should a CISO look for in an incident response partner?”
Prompt coverage shows whether your brand is visible across the way people actually ask AI systems for help.
7. Content retrieval success rate
Content retrieval success rate measures how often AI systems pull from your owned content when answering relevant prompts. This is where it gets technical.
If your content isn’t crawlable, structured, fresh, or easy to parse, it may struggle to appear in generative outputs, regardless of subject-matter strength.
You should evaluate:
Crawlability.
Indexability.
Internal linking.
Page speed.
Schema markup.
Clear headings.
Answer-first formatting.
Author attribution.
Publication and update dates.
Canonical handling.
Robots.txt and AI crawler access rules.
Content freshness.
Source clarity.
Gaps in any of these areas reduce the likelihood that your content is retrieved and used — even when it’s the best answer available.
8. Conversion influence after AI interaction
Conversion influence measures how visibility in AI-generated outputs contributes to downstream business outcomes. That connection isn’t always direct — and it’s rarely cleanly attributed.
A user may see your brand in an AI answer, search your name later, visit directly, ask a colleague, or convert through a paid retargeting path.
Still, brands should track directional signals:
AI referral traffic.
Assisted conversions.
Branded search lift.
Direct traffic changes.
Demo or lead quality from AI-referred sessions.
Returning visitors after AI visibility spikes.
Sales conversations mentioning ChatGPT, Perplexity, Gemini, or AI Overviews.
Pipeline influenced by AI-discovery queries.
AI search visitors convert at a 23x higher rate than traditional organic search visitors, even though AI traffic volume was much smaller, according to Ahrefs.
That’s the measurement nuance: AI search may drive fewer sessions, but the sessions that do occur can be higher-intent.
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Tools and methods for tracking GEO metrics
GEO measurement is still in its early stages, and no single platform captures the full picture. Most brands will need a mix of automated tools, manual audits, analytics configuration, and competitive testing.
Emerging GEO analytics platforms
A growing set of tools — from established SEO platforms to GEO-native products — now track how brands appear across AI-driven search experiences.
For example:
Semrush AI Toolkit surfaces visibility trends tied to AI-driven search.
SE Ranking AI Visibility Tracker monitors brand presence across AI-generated outputs.
Profound focuses on AI citation frequency, sentiment, and competitive visibility.
Peec AI tracks brand presence and representation across AI systems.
The category is still evolving, but early tools give brands a way to move from assumptions to actual visibility data.
Prompt testing frameworks
Manual prompt testing is still useful, especially when building a baseline. Create a controlled prompt set by topic, funnel stage, persona, and geography. 
Run those prompts consistently across the same AI platforms. Capture:
Whether your brand appears.
Which competitors appear.
Which sources are cited.
How your brand is described.
Whether the answer is accurate.
Whether your owned content is cited.
Whether the answer changes across repeated tests.
Because AI answers can vary, single-prompt testing isn’t enough. Track patterns over time.
Analytics and logs
Use GA4, server logs, CRM fields, and referral data to identify traffic and conversions from AI platforms — particularly shifts in direct, branded, and assisted conversions.
Track known AI referrers, including ChatGPT, Perplexity, Gemini, Copilot, Claude, and other AI tools, where possible. Treat this as directional rather than complete, because many AI-influenced journeys show up as direct, branded search, or otherwise unattributed traffic.
Search Console and traditional SEO tools
Search Console still matters, even as clicks decline.
Impressions show whether content is being surfaced, while query data highlights where AI Overviews are absorbing demand, where branded search is increasing, and where content may need restructuring for answer inclusion.
Traditional SEO tools remain useful for technical health, content gaps, backlinks, keyword demand, and competitive research. GEO measurement builds on that foundation, tracking how content is surfaced in AI search.
How to build a GEO measurement framework
Start with a baseline. Choose 5-10 core topics you want AI systems to associate with your brand. For each, map prompts across the user journey. Then build a dashboard across four categories — and assign each to a clear action:
Visibility: Where do we show up?




AI citation frequency.
Share of Model Voice.
Prompt coverage.
Answer inclusion rate.
Accuracy and reputation: How are we represented?




Sentiment in AI responses.
Message consistency.
Misinformation or hallucination rate.
Competitive framing.
Technical and content: Can our content be used?




Content retrieval success rate.
Schema coverage.
Crawlability.
Freshness.
Entity consistency.
Business impact: Does it drive outcomes?




AI referral traffic.
Assisted conversions.
Branded search lift.
Direct traffic movement.
Lead quality.
Pipeline influenced by AI discovery.
Review these metrics together, not in isolation. Use them to decide what to update, expand, or deprioritize. Finally, connect the framework to business goals.
A publisher may prioritize citations and source inclusion. A B2B SaaS company may focus on category prompts and comparison visibility. An ecommerce brand may look at product recommendations, review sentiment, and visibility across discovery surfaces.
There’s no universal GEO dashboard — only the one that helps your team decide what to do next.

  
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Turning GEO metrics into action
GEO metrics are only useful if they change what teams do next. Define the topics you want to be known for, track how those topics show up across AI systems, and use that data to decide what to update, expand, or deprioritize.
Treat visibility as a feedback loop. If your brand isn’t appearing, refine the content. If it’s appearing inconsistently, strengthen the signals around it. If it’s showing up but misrepresented, correct the source.
Over time, the advantage goes to teams that act on these signals consistently — not just the ones that track them.

AI search is creating new visibility gaps for brands. These metrics help you track presence, influence, and inclusion across platforms.

8 GEO metrics to track in 2026

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO.png" class="attachment-large size-large wp-post-image" alt="How to use Google and LLM insights to improve international SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Turn Google and AI search signals into localized content structures that better match regional user behavior.

Turn Google and AI search signals into localized content structures that better match regional user behavior.

How to use Google and LLM insights to improve international SEO

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI-driven search draws on broader signals, exposing gaps in fragmented campaigns and favoring brands with a consistent, cross-channel presence.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507.jpg" alt="" class="wp-image-476065" srcset="https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/04/BrickMarketing-20260507-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p>Over the past few decades, digital marketing has settled into a stable system. While it spans SEO, content marketing, social media, and digital advertising, many programs have relied on a predictable core that didn’t always use every available channel.</p>



<p>This gave digital marketers a sense of predictability and comfort. For years, teams stuck with what worked and refined execution through the same familiar framework. AI search has disrupted that comfort and exposed our inconsistencies. To succeed with AI SEO, we need a <a href="https://www.brickmarketing.com/aio-geo-search" target="_blank" rel="noopener sponsored nofollow">much more comprehensive approach</a>.</p>



<h2 class="wp-block-heading" id="h-ai-seo-rewards-strategic-marketing-nbsp">AI SEO rewards strategic marketing&nbsp;</h2>



<p>Over the past 15 to 20 years, digital marketing settled into a predictable rhythm, with each channel playing a defined role.&nbsp;</p>



<p>Content marketing, social media, SEO, paid advertising, and email followed similar strategies with little variation. Little happened outside this structure, and many of us grew “lazy.”&nbsp;</p>



<p>The structure worked, so we let other strategies fall away.</p>



<p>The problem? It created a false sense of security. We should have been doing more all along, and those broader strategies are now driving real visibility in AI search.</p>



<p>AI has disrupted digital marketing in ways that weren’t obvious at first. It’s changed user search behavior and how brands are evaluated.&nbsp;</p>



<p>Traditional search relied on algorithms and a primary source. AI pulls from multiple inputs across many sources.</p>



<p>Those sources should already exist. They’re your marketing — the way you present your brand across platforms like social media, third-party directories, press releases, brand mentions, and more. In short, anything outside your website.</p>



<p>In this system, your website and the strategic marketing that supports it are just one part of the whole. It’s now one of many sources AI uses to understand your brand and offer. AI search reflects the strength of marketing across all these sources.</p>



<h2 class="wp-block-heading" id="h-visibility-is-not-limited-to-your-website">Visibility Is not limited to your website</h2>



<p>One of the biggest disruptions AI has caused is that the website is no longer central to your marketing strategy or visibility. It’s now part of a much larger ecosystem. You still need a strong website, as always, but you must account for how much broader the landscape has become with AI search.</p>



<p>While driving traffic to your website still matters, it’s no longer the only focus. The goal used to be maximizing website visibility — achieve that, and results would follow. That still works to a degree, but treating it as the only path to visibility is outdated.</p>



<p>AI pulls information from a wide range of sources — articles, brand mentions across platforms, third-party profiles, published content — and all of it shapes how it understands who you are and what you do.&nbsp;</p>



<p>Your website is just one part of this broader scope. If you focus only on your website, you limit AI’s ability to find you.</p>



<p>This is where most marketing programs fall short, especially those built before AI. To modernize, your brand must be visible across a much wider scope. </p>



<h2 class="wp-block-heading" id="h-ai-seo-requires-an-intentional-presence">AI SEO requires an intentional presence</h2>



<p>AI favors brands that show up online with intent. They’ve built a cohesive ecosystem across the wider internet.&nbsp;</p>



<p>A segmented marketing approach may have worked in the past, but it no longer has the same impact. We got away with it because when each channel performed well, it still felt effective and met our goals.</p>



<p>AI doesn’t allow this anymore. It favors brands with many connected signals, because it links them across the internet. It evaluates how your brand appears across these sources and looks for consistent messaging and expertise.&nbsp;</p>



<p>When these signals align, your AI visibility strengthens. When they’re scattered or your broader presence is weak, your AI visibility is weak.</p>



<p>This is why it’s important to develop a marketing strategy that accounts for this. A brand with a coordinated presence across the internet — across its website and other marketing channels — is what’s required today.&nbsp;</p>



<h2 class="wp-block-heading" id="h-lazy-marketing-strategies-are-exposed">Lazy marketing strategies are exposed</h2>



<p>This is the real issue with “lazy marketing.” We define it as sticking to the old approach — treating each channel separately and relying on the same tactics that have always worked. That approach may have delivered results before, but those days are gone.</p>



<p>At the time, this approach still delivered results. A strong SEO foundation consistently drove leads, and paid advertising offered similar predictability. These tactics worked so well that there was little need to go beyond them.</p>



<p>We need to go beyond it to keep up. Your brand needs to show up across multiple sources — that’s how AI finds you. If your competitors are already building their presence, you need to do the same or get left behind. They’ll take more space in AI-generated answers than you.</p>



<p>This means that if you have gaps in your marketing, you can’t hide them anymore. AI exposes these inconsistencies and forces you into the broader digital space.</p>



<h2 class="wp-block-heading" id="h-transition-into-the-era-of-ai-search-nbsp">Transition into the era of AI search&nbsp;</h2>



<p>Now is the time to move beyond the old model and adopt a new understanding of what works in digital marketing. The old approach no longer works on its own — it must be part of a broader system.</p>



<p>These are the strategies we should have been using all along: press releases, directory listings, and marketing beyond your own website.</p>



<p><a href="https://www.brickmarketing.com/blog/category/ai-marketing" target="_blank" rel="noopener sponsored nofollow">AI search rewards an all-encompassing marketing strategy</a> because that’s what works. Core channels like social media, SEO, content marketing, and paid advertising still matter, but they’re not enough on their own. </p>



<p>AI hasn’t changed the rules. It has enforced them.</p>



<p>This is what has always worked in marketing. The difference now is that you can’t get away with doing less.</p>


Over the past few decades, digital marketing has settled into a stable system. While it spans SEO, content marketing, social media, and digital advertising, many programs have relied on a predictable core that didn’t always use every available channel.
This gave digital marketers a sense of predictability and comfort. For years, teams stuck with what worked and refined execution through the same familiar framework. AI search has disrupted that comfort and exposed our inconsistencies. To succeed with AI SEO, we need a much more comprehensive approach.
AI SEO rewards strategic marketing 
Over the past 15 to 20 years, digital marketing settled into a predictable rhythm, with each channel playing a defined role. 
Content marketing, social media, SEO, paid advertising, and email followed similar strategies with little variation. Little happened outside this structure, and many of us grew “lazy.” 
The structure worked, so we let other strategies fall away.
The problem? It created a false sense of security. We should have been doing more all along, and those broader strategies are now driving real visibility in AI search.
AI has disrupted digital marketing in ways that weren’t obvious at first. It’s changed user search behavior and how brands are evaluated. 
Traditional search relied on algorithms and a primary source. AI pulls from multiple inputs across many sources.
Those sources should already exist. They’re your marketing — the way you present your brand across platforms like social media, third-party directories, press releases, brand mentions, and more. In short, anything outside your website.
In this system, your website and the strategic marketing that supports it are just one part of the whole. It’s now one of many sources AI uses to understand your brand and offer. AI search reflects the strength of marketing across all these sources.
Visibility Is not limited to your website
One of the biggest disruptions AI has caused is that the website is no longer central to your marketing strategy or visibility. It’s now part of a much larger ecosystem. You still need a strong website, as always, but you must account for how much broader the landscape has become with AI search.
While driving traffic to your website still matters, it’s no longer the only focus. The goal used to be maximizing website visibility — achieve that, and results would follow. That still works to a degree, but treating it as the only path to visibility is outdated.
AI pulls information from a wide range of sources — articles, brand mentions across platforms, third-party profiles, published content — and all of it shapes how it understands who you are and what you do. 
Your website is just one part of this broader scope. If you focus only on your website, you limit AI’s ability to find you.
This is where most marketing programs fall short, especially those built before AI. To modernize, your brand must be visible across a much wider scope. 
AI SEO requires an intentional presence
AI favors brands that show up online with intent. They’ve built a cohesive ecosystem across the wider internet. 
A segmented marketing approach may have worked in the past, but it no longer has the same impact. We got away with it because when each channel performed well, it still felt effective and met our goals.
AI doesn’t allow this anymore. It favors brands with many connected signals, because it links them across the internet. It evaluates how your brand appears across these sources and looks for consistent messaging and expertise. 
When these signals align, your AI visibility strengthens. When they’re scattered or your broader presence is weak, your AI visibility is weak.
This is why it’s important to develop a marketing strategy that accounts for this. A brand with a coordinated presence across the internet — across its website and other marketing channels — is what’s required today. 
Lazy marketing strategies are exposed
This is the real issue with “lazy marketing.” We define it as sticking to the old approach — treating each channel separately and relying on the same tactics that have always worked. That approach may have delivered results before, but those days are gone.
At the time, this approach still delivered results. A strong SEO foundation consistently drove leads, and paid advertising offered similar predictability. These tactics worked so well that there was little need to go beyond them.
We need to go beyond it to keep up. Your brand needs to show up across multiple sources — that’s how AI finds you. If your competitors are already building their presence, you need to do the same or get left behind. They’ll take more space in AI-generated answers than you.
This means that if you have gaps in your marketing, you can’t hide them anymore. AI exposes these inconsistencies and forces you into the broader digital space.
Transition into the era of AI search 
Now is the time to move beyond the old model and adopt a new understanding of what works in digital marketing. The old approach no longer works on its own — it must be part of a broader system.
These are the strategies we should have been using all along: press releases, directory listings, and marketing beyond your own website.
AI search rewards an all-encompassing marketing strategy because that’s what works. Core channels like social media, SEO, content marketing, and paid advertising still matter, but they’re not enough on their own. 
AI hasn’t changed the rules. It has enforced them.
This is what has always worked in marketing. The difference now is that you can’t get away with doing less.

AI-driven search draws on broader signals, exposing gaps in fragmented campaigns and favoring brands with a consistent, cross-channel presence.

AI SEO punishes lazy marketing strategies by Brick Marketing

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding.png" class="attachment-large size-large wp-post-image" alt="Microsoft Bing traditional search vs. grounding systems" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search is pushing indexes beyond relevance, with Bing focusing on facts, attribution, and confidence before answers are generated.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding.png" class="attachment-large size-large wp-post-image" alt="Microsoft Bing traditional search vs. grounding systems" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/bing-index-traditional-search-ai-grounding-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The search index is evolving from ranking pages to supporting AI-generated answers. In a <a href="https://www.linkedin.com/feed/update/urn:li:activity:7457826865551040512?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7457826865551040512%2C7457830105592131586%29&amp;replyUrn=urn%3Ali%3Acomment%3A%28activity%3A7457826865551040512%2C7457831075940048896%29&amp;dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287457830105592131586%2Curn%3Ali%3Aactivity%3A7457826865551040512%29&amp;dashReplyUrn=urn%3Ali%3Afsd_comment%3A%287457831075940048896%2Curn%3Ali%3Aactivity%3A7457826865551040512%29" target="_blank" rel="noopener">technical blog post</a> &#8220;on the evolving technical characteristics of the index,&#8221; published today, Microsoft Bing explained why AI search needs a different indexing system than traditional web search.</p>



<p><strong>Traditional search vs. grounding systems. </strong>Microsoft said traditional search can rely on users to self-correct, while AI systems need stronger evidence because they generate committed answers.</p>



<ul class="wp-block-list">
<li>Traditional search is built around documents. Users get ranked links, scan the results, and decide what to trust.</li>



<li>Grounding systems are built around supportable facts with clear sourcing. The AI uses that information to generate a combined answer, where mistakes can compound across sources and reasoning steps.</li>
</ul>



<p>They shared this table: </p>



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<p><strong>What&#8217;s different.</strong> Traditional ranking is optimized for relevance. Grounding must also assess whether information is accurate, up to date, clearly sourced, and sufficient to support an answer. That means AI indexes need to account for whether:</p>



<ul class="wp-block-list">
<li>A page’s meaning survives chunking and transformation.</li>



<li>The source is clearly identified.</li>



<li>The information is fresh enough to use.</li>



<li>Important facts are actually retrievable and groundable.</li>



<li>Grounding systems need to detect disagreements between sources before generating an answer.</li>
</ul>



<p><strong>Stale content. </strong>Stale content creates a different risk in AI answers, Microsoft said. In traditional search, it may hurt ranking quality. In grounding systems, it can directly generate a wrong answer.</p>



<p><strong>Contradictions.</strong> A search engine can rank one source above another and let users decide. Grounding systems must recognize conflicting evidence before turning it into a single answer, according to Microsoft.</p>



<p><strong>Retrieval is more complex.</strong> Search is usually a single interaction: query in, ranked results out. Microsoft said grounded AI systems may retrieve information repeatedly, refine based on earlier results, combine evidence, and reassess confidence before answering.</p>



<p><strong>How indexing quality is measured.</strong> Search quality has traditionally focused on ranking performance and user behavior. Grounding systems also need to measure factual fidelity, source quality, freshness, evidence strength, and conflict detection. The industry is still learning how to rigorously measure grounding quality, Microsoft said.</p>



<p><strong>Grounding doesn&#8217;t replace search.</strong> Grounding builds on existing search infrastructure while adding systems focused on evidence quality, attribution, and deciding when an AI system should avoid answering, Microsoft said.</p>



<p><strong>Why we care. </strong>For decades, search indexes helped determine which pages users should visit. Today, AI grounding determines which information supports an AI-generated answer. Microsoft described grounding as a new layer on top of traditional search, built for AI systems that need higher confidence in the information they use. That shift could push brands and publishers to focus more on creating information AI systems can confidently use.</p>



<p><strong>The blog post.</strong> <a href="https://blogs.bing.com/search/May-2026/Evolving-role-of-the-index-From-ranking-pages-to-supporting-answers" target="_blank" rel="noopener">Evolving role of the index: From ranking pages to supporting answers</a>


The search index is evolving from ranking pages to supporting AI-generated answers. In a technical blog post “on the evolving technical characteristics of the index,” published today, Microsoft Bing explained why AI search needs a different indexing system than traditional web search.
Traditional search vs. grounding systems. Microsoft said traditional search can rely on users to self-correct, while AI systems need stronger evidence because they generate committed answers.
Traditional search is built around documents. Users get ranked links, scan the results, and decide what to trust.
Grounding systems are built around supportable facts with clear sourcing. The AI uses that information to generate a combined answer, where mistakes can compound across sources and reasoning steps.
They shared this table: 

			
				




What’s different. Traditional ranking is optimized for relevance. Grounding must also assess whether information is accurate, up to date, clearly sourced, and sufficient to support an answer. That means AI indexes need to account for whether:
A page’s meaning survives chunking and transformation.
The source is clearly identified.
The information is fresh enough to use.
Important facts are actually retrievable and groundable.
Grounding systems need to detect disagreements between sources before generating an answer.
Stale content. Stale content creates a different risk in AI answers, Microsoft said. In traditional search, it may hurt ranking quality. In grounding systems, it can directly generate a wrong answer.
Contradictions. A search engine can rank one source above another and let users decide. Grounding systems must recognize conflicting evidence before turning it into a single answer, according to Microsoft.
Retrieval is more complex. Search is usually a single interaction: query in, ranked results out. Microsoft said grounded AI systems may retrieve information repeatedly, refine based on earlier results, combine evidence, and reassess confidence before answering.
How indexing quality is measured. Search quality has traditionally focused on ranking performance and user behavior. Grounding systems also need to measure factual fidelity, source quality, freshness, evidence strength, and conflict detection. The industry is still learning how to rigorously measure grounding quality, Microsoft said.
Grounding doesn’t replace search. Grounding builds on existing search infrastructure while adding systems focused on evidence quality, attribution, and deciding when an AI system should avoid answering, Microsoft said.
Why we care. For decades, search indexes helped determine which pages users should visit. Today, AI grounding determines which information supports an AI-generated answer. Microsoft described grounding as a new layer on top of traditional search, built for AI systems that need higher confidence in the information they use. That shift could push brands and publishers to focus more on creating information AI systems can confidently use.
The blog post. Evolving role of the index: From ranking pages to supporting answers

AI search is pushing indexes beyond relevance, with Bing focusing on facts, attribution, and confidence before answers are generated.

Microsoft: AI answers need a smarter search index

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Developers can now access unified paid and organic conversion data programmatically, though the feature is still in limited alpha.

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/12/broken-google-analytics-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google is expanding its Analytics Data API to include cross-channel conversion reporting — giving developers programmatic access to paid and organic performance data.</p>



<p><strong>What’s happening</strong>. The new feature, currently in alpha, allows Google Analytics and Google Ads users to pull conversion data across channels via the API — mirroring what’s available in the Conversion performance report in the Analytics interface.</p>



<p>This means developers can now access the same insights without relying on manual reporting.</p>



<p><strong>Why we care. </strong>As measurement becomes more complex, advertisers need unified views of performance across paid and organic channels. This update enables teams to automate reporting, integrate data into their own systems and build more advanced analysis workflows.</p>



<p>It’s particularly valuable for businesses managing multiple platforms and looking to centralise performance data.</p>



<p><strong>The caveat</strong>. This feature may not be available to every Google Analytics property yet. Google says it is actively working to expand access, and advertisers should check with their support teams to confirm eligibility.</p>



<p><strong>What to watch:</strong>



<ul class="wp-block-list">
<li>When the feature moves beyond alpha and becomes widely available</li>



<li>How advertisers use API access to build custom attribution models</li>



<li>Whether more reporting capabilities are added to the Data API</li>
</ul>



<p><strong>Bottom line</strong>. By <a href="https://support.google.com/analytics/answer/9164320?hl=en#05042026">bringing cross-channel conversion data into the API</a>, Google is giving advertisers and developers more control over how they access, analyse and act on performance data.</p>


Google is expanding its Analytics Data API to include cross-channel conversion reporting — giving developers programmatic access to paid and organic performance data.
What’s happening. The new feature, currently in alpha, allows Google Analytics and Google Ads users to pull conversion data across channels via the API — mirroring what’s available in the Conversion performance report in the Analytics interface.
This means developers can now access the same insights without relying on manual reporting.
Why we care. As measurement becomes more complex, advertisers need unified views of performance across paid and organic channels. This update enables teams to automate reporting, integrate data into their own systems and build more advanced analysis workflows.
It’s particularly valuable for businesses managing multiple platforms and looking to centralise performance data.
The caveat. This feature may not be available to every Google Analytics property yet. Google says it is actively working to expand access, and advertisers should check with their support teams to confirm eligibility.
What to watch:




When the feature moves beyond alpha and becomes widely available
How advertisers use API access to build custom attribution models
Whether more reporting capabilities are added to the Data API
Bottom line. By bringing cross-channel conversion data into the API, Google is giving advertisers and developers more control over how they access, analyse and act on performance data.

Developers can now access unified paid and organic conversion data programmatically, though the feature is still in limited alpha.

Google Analytics Data API adds cross-channel conversion reporting (alpha)

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026.png" class="attachment-large size-large wp-post-image" alt="The real strategy behind negative keywords in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From match types to automation, learn how to make smarter exclusion decisions that align with account goals.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026.png" class="attachment-large size-large wp-post-image" alt="The real strategy behind negative keywords in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/The-real-strategy-behind-negative-keywords-in-2026-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Negative keywords aren’t a checklist anymore. In 2026, they’re a series of strategic decisions — and how you make them shapes how the algorithm interprets your account.</p>



<p>If you’re still treating negatives like maintenance, you’re missing the point. Every exclusion is a signal: who you want to reach, what you’re willing to pay for, and how your campaigns should perform.</p>



<p>Here are six decisions that define modern negative keyword strategy — and why they matter more than ever.</p>



<h2 class="wp-block-heading" id="h-how-negative-keywords-shape-campaign-performance">How negative keywords shape campaign performance</h2>



<p>Negative keywords are how you sculpt a campaign so the right ad shows up for the right person. The user’s query should match the ad. The ad should match the landing page. That alignment is what creates a good experience for the person on the other side of the screen (a.k.a., your ideal client).</p>



<p>When that alignment breaks down, you waste budget. You also drag down click-through rate (CTR) and Quality Score, and push CPCs up. All of this is what ultimately makes the algorithm work against you in an ads account.</p>



<p>But many of us were never really taught how negatives fit into an overall account strategy. We were taught how to add them. There’s a big difference.</p>



<p>Now let’s get into the six strategic choices.</p>



<h2 class="wp-block-heading" id="h-1-how-aggressive-should-you-be-with-negatives">1. How aggressive should you be with negatives?</h2>



<p>This is the first decision an account manager needs to make, and most people skip it.</p>



<p>Are you scraping the bottom of the barrel every week, pulling out every search term that didn’t convert? Are you letting things slide so you can keep mining new keyword opportunities? Or are you somewhere in the middle, adding negatives mostly to show that you’re doing something?</p>



<p>There’s no universal right answer. But you do need to pick a level of aggression and back it up based on the account&#8217;s performance and goals.</p>



<p>A growth-focused account probably shouldn’t be aggressive. An efficiency-focused account probably should. A small-budget account that can’t afford to learn slowly often needs to be more aggressive than an enterprise account that can.</p>



<p>Pick the level. Defend the level when you’re working in the account and talking to your team. Then the rest of your choices become easier.</p>



<h2 class="wp-block-heading" id="h-2-how-to-use-match-types-for-negative-keywords">2. How to use match types for negative keywords</h2>



<p>Negatives don’t match the same way regular keywords do — broad, phrase, and exact — but most advertisers default to one match type without thinking about why.</p>



<p>Here’s how I think about it:</p>



<ul class="wp-block-list">
<li><strong>Negative exact match:</strong> Use this for strict removal. A specific long-tail variation that’s wasting budget, but you don’t want to nuke similar queries.</li>



<li><strong>Negative phrase match:</strong> Use this for groups of related queries you want gone. Competitor names, certain question phrases, and intent modifiers like “tutorial” or “review.”</li>



<li><strong>Negative broad match:</strong> Use this for words you want eliminated entirely — words like “cheap,” “dangerous,” or “free,” that signal a misaligned audience no matter how the rest of the query is constructed.</li>
</ul>



<p>This isn’t an either/or decision. A real negative keyword strategy uses all three match types intentionally, in different places, and for different reasons.</p>



<h2 class="wp-block-heading" id="h-3-when-should-you-add-negative-keywords">3. When should you add negative keywords?</h2>



<p>I’ve seen everything here. Some account managers add negatives on a strict weekly cadence, even when the data doesn&#8217;t call for it. Some only touch them when something stops converting. Some only review when a quarterly check-in forces them to. And some add them just to look busy on a status report.</p>



<p>My recommendation: don’t add a negative just because a keyword didn’t convert, especially when you’re trying to scale. You never know if it would’ve converted with a little more volume. Be careful, and tie the trigger back to your account’s goals.</p>



<ul class="wp-block-list">
<li>For accounts in growth mode, the trigger might be: “This query has cost more than 3x my target CPA with zero conversions over 90 days.”</li>



<li>For accounts focused on efficiency, the trigger might be: “This query cost more than $X without converting, period.”</li>
</ul>



<p>Both are valid. They just align to different goals in different accounts.</p>



<h2 class="wp-block-heading" id="h-4-what-time-frame-should-guide-your-decisions">4. What time frame should guide your decisions?</h2>



<p>How far back are you looking when you make the call to add something as a negative?</p>



<p>A 30-day window is aggressive. You’re cutting things off before they’ve had a real chance to convert. That’s appropriate for short-cycle ecommerce, fast-moving promotions, or tightly capped budgets. But this might not work for all accounts.</p>



<p>A 90-day window is balanced. Long enough for sales cycles to play out, short enough to actually act on. This is the default I recommend for most paid search accounts.</p>



<p>A 365-day window is conservative. You’re giving every query maximum runway. Appropriate for high-consideration B2B or anything with a long buying cycle.</p>



<p>Longer time frames give individual queries more chances to succeed. Shorter time frames protect your budget faster. Pick the time frame that matches the aggressiveness level you set in your top choice, and don’t make these decisions in isolation from each other.</p>



<h2 class="wp-block-heading" id="h-5-how-much-should-you-sculpt-your-campaigns">5. How much should you sculpt your campaigns?</h2>



<p>This is where the role of AI really starts to matter when it comes to negative keywords.</p>



<ul class="wp-block-list">
<li>How much control do you want over your account?</li>



<li>And maybe more importantly: how much do you trust the machine?</li>
</ul>



<p>Some advertisers embed competitor keywords as negatives in non-brand campaigns just to keep the targeting clean. Some account managers let those queries through because they’re converting, even if they technically “shouldn’t.” Some refuse to add negatives at the campaign level at all, because they trust the algorithm to figure out what should match where.</p>



<p>Consider that the machine and the algorithms have more information than we do. The platforms know what was searched before the query and more about user behavior. They know what was searched after. They know what session context the user is in. They can match queries to keywords in ways we can’t replicate manually.</p>



<p>But sculpting accounts is still how we communicate intent to that machine. If you don’t sculpt, the bidding algorithm doesn’t know what you actually want, and it just optimizes against the goals you handed it, which isn’t always the same thing as the outcomes you actually care about.</p>



<p>I lean toward more sculpting in 2026, not less. The algorithm has gotten better and will continue to improve, but so have the ways it can spend your budget on the wrong queries.</p>



<h2 class="wp-block-heading" id="h-6-how-should-you-manage-negatives-in-practice">6. How should you manage negatives in practice?</h2>



<p>This is the choice that didn’t exist five years ago.</p>



<p>In 2026, you have options that previous generations of account managers never had:</p>



<ul class="wp-block-list">
<li>A fully manual review with a spreadsheet and a search term report.</li>



<li>Google Ads’ built-in negative keyword suggestions.</li>



<li>A third-party tool that surfaces negatives semantically.</li>



<li>Pasting your search term report into a chat and letting AI flag candidates for you.</li>



<li>Delegating the entire task to a tool that adds negatives on autopilot, with no human in the loop.</li>
</ul>



<p>The question isn’t which one is best. The question is: how comfortable are you removing yourself from the loop? At what level do you want AI to handle this task?</p>



<p>For some accounts that are highly templated, low-stakes, and high-volume, maybe fully delegated AI negative keyword management is fine. For most accounts, I’d recommend a hybrid: AI surfaces candidates, and you approve them. The middle ground gives you speed without sacrificing oversight.</p>



<p>But the most important part is to make this decision intentionally, not by default, and to be able to defend the choice to key stakeholders. If you’re still doing all your negatives by hand in 2026, that should be a deliberate choice, not just “how I’ve always done it.”</p>



<h2 class="wp-block-heading" id="h-a-few-golden-rules-that-hold-up-in-any-era">A few golden rules that hold up in any era</h2>



<p>No matter which choices you make above, a handful of things still hold true:</p>



<ul class="wp-block-list">
<li><strong>Pull the search terms report regularly.</strong> The data won’t tell you what to decide, but it will tell you what to decide about. Make sure you look at the reports regularly, and remember no decision is still a decision.</li>



<li><strong>Keep your negatives updated as your campaigns evolve.</strong> The negatives that made sense for last quarter’s strategy may be the wrong ones now.</li>



<li><strong>Start with a few negatives and build from there.</strong> Being too negative too soon is genuinely harmful when an account is in a growth phase.</li>



<li><strong>Humans don’t search in straight lines.</strong> The query that looks irrelevant to your offer might be the final step before a conversion. Ad platforms try to teach us this in their documentation and training and reiterate that intent is messy, nonlinear, and often unpredictable. But as PPC practitioners, we still find ourselves resisting the idea that the system can make these judgment calls on our behalf.</li>
</ul>



<p><a href="https://searchengineland.com/author/paul-demott">Paul DeMott,</a> owner of Helium, sees this most clearly in high-spend accounts:</p>



<ul class="wp-block-list">
<li>“Most negative keyword lists are too exhaustive and haven’t been revisited in years… aggressive negative lists often hurt more than they help. You’re constraining the algorithm’s exploration on the exact accounts where it has the signal to make smart calls.”</li>
</ul>



<p>The risk today isn’t under-negating. It’s over-sculpting based on outdated assumptions.</p>



<p><a href="https://www.linkedin.com/in/brunelledigital/">Jordan Brunelle</a>, owner of Good Growth Marketing, points to a related issue: shutting down queries too quickly because they don’t match the product or service literally.</p>



<ul class="wp-block-list">
<li>“A lot of account managers are quick to negate keywords that don’t perfectly match their product/service… we must get into the head of the searcher and consider what their ultimate need is.”</li>
</ul>



<p>Intent isn’t always obvious at the query level. It often becomes clear only at the conversion level.</p>



<p><a href="https://www.linkedin.com/in/breannebartlett/">Breanne Bartlett</a>, a paid search consultant, frames it simply:</p>



<ul class="wp-block-list">
<li>“Most account managers still treat ‘vague’ queries as bad and overuse negatives to control them… the mistake isn’t allowing these queries, it’s blocking them before you’ve seen how they actually convert.”</li>
</ul>



<p>Top accounts aren’t restrictive. They’re responsive. They remove proven irrelevance, not theoretical inefficiency.</p>



<p>That tension between control and trust is showing up everywhere. As platforms introduce features like previewing the impact of negative keywords before applying them — something <a href="https://searchengineland.com/author/menachem-ani">Menachem Ani</a>, founder of a Google Premier Partner agency JXT, recently highlighted — the direction is clear: more data-driven decisions, less reactive cleanup.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="546" height="517" src="https://searchengineland.com/wp-content/seloads/2026/05/image-83.png" alt="" class="wp-image-476564"/></figure>
</div>


<p>At the same time, the core principle hasn’t changed. As <a href="https://www.linkedin.com/in/borisbeceric/">Boris Beceric</a>, a Google Ads consultant, reminded practitioners, efficiency starts with exclusion:</p>



<ul class="wp-block-list">
<li>“Most small budgets don’t fail because the offer is bad. They fail because the ads get shown for the wrong searches.”</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="542" height="513" src="https://searchengineland.com/wp-content/seloads/2026/05/image-84.png" alt="" class="wp-image-476565"/></figure>
</div>


<p>Beceric’s post on LinkedIn reframes negatives as a proactive efficiency tool, not just a cleanup task, and underscores that deciding what not to target is just as strategic as deciding what to include.</p>



<p>Together, these perspectives capture the evolution of negative keyword strategy: from maintenance to signal-shaping, from exclusion to intent communication. The smartest account managers aren’t just adding negatives — they’re auditing, previewing, and sculpting them with purpose and a larger goal in mind.</p>



<h2 class="wp-block-heading" id="h-what-your-negative-keyword-decisions-are-really-doing">What your negative keyword decisions are really doing</h2>



<p>Negative keywords have always been part of paid search. What’s changed in 2026 is how much depends on how you guide — or defer to — the machine.</p>



<p>The real risk is making decisions about negative keywords on autopilot. Stale lists, over-sculpting, and unexamined habits quietly shape how the algorithm interprets your intent.</p>



<p>Every choice ties back to one question: <strong>what are the goals of this ad account?</strong>



<p>A growth-focused account makes different choices than an efficiency-focused one. A small-budget account behaves differently from an enterprise one. A B2B account isn’t the same as high-velocity ecommerce. Even the “right” level of control shifts as Smart Bidding gains more signal.</p>



<p>Negative keywords aren’t a checklist in 2026. They’re a series of decisions that compound over time — and the account managers who make them intentionally, with fresh data, and clear goals, are the ones who deliver better results and can explain why.</p>


Negative keywords aren’t a checklist anymore. In 2026, they’re a series of strategic decisions — and how you make them shapes how the algorithm interprets your account.
If you’re still treating negatives like maintenance, you’re missing the point. Every exclusion is a signal: who you want to reach, what you’re willing to pay for, and how your campaigns should perform.
Here are six decisions that define modern negative keyword strategy — and why they matter more than ever.
How negative keywords shape campaign performance
Negative keywords are how you sculpt a campaign so the right ad shows up for the right person. The user’s query should match the ad. The ad should match the landing page. That alignment is what creates a good experience for the person on the other side of the screen (a.k.a., your ideal client).
When that alignment breaks down, you waste budget. You also drag down click-through rate (CTR) and Quality Score, and push CPCs up. All of this is what ultimately makes the algorithm work against you in an ads account.
But many of us were never really taught how negatives fit into an overall account strategy. We were taught how to add them. There’s a big difference.
Now let’s get into the six strategic choices.
1. How aggressive should you be with negatives?
This is the first decision an account manager needs to make, and most people skip it.
Are you scraping the bottom of the barrel every week, pulling out every search term that didn’t convert? Are you letting things slide so you can keep mining new keyword opportunities? Or are you somewhere in the middle, adding negatives mostly to show that you’re doing something?
There’s no universal right answer. But you do need to pick a level of aggression and back it up based on the account’s performance and goals.
A growth-focused account probably shouldn’t be aggressive. An efficiency-focused account probably should. A small-budget account that can’t afford to learn slowly often needs to be more aggressive than an enterprise account that can.
Pick the level. Defend the level when you’re working in the account and talking to your team. Then the rest of your choices become easier.
2. How to use match types for negative keywords
Negatives don’t match the same way regular keywords do — broad, phrase, and exact — but most advertisers default to one match type without thinking about why.
Here’s how I think about it:
Negative exact match: Use this for strict removal. A specific long-tail variation that’s wasting budget, but you don’t want to nuke similar queries.
Negative phrase match: Use this for groups of related queries you want gone. Competitor names, certain question phrases, and intent modifiers like “tutorial” or “review.”
Negative broad match: Use this for words you want eliminated entirely — words like “cheap,” “dangerous,” or “free,” that signal a misaligned audience no matter how the rest of the query is constructed.
This isn’t an either/or decision. A real negative keyword strategy uses all three match types intentionally, in different places, and for different reasons.
3. When should you add negative keywords?
I’ve seen everything here. Some account managers add negatives on a strict weekly cadence, even when the data doesn’t call for it. Some only touch them when something stops converting. Some only review when a quarterly check-in forces them to. And some add them just to look busy on a status report.
My recommendation: don’t add a negative just because a keyword didn’t convert, especially when you’re trying to scale. You never know if it would’ve converted with a little more volume. Be careful, and tie the trigger back to your account’s goals.
For accounts in growth mode, the trigger might be: “This query has cost more than 3x my target CPA with zero conversions over 90 days.”
For accounts focused on efficiency, the trigger might be: “This query cost more than $X without converting, period.”
Both are valid. They just align to different goals in different accounts.
4. What time frame should guide your decisions?
How far back are you looking when you make the call to add something as a negative?
A 30-day window is aggressive. You’re cutting things off before they’ve had a real chance to convert. That’s appropriate for short-cycle ecommerce, fast-moving promotions, or tightly capped budgets. But this might not work for all accounts.
A 90-day window is balanced. Long enough for sales cycles to play out, short enough to actually act on. This is the default I recommend for most paid search accounts.
A 365-day window is conservative. You’re giving every query maximum runway. Appropriate for high-consideration B2B or anything with a long buying cycle.
Longer time frames give individual queries more chances to succeed. Shorter time frames protect your budget faster. Pick the time frame that matches the aggressiveness level you set in your top choice, and don’t make these decisions in isolation from each other.
5. How much should you sculpt your campaigns?
This is where the role of AI really starts to matter when it comes to negative keywords.
How much control do you want over your account?
And maybe more importantly: how much do you trust the machine?
Some advertisers embed competitor keywords as negatives in non-brand campaigns just to keep the targeting clean. Some account managers let those queries through because they’re converting, even if they technically “shouldn’t.” Some refuse to add negatives at the campaign level at all, because they trust the algorithm to figure out what should match where.
Consider that the machine and the algorithms have more information than we do. The platforms know what was searched before the query and more about user behavior. They know what was searched after. They know what session context the user is in. They can match queries to keywords in ways we can’t replicate manually.
But sculpting accounts is still how we communicate intent to that machine. If you don’t sculpt, the bidding algorithm doesn’t know what you actually want, and it just optimizes against the goals you handed it, which isn’t always the same thing as the outcomes you actually care about.
I lean toward more sculpting in 2026, not less. The algorithm has gotten better and will continue to improve, but so have the ways it can spend your budget on the wrong queries.
6. How should you manage negatives in practice?
This is the choice that didn’t exist five years ago.
In 2026, you have options that previous generations of account managers never had:
A fully manual review with a spreadsheet and a search term report.
Google Ads’ built-in negative keyword suggestions.
A third-party tool that surfaces negatives semantically.
Pasting your search term report into a chat and letting AI flag candidates for you.
Delegating the entire task to a tool that adds negatives on autopilot, with no human in the loop.
The question isn’t which one is best. The question is: how comfortable are you removing yourself from the loop? At what level do you want AI to handle this task?
For some accounts that are highly templated, low-stakes, and high-volume, maybe fully delegated AI negative keyword management is fine. For most accounts, I’d recommend a hybrid: AI surfaces candidates, and you approve them. The middle ground gives you speed without sacrificing oversight.
But the most important part is to make this decision intentionally, not by default, and to be able to defend the choice to key stakeholders. If you’re still doing all your negatives by hand in 2026, that should be a deliberate choice, not just “how I’ve always done it.”
A few golden rules that hold up in any era
No matter which choices you make above, a handful of things still hold true:
Pull the search terms report regularly. The data won’t tell you what to decide, but it will tell you what to decide about. Make sure you look at the reports regularly, and remember no decision is still a decision.
Keep your negatives updated as your campaigns evolve. The negatives that made sense for last quarter’s strategy may be the wrong ones now.
Start with a few negatives and build from there. Being too negative too soon is genuinely harmful when an account is in a growth phase.
Humans don’t search in straight lines. The query that looks irrelevant to your offer might be the final step before a conversion. Ad platforms try to teach us this in their documentation and training and reiterate that intent is messy, nonlinear, and often unpredictable. But as PPC practitioners, we still find ourselves resisting the idea that the system can make these judgment calls on our behalf.
Paul DeMott, owner of Helium, sees this most clearly in high-spend accounts:
“Most negative keyword lists are too exhaustive and haven’t been revisited in years… aggressive negative lists often hurt more than they help. You’re constraining the algorithm’s exploration on the exact accounts where it has the signal to make smart calls.”
The risk today isn’t under-negating. It’s over-sculpting based on outdated assumptions.
Jordan Brunelle, owner of Good Growth Marketing, points to a related issue: shutting down queries too quickly because they don’t match the product or service literally.
“A lot of account managers are quick to negate keywords that don’t perfectly match their product/service… we must get into the head of the searcher and consider what their ultimate need is.”
Intent isn’t always obvious at the query level. It often becomes clear only at the conversion level.
Breanne Bartlett, a paid search consultant, frames it simply:
“Most account managers still treat ‘vague’ queries as bad and overuse negatives to control them… the mistake isn’t allowing these queries, it’s blocking them before you’ve seen how they actually convert.”
Top accounts aren’t restrictive. They’re responsive. They remove proven irrelevance, not theoretical inefficiency.
That tension between control and trust is showing up everywhere. As platforms introduce features like previewing the impact of negative keywords before applying them — something Menachem Ani, founder of a Google Premier Partner agency JXT, recently highlighted — the direction is clear: more data-driven decisions, less reactive cleanup.




At the same time, the core principle hasn’t changed. As Boris Beceric, a Google Ads consultant, reminded practitioners, efficiency starts with exclusion:
“Most small budgets don’t fail because the offer is bad. They fail because the ads get shown for the wrong searches.”




Beceric’s post on LinkedIn reframes negatives as a proactive efficiency tool, not just a cleanup task, and underscores that deciding what not to target is just as strategic as deciding what to include.
Together, these perspectives capture the evolution of negative keyword strategy: from maintenance to signal-shaping, from exclusion to intent communication. The smartest account managers aren’t just adding negatives — they’re auditing, previewing, and sculpting them with purpose and a larger goal in mind.
What your negative keyword decisions are really doing
Negative keywords have always been part of paid search. What’s changed in 2026 is how much depends on how you guide — or defer to — the machine.
The real risk is making decisions about negative keywords on autopilot. Stale lists, over-sculpting, and unexamined habits quietly shape how the algorithm interprets your intent.
Every choice ties back to one question: what are the goals of this ad account?



A growth-focused account makes different choices than an efficiency-focused one. A small-budget account behaves differently from an enterprise one. A B2B account isn’t the same as high-velocity ecommerce. Even the “right” level of control shifts as Smart Bidding gains more signal.
Negative keywords aren’t a checklist in 2026. They’re a series of decisions that compound over time — and the account managers who make them intentionally, with fresh data, and clear goals, are the ones who deliver better results and can explain why.

From match types to automation, learn how to make smarter exclusion decisions that align with account goals.

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