<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations.png" class="attachment-large size-large wp-post-image" alt="ChatGPT thinking citations" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Only 25.6% source overlap between minimal and high reasoning across 100 prompts and 20 buyer journeys, according to Semrush and Kevin Indig.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations.png" class="attachment-large size-large wp-post-image" alt="ChatGPT thinking citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-thinking-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>ChatGPT’s high-reasoning mode acted like a different search surface for brand visibility in a Semrush analysis with Kevin Indig. It cited different domains than in minimal reasoning mode and ran nearly five times as many web searches.</p>



<p><strong>By the numbers. </strong>Only 25.6% of cited domains overlapped between minimal and high reasoning for the same prompts. </p>



<ul class="wp-block-list">
<li>Nearly three in four sources changed when ChatGPT shifted from Instant-style answers to Thinking-style answers.</li>
</ul>



<p><strong>Thinking mode used more sources. </strong>Citation rates rose from 50% in minimal reasoning to 68% in high reasoning. Cited answers also used more sources, rising from 2.6 to 4.5 citations per response. High reasoning ran 1,130 web searches across the test set, versus 245 for minimal reasoning.</p>



<p><strong>Reddit lost ground</strong>. Reddit’s citation share fell from 15% to 7% when high reasoning was on. User-generated content and review sites dropped from 14.3% to 6%.</p>



<ul class="wp-block-list">
<li>Government and academic sources rose from 1.9% to 8.8%. </li>



<li>Official documentation and support pages grew from 12.4% to 17.5%.</li>
</ul>



<p><strong>Comparison prompts drove searches</strong>. At the comparison stage, high reasoning averaged 24 sub-queries per prompt, versus 5.5 for minimal reasoning. Average citations also peaked there, at 9.8 per high-reasoning response versus 5.8 for minimal reasoning.</p>



<ul class="wp-block-list">
<li>For example, a CRM comparison may trigger separate searches for pricing, integrations, security, support pages, and documentation before ChatGPT forms its answer.</li>
</ul>



<p><strong>Early citations lasted longer</strong>. High reasoning was more likely to carry a brand from early research into later buying questions.</p>



<ul class="wp-block-list">
<li>In four of the 20 journeys tested, a brand cited at the problem stage still appeared at the selection stage. Minimal reasoning showed no full-journey persistence (whether a brand cited at the Problem stage survived to the Selection stage of the same journey).</li>



<li>High reasoning also reused the same domains more often within a single answer. The same domain appeared multiple times in 51 of 100 high-reasoning responses, versus 26 of 100 minimal-reasoning responses.</li>
</ul>



<p><strong>Finance saw the biggest jump</strong>. The citation lift varied by category:</p>



<ul class="wp-block-list">
<li>Finance had the largest increase, with citation rates rising 28 percentage points in high reasoning. </li>



<li>Health and lifestyle rose 24 points.</li>



<li>B2B SaaS gained 16 points.</li>



<li>Consumer tech barely moved, rising only 4 points. </li>



<li>Even though high reasoning ran more sub-queries for consumer tech prompts than for any other category, it often landed on the same brands and sources as minimal reasoning.</li>
</ul>



<p><strong>Why we care. </strong>Your content may appear in fast ChatGPT answers but disappear when users ask more complex questions. Visibility depends on whether your pages, documentation, and third-party references can surface across the smaller searches ChatGPT runs before it answers.</p>



<p><strong>About the data. </strong>Semrush and Indig tested 100 prompts across 20 buyer journeys in B2B SaaS, finance, consumer tech, and health and lifestyle. Each prompt ran once in minimal reasoning and once in high reasoning. The analysis tracked citation rate, cited sources, and fan-out queries.</p>



<p><strong>The report.</strong> <a href="https://www.semrush.com/blog/chatgpt-reasoning-ai-visibility/" target="_blank" rel="noopener">Only 25% of cited sources overlap between ChatGPT&#8217;s different reasoning modes [Study]</a>


ChatGPT’s high-reasoning mode acted like a different search surface for brand visibility in a Semrush analysis with Kevin Indig. It cited different domains than in minimal reasoning mode and ran nearly five times as many web searches.
By the numbers. Only 25.6% of cited domains overlapped between minimal and high reasoning for the same prompts. 
Nearly three in four sources changed when ChatGPT shifted from Instant-style answers to Thinking-style answers.
Thinking mode used more sources. Citation rates rose from 50% in minimal reasoning to 68% in high reasoning. Cited answers also used more sources, rising from 2.6 to 4.5 citations per response. High reasoning ran 1,130 web searches across the test set, versus 245 for minimal reasoning.
Reddit lost ground. Reddit’s citation share fell from 15% to 7% when high reasoning was on. User-generated content and review sites dropped from 14.3% to 6%.
Government and academic sources rose from 1.9% to 8.8%. 
Official documentation and support pages grew from 12.4% to 17.5%.
Comparison prompts drove searches. At the comparison stage, high reasoning averaged 24 sub-queries per prompt, versus 5.5 for minimal reasoning. Average citations also peaked there, at 9.8 per high-reasoning response versus 5.8 for minimal reasoning.
For example, a CRM comparison may trigger separate searches for pricing, integrations, security, support pages, and documentation before ChatGPT forms its answer.
Early citations lasted longer. High reasoning was more likely to carry a brand from early research into later buying questions.
In four of the 20 journeys tested, a brand cited at the problem stage still appeared at the selection stage. Minimal reasoning showed no full-journey persistence (whether a brand cited at the Problem stage survived to the Selection stage of the same journey).
High reasoning also reused the same domains more often within a single answer. The same domain appeared multiple times in 51 of 100 high-reasoning responses, versus 26 of 100 minimal-reasoning responses.
Finance saw the biggest jump. The citation lift varied by category:
Finance had the largest increase, with citation rates rising 28 percentage points in high reasoning. 
Health and lifestyle rose 24 points.
B2B SaaS gained 16 points.
Consumer tech barely moved, rising only 4 points. 
Even though high reasoning ran more sub-queries for consumer tech prompts than for any other category, it often landed on the same brands and sources as minimal reasoning.
Why we care. Your content may appear in fast ChatGPT answers but disappear when users ask more complex questions. Visibility depends on whether your pages, documentation, and third-party references can surface across the smaller searches ChatGPT runs before it answers.
About the data. Semrush and Indig tested 100 prompts across 20 buyer journeys in B2B SaaS, finance, consumer tech, and health and lifestyle. Each prompt ran once in minimal reasoning and once in high reasoning. The analysis tracked citation rate, cited sources, and fan-out queries.
The report. Only 25% of cited sources overlap between ChatGPT’s different reasoning modes [Study]

Only 25.6% source overlap between minimal and high reasoning across 100 prompts and 20 buyer journeys, according to Semrush and Kevin Indig.

ChatGPT Thinking mode changes which brands get cited

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-800x450.png" class="attachment-large size-large wp-post-image" alt="How to use Performance Max for any type of business" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google's Channel Diagnostics for PMax, helps advertisers identify missing or disapproved assets that could limit performance.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-800x450.png" class="attachment-large size-large wp-post-image" alt="How to use Performance Max for any type of business" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2024/06/How-to-use-Performance-Max-for-any-type-of-business.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google is introducing a new Channel Diagnostics feature for Performance Max, giving advertisers a centralised view of asset issues that may be limiting campaign delivery across Google&#8217;s channels.</p>



<p><strong>What&#8217;s happening</strong>. The new Channel Diagnostics section is now available within Insights &amp; Reports > Channel Performance in Performance Max campaigns.</p>



<p>Advertisers can:</p>



<ul class="wp-block-list">
<li>View diagnostics across all Performance Max channels or drill into individual channels.</li>



<li>Identify missing or disapproved assets affecting campaign eligibility.</li>



<li>See exactly which asset types—such as headlines, descriptions, or images—need attention.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/07/pmax-channel-diagnostics.jpg" alt="" class="wp-image-481446" srcset="https://searchengineland.com/wp-content/seloads/2026/07/pmax-channel-diagnostics.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/07/pmax-channel-diagnostics-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/pmax-channel-diagnostics-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p><strong>Why we care</strong>. Performance Max is often criticised for offering limited visibility into campaign issues. The new diagnostics tool makes it easier for advertisers to spot missing creative assets that could be preventing campaigns from serving across Search, Display, YouTube, Discover, Gmail, and Maps.</p>



<p><strong>Between the lines</strong>. By surfacing channel-specific asset gaps in one place, Google is giving advertisers more actionable insights without requiring them to manually audit asset groups.</p>



<p><strong>The bottom line</strong>. Channel Diagnostics gives Performance Max advertisers a quicker way to identify and fix missing assets, helping ensure campaigns remain eligible to serve across Google&#8217;s full range of inventory.</p>



<p><strong>Spotted.</strong> This update was spotted by Google Ads Specialist who shared the update on <a href="https://www.linkedin.com/feed/update/urn:li:activity:7477277181820272641/">LinkedIn</a>. </p>


Google is introducing a new Channel Diagnostics feature for Performance Max, giving advertisers a centralised view of asset issues that may be limiting campaign delivery across Google’s channels.
What’s happening. The new Channel Diagnostics section is now available within Insights & Reports > Channel Performance in Performance Max campaigns.
Advertisers can:
View diagnostics across all Performance Max channels or drill into individual channels.
Identify missing or disapproved assets affecting campaign eligibility.
See exactly which asset types—such as headlines, descriptions, or images—need attention.




Why we care. Performance Max is often criticised for offering limited visibility into campaign issues. The new diagnostics tool makes it easier for advertisers to spot missing creative assets that could be preventing campaigns from serving across Search, Display, YouTube, Discover, Gmail, and Maps.
Between the lines. By surfacing channel-specific asset gaps in one place, Google is giving advertisers more actionable insights without requiring them to manually audit asset groups.
The bottom line. Channel Diagnostics gives Performance Max advertisers a quicker way to identify and fix missing assets, helping ensure campaigns remain eligible to serve across Google’s full range of inventory.
Spotted. This update was spotted by Google Ads Specialist who shared the update on LinkedIn.

Google's Channel Diagnostics for PMax, helps advertisers identify missing or disapproved assets that could limit performance.

Google adds Channel Diagnostics to Performance Max

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google will continue to support the open-source AMPhtml format, the company told me.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-rocket-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google has updated how it connects users to AMP pages from Search by sending them directly to publisher-hosted AMP pages. Previously, Google Search would take users to the cached pages displayed in an AMP viewer.</p>



<p>A Google spokesperson told Search Engine Land:</p>



<ul class="wp-block-list">
<li>&#8220;Starting today, we are updating how we connect users to AMP pages from Search, taking them directly to the AMP host pages.&#8221;</li>
</ul>



<p>Google added that rankings are unchanged but this should result in making it easier to create and maintain AMP pages, Google told us:</p>



<ul class="wp-block-list">
<li>&#8220;AMP content will continue to rank just like any other webpage, and this change will reduce maintenance efforts for publishers creating AMP content.&#8221;</li>
</ul>



<p><strong>No change in ranking. </strong>Google Search remains unchanged in terms how it ranks AMP pages within the search results.  Serving and ranking of AMP content in Google Search and Google Discover will remain the same, Google told us.</p>



<p><strong>Why the change. </strong>Google said that this change should make it easier for publishers, by allowing them to maintain simpler analytics management and tracking.  This should also make it easier for sites to create AMP pages and maintain those AMP pages going forward.</p>



<p>Google also told us that the company continue to support the open-source AMPhtml format. Google also posted this information <a href="https://developers.google.com/search/updates#updating-amp-documentation">over here</a>.</p>



<p>AMP has not been given <a href="https://searchengineland.com/amp-wont-be-required-for-googles-top-stories-section-335276">preferential treatment</a> for a while now, and honestly, it is surprising when you stumble on an AMP page these days.  In fact, this site <a href="https://searchengineland.com/why-were-turning-off-amp-pages-at-search-engine-land-376228">turned off AMP in 2021</a>.</p>



<p>Yea, it has been a long time since we covered AMP here but this is a change and it is important to document.</p>


Google has updated how it connects users to AMP pages from Search by sending them directly to publisher-hosted AMP pages. Previously, Google Search would take users to the cached pages displayed in an AMP viewer.
A Google spokesperson told Search Engine Land:
“Starting today, we are updating how we connect users to AMP pages from Search, taking them directly to the AMP host pages.”
Google added that rankings are unchanged but this should result in making it easier to create and maintain AMP pages, Google told us:
“AMP content will continue to rank just like any other webpage, and this change will reduce maintenance efforts for publishers creating AMP content.”
No change in ranking. Google Search remains unchanged in terms how it ranks AMP pages within the search results.  Serving and ranking of AMP content in Google Search and Google Discover will remain the same, Google told us.
Why the change. Google said that this change should make it easier for publishers, by allowing them to maintain simpler analytics management and tracking.  This should also make it easier for sites to create AMP pages and maintain those AMP pages going forward.
Google also told us that the company continue to support the open-source AMPhtml format. Google also posted this information over here.
AMP has not been given preferential treatment for a while now, and honestly, it is surprising when you stumble on an AMP page these days.  In fact, this site turned off AMP in 2021.
Yea, it has been a long time since we covered AMP here but this is a change and it is important to document.

Google will continue to support the open-source AMPhtml format, the company told me.

Google Search now sends searchers directly to publisher-hosted AMP pages

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/bruce-clay-in-memoriam.png" class="attachment-large size-large wp-post-image" alt="Bruce Clay in memoriam" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/bruce-clay-in-memoriam.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/bruce-clay-in-memoriam-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/bruce-clay-in-memoriam-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/bruce-clay-in-memoriam-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A longtime search leader’s final industry conversation explores AI, content structure and the evolving craft behind ranking well.

A longtime search leader’s final industry conversation explores AI, content structure and the evolving craft behind ranking well.

In memoriam: A tribute to Bruce Clay 

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future.png 1920w, https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is testing AI-generated summaries beneath Search ads, potentially giving its AI a larger role in how paid ads are presented

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future.png 1920w, https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/10/google-ai-mode-future-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google appears to be experimenting with AI-generated summaries inside sponsored search results, a move that could reshape how users interact with paid ads and how advertisers&#8217; messaging is presented.</p>



<p><strong>What&#8217;s happening</strong>. Some advertisers are seeing AI-generated summaries displayed directly beneath Google Ads descriptions in Search results. The summaries include a disclaimer stating: &#8220;Google AI responses are generated independently and can make mistakes, so double-check responses.&#8221;</p>



<p>The feature was first spotted by digital marketer Darcy Burk, who shared a screenshot of the test on X.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1206" height="1273" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ad-ai-generated-summary2-m8RjLPYy.png" alt="" class="wp-image-481427" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ad-ai-generated-summary2-m8RjLPYy.png 1206w, https://searchengineland.com/wp-content/seloads/2026/07/google-ad-ai-generated-summary2-m8RjLPYy-768x811.png 768w" sizes="(max-width: 1206px) 100vw, 1206px" /></figure>



<p><strong>Why we care</strong>. If rolled out more broadly, AI-generated summaries could influence how users interpret ads by highlighting information Google—not the advertiser—considers most relevant. This definitely raises questions about messaging accuracy (as stated in the Google disclaimer), brand control, and click-through performance.</p>



<p><strong>Between the lines</strong>. Google has previously experimented with AI-generated summaries for organic search listings. Extending similar functionality to paid ads would represent another step in integrating generative AI across the Search experience, though it&#8217;s unclear how summaries are generated or whether advertisers will have any control over the content.</p>



<p><strong>What we&#8217;re watching:</strong> Google has not publicly announced the feature or responded to requests for comment, making it unclear whether this is a limited experiment or part of a broader rollout.</p>



<p><strong>The bottom line</strong>. Google is testing AI-generated summaries within Search ads, signalling that generative AI could soon play a larger role in how paid search ads are displayed—even if advertisers don&#8217;t write the copy themselves.</p>



<p><strong>First spotted.</strong> Digital marketer Burke, is understandably not happy with <a href="https://x.com/darcyburk/status/2071776676904636798?">this update</a>.</p>


Google appears to be experimenting with AI-generated summaries inside sponsored search results, a move that could reshape how users interact with paid ads and how advertisers’ messaging is presented.
What’s happening. Some advertisers are seeing AI-generated summaries displayed directly beneath Google Ads descriptions in Search results. The summaries include a disclaimer stating: “Google AI responses are generated independently and can make mistakes, so double-check responses.”
The feature was first spotted by digital marketer Darcy Burk, who shared a screenshot of the test on X.




Why we care. If rolled out more broadly, AI-generated summaries could influence how users interpret ads by highlighting information Google—not the advertiser—considers most relevant. This definitely raises questions about messaging accuracy (as stated in the Google disclaimer), brand control, and click-through performance.
Between the lines. Google has previously experimented with AI-generated summaries for organic search listings. Extending similar functionality to paid ads would represent another step in integrating generative AI across the Search experience, though it’s unclear how summaries are generated or whether advertisers will have any control over the content.
What we’re watching: Google has not publicly announced the feature or responded to requests for comment, making it unclear whether this is a limited experiment or part of a broader rollout.
The bottom line. Google is testing AI-generated summaries within Search ads, signalling that generative AI could soon play a larger role in how paid search ads are displayed—even if advertisers don’t write the copy themselves.
First spotted. Digital marketer Burke, is understandably not happy with this update.

Google is testing AI-generated summaries beneath Search ads, potentially giving its AI a larger role in how paid ads are presented

Google tests AI-generated summaries in Search ads

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/GraphRAG-What-entity-first-retrieval-means-for-SEO.png" class="attachment-large size-large wp-post-image" alt="GraphRAG- What entity-first retrieval means for SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/GraphRAG-What-entity-first-retrieval-means-for-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/GraphRAG-What-entity-first-retrieval-means-for-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/GraphRAG-What-entity-first-retrieval-means-for-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/GraphRAG-What-entity-first-retrieval-means-for-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization.

Making your brand machine-readable and increasing its chances of being selected for AI-generated answers are only part of the picture. Underneath both is a retrieval layer that’s changing how AI systems identify entities, connect facts, and decide which brands to cite. 
That layer is GraphRAG. Understanding how it works turns “optimize for AI” from a vague idea into a practical strategy.
What is GraphRAG, actually?
GraphRAG extends traditional retrieval-augmented generation (RAG) with a knowledge graph that helps AI understand entities and the relationships between them.
It came out of Microsoft Research in 2024, and there’s a whole ecosystem built around it now. Instead of working from a flat sea of text scraps, it builds a map. 
Nodes are the entities (your company, your products, your people, your certifications).
Edges are the relationships between them (for example, “offers,” “is certified by,” and “authored”). 
Picture it as things and the lines connecting them. When a model works from a map instead of a pile of scraps, it doesn’t have to guess its way to an answer. It follows the lines. 
If the map says Entity A holds Certification B in Region C, the system follows that path with confidence instead of inferring it and crossing its fingers. That’s why graph-based retrieval produces more complete, better-grounded answers to hard questions, with far fewer hallucinations.
You don’t have to take my word for the failure modes. Microsoft laid them out in its GraphRAG patent, “Knowledge Graph Extraction” (US20250131289A1). It identifies the recall problem outright: In naive RAG, a less-prominent entity can get lost in the chunk embeddings, so nothing useful comes back. 
It also describes the fix: entity resolution that merges duplicate spellings of the same thing (the patent’s example untangles two spellings of one place name), so the system treats them as one. It’s one of the foundational building blocks behind graph-based retrieval.
Dig deeper: What patents reveal about the foundations of AI search



  
AI recommends.
      
See your AI visibility
    
Why your best content keeps getting passed over
Traditional RAG works by chopping content into fixed chunks, turning each one into a string of numbers (a vector), and storing those vectors in a database. When you ask a question, it retrieves the closest chunks in vector space and hands them to a language model to generate an answer.
That’s fine for “What’s the capital of France?” It falls apart on the questions that actually pay your bills: the multi-step ones. 
Ask it to find a provider that offers a specific service, holds a specific certification, and operates in a specific region, and naive RAG is stuck duct-taping an answer together from scraps that merely sound related. It has no idea how your facts connect, so it guesses across the gaps. 
When a system is forced to guess, the safe move is to leave your brand out of the answer rather than risk saying something wrong about you. Read that twice, because it’s the whole game.
That’s the trapdoor hiding under a lot of “our content is great, and we still never get cited.” GraphRAG consistently outperforms naive RAG on the complex, multi-hop questions where vector search falls apart. That’s where the leak is.
Your content probably isn’t the problem. The machine just couldn’t reliably tell what you are, how your facts fit together, or whether it could trust those connections enough to put your name on them.
The three problems GraphRAG is built to fix
GraphRAG’s strengths line up almost perfectly with three headaches you already deal with:
Disambiguation: This happens when the same entity, under different names, gets counted as separate, weaker signals instead of one. If “the firm,” “the agency,” and your actual brand name never resolve to a single entity, you’ve split your own authority three ways and handed two of them away.
Attribution: This is what happens when you don’t get the recognition you deserve. When your content gets blended into an AI answer, your identity tends to evaporate. The fact survives. The credit doesn’t.
Relationships: This happens when the connections that give your expertise meaning stay buried in prose instead of being declared as relationships a machine can read.
If you’ve ever watched AI confidently repeat something you wrote without naming you, or credit a competitor for your specialty, you’ve seen all three at work. 
Here’s what ties them together: None of them is a content-quality problem. It’s not about content. It’s about identity.
Same good sentence, just more of it the machine can use
Let me make this concrete, because the concept of “entity” will turn into mush fast if I don’t. Here are two examples, and I’ll flag the made-up one so nobody thinks I’m describing a real client.
Let’s start with a real-world example: Wayne Gretzky. Go run a quick test. Search his name in any AI client. Without hesitation, you’ll get a tidy box of facts, links to his former teams, his records, and more. AI will tell you who he is with total confidence. That’s not luck. That’s what a well-established entity looks like. His identity is nailed down and agreed upon across the web, so no machine has to guess who he is. Go look. It’s the clearest picture of what you’re ultimately aiming for.
Now let’s look at the opposite. Picture a goaltending coach in Moncton. Let’s call her Marie Tremblay. Her About page says, plainly and well:
“Our head coach, Marie ‘Lefty’ Tremblay, has run elite goaltending camps across the Maritimes for 20 years.”
That’s a good sentence. A parent reads it and gets it instantly. Leave it exactly as it is. Optimizing for machines doesn’t mean you stop writing for humans, and it absolutely doesn’t mean swapping your real voice for robotic phrasing. 
There’s no special sentence you write for AI. Instead, there’s the perfectly good sentence you’ve already written, plus what you add around it so a machine can use it.
What do you add? Nothing to the prose. Instead, you make explicit what a human reader infers automatically:
That “Lefty” and “Marie Tremblay” are one person, not two.
That Marie is connected to the academy, to goaltending as a discipline, and to the Maritimes as the region she serves.
That “20 years” and “elite” aren’t just adjectives. They point to something real that a machine can verify.
A human already knows all of that from one sentence. The machine doesn’t, so it won’t know to surface Marie in search queries where she should be a natural fit. Your job is to close the gap between what your reader understands and what the machine can verify until Marie is as legible to a system as The Great One already is. Keep the same sentence. Add the information around it. 
Why a flat triple isn’t enough for the knowledge graph anymore
Knowledge graphs are built on triples: subject, predicate, object. “Acme offers consulting.” Clean, powerful, and completely flat. However, a bare triple like that can’t easily carry the high-stakes information that lives or dies on, like whether a relationship is true, where it applies, who says so, and what backs it up.
That’s exactly the gap the standards community is working to close. The W3C is extending the model with Resource Description Framework (RDF)-star, which allows site owners to make statements about statements. They can attach metadata, such as source, date, and confidence, directly to a relationship instead of leaving it as a bare claim. It’s working its way through the RDF 1.2 standardization process (the RDF 1.2 Primer is the plain-English introduction), and its core specification reached Candidate Recommendation in April.
Microsoft’s GraphRAG patent follows the same direction. It pulls claims into a subject-action-object structure and weights relationships by how often they actually appear rather than treating every stated link as gospel.
The practical lesson isn’t complicated. The future of this layer isn’t just saying two things are related. It’s saying they’re related, and here’s the proof in a form a machine can verify. A richer triple beats a flatter page.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
The publishing layer is starting to answer back
Keep an eye one floor up from the models, because that’s where the wind is shifting.
On June 1, the new open standard EntityMap launched a 33-day public consultation ahead of its July 1 launch. It was started by Fred Laurent, CTO of InLinks and Waikay, with backing from Dixon Jones. Those are names this audience already associates with entity SEO and “strings to things.” The idea is deliberately familiar.
Where sitemap.xml tells search engines which pages exist, an entitymap.json file tells AI systems what an organization actually knows: which entities it covers, how they relate, and where the evidence lives. It’s open-licensed, with a human-readable companion file and a working reference implementation. 
What problems is it aiming to fix? Precisely the three headaches above, with the richer-triple idea baked right in. Every declared relationship can carry its receipts: a source URL, a publisher, and a timestamp. That’s no accident. It’s the publishing world building a proper front door for graph-based retrieval with provenance attached.
One caveat, and I’ll be blunt, because this is where reporting turns into cheerleading if you’re not careful. EntityMap is a proposal in consultation, not a rule anyone has to follow. No major engine has committed to reading files like these, so it’s still too early to treat it as a box to check. Treat it as a signal of what’s coming. Credible people are building entity-first publishing standards. That’s the part worth watching.
The honest state of play for GraphRAG
Two things keep GraphRAG firmly out of hype territory.
GraphRAG is expensive. Building the map, where a language model has to extract every entity and relationship, is the costly part. By Microsoft’s own estimate, graph extraction accounts for roughly 75% of indexing costs. That LLM tax is the real reason web-scale, real-time graph retrieval hasn’t swallowed everything overnight.
That cost curve is bending fast. A wave of recent research is tackling it directly, including TurboQuant, a vector compression method from Google Research and NYU, presented at ICLR 2026. It shrinks the memory footprint of the vectors these systems traverse severalfold with minimal quality loss. That’s the infrastructure catching up to the ambition.
That doesn’t mean the limitations have vanished, and it doesn’t mean every engine is running GraphRAG across the open web today. It means the economics are improving, which helps explain why entity-first standards are emerging now instead of five years from now. I’ve been in this game long enough to be suspicious of anything sold as inevitable, and this one passes the smell test.
To be clear, your existing structured data still matters. Schema.org markup, a clean Knowledge Panel, consistent NAP, none of that’s going anywhere. Entity-first work extends the structured-data discipline you already have. It doesn’t replace it.
Your entity-first action plan
Here’s where it gets practical. None of the following suggestions asks you to bet on any single standard.
Inventory your entities, not just your keywords 
Go beyond the keywords that have traditionally brought users to your site. Write down the things your brand genuinely knows something about: products, services, people, methods, and concepts. That’s your entity map, whether or not you ever publish one.
Disambiguate, then connect to the graph 
Claim and confirm your Wikidata entity and Google Knowledge Panel. Standardize your name so every variant resolves to one entity. Keep your sameAs links consistent across your structured data. This is the step that tells the world “Lefty” and “Marie Tremblay” are the same person, not two half-strangers splitting her reputation.
Make the relationships explicit 
Use Schema.org types and properties (Organization, Person, Product, knowsAbout, sameAs, and author) so the connections in your expertise are declared rather than implied. Mirror those same relationships in your internal linking. This is where you state, in a form a machine can read, that Marie coaches for the academy, knows about goaltending, and works in the Maritimes.
Attach evidence to every claim 
Tie your facts to sources a machine can verify: named authors, first-party data, and citations. Graph-based systems increasingly want the proof behind a relationship, not just the assertion. That’s how “20 years” and “elite” stop being adjectives and become claims with receipts.
Front-load your defining facts
Retrieval still reads through narrow windows. Put the clearest, most verifiable statement of what you are and what you do near the top, before it falls outside the chunk the system actually reads.
Watch the publishing layer, but don’t bet the farm on it 
Read the EntityMap spec while it’s in consultation, and speak up if you’ve got a perspective because the people shaping it are asking for exactly that. Decide later whether an entity index belongs in your stack. Keep your Schema.org work humming either way.
Tie your entity map to revenue 
Map your entity coverage to the queries that actually drive revenue so it lands with leadership as margin protection instead of a science project.
Measure what AI systems can recognize
The old KPIs, rankings, and clicks only describe the search-page model. Add a few more metrics, keeping in mind that the field is still maturing:
AI citation share: Across AI answers in your category, how often do you get named or cited versus your competitors? Track it with an AI visibility tool and trend it monthly.
Entity recognition: Do your key entities have confirmed Knowledge Panels and Wikidata entries? It’s a simple yes-or-no measure, but it’s foundational.
Relationship completeness: What share of your priority entities has explicit, marked-up relationships and consistent sameAs links?
Attribution rate: What share of your core claims is backed by linked, verifiable evidence?
Answer-equity proxies: Branded-query lift, assisted conversions from AI referrals, and lead stability as raw click volume softens. These business signals show whether your authority is compounding, even when CTR isn’t.
customers won’t either.
      
See your AI visibility
    
Where graph-based retrieval is heading
The road ahead for graph-based retrieval runs through multimodal graphs (text linked to images, audio, and structured data), streaming and incremental indexing for live data, and domain-specific ontologies, which are standardized vocabularies for fields like medicine, finance, and law.
The move from strings to things is gaining momentum. The brands that stay visible won’t be the ones shouting the loudest. They’ll be the ones a machine can understand without guessing, with clear entities, explicit relationships, and claims backed by evidence.
You don’t have to wait for a standard to launch before you start preparing. Make your brand legible to systems that don’t just read pages. They read what you know. In the answer economy, it was never about content. It’s always been about identity.

GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization.

GraphRAG: What entity-first retrieval means for SEO

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-TikTok-deserves-a-place-in-your-SEO-strategy.png" class="attachment-large size-large wp-post-image" alt="Why TikTok deserves a place in your SEO strategy" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-TikTok-deserves-a-place-in-your-SEO-strategy.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-TikTok-deserves-a-place-in-your-SEO-strategy-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-TikTok-deserves-a-place-in-your-SEO-strategy-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-TikTok-deserves-a-place-in-your-SEO-strategy-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>TikTok is adding new ways for businesses to be discovered, from Local Feed and AI summaries to creator reviews and TikTok Shop.


TikTok has helped make discovery more fragmented than ever. Someone might discover a restaurant on TikTok, verify it on Google Reviews, check Reddit for opinions, look at the menu on the business’s website, and then book a table. Or they might do all of that in a different order.
Nearly half of U.S. consumers used TikTok as a search engine in 2026, up from 41% in 2024, according to Adobe’s survey. The survey also found that users were especially drawn to TikTok’s short-form format, storytelling, and interactivity. Tutorials were the preferred search content format, followed by product reviews, personal stories, and influencer recommendations.
Its 2026 updates have leaned even further into becoming part of the search journey. Many buying decisions are visual, social, emotional, and trust-driven, and that’s exactly where TikTok is stepping in. From Local Feed to AI Overviews to new shopping features, TikTok is trying to meet users where they already are.
While many SEOs are still hesitant to use TikTok, you should ask yourself: How do you make sure people can find, understand, trust, and choose you wherever their search journey starts? Sometimes, that starting point might be TikTok.
TikTok SEO is more than hashtags now
Like traditional SEO, TikTok SEO is the process of making a business, place, product, service, or experience easier to discover. As the app has evolved, so have the features and ways people can find your content.
Previously, TikTok SEO was mostly about:
Captions.
Hashtags.
Trending sounds.
Posting times.
Hoping a video reached the For You feed.
Those elements still matter, but they’re no longer the whole picture. Users can discover your content through TikTok Search, recommendations, location-based surfaces (Local Feed and Places), reviews, comments, creator content, visual cues, and AI-assisted systems.
A more complete definition of TikTok SEO includes:
Search query relevance.
Spoken topic clarity.
On-screen text.
Captions and hashtags.
Location and place context.
Search suggestions.
Creator reviews and comments.
Product and visual signals.
What people search for after seeing your content.
TikTok’s documentation says search results can be influenced by how well content matches a query, along with hashtags, sounds, user interactions, and user context, such as language and location. 
Recommendations in the For You feed also weigh user interactions, content information, and user information, with watch behavior carrying significant weight.
customers find first.
      
Start your free trial
    
Let’s look at some of these aspects of search.
Local Feed
TikTok launched Local Feed in the U.S. on Feb. 11. It’s a home-screen tab designed to surface nearby content related to travel, events, restaurants, shopping, small businesses, and local creators. TikTok says posts appear based on location, topic, and when the content was published.  
The new Local Feed creates another organic touchpoint for discovery.




A restaurant can show up for someone deciding where to eat nearby. A wellness club can appear for someone looking for something to do this weekend. A venue can answer “before you go” questions before a guest ever reaches the box office.
For people who opt in, TikTok can use precise device location to make Local Feed even more relevant. However, there are a few caveats:
TikTok’s precise location setting is off by default, optional, available only to accounts for users 18 and older, and rolling out gradually across the U.S.
TikTok also says content from private accounts, accounts for users under 18, and posts limited to Friends or Only You won’t appear in Local Feed.
TikTok’s Local Explorer Program 
TikTok’s Local Explorer Program is one of the clearest signs that the platform is investing in place-based discovery.




The program encourages users to submit location-based reviews and rewards participation with experience points, levels, badges, community access, and other perks. Availability has been limited or uneven across regions, so businesses shouldn’t assume every market has the same level of access or activity.
I’ve also seen TikTok further incentivize reviews for places with no existing reviews. This coffee shop didn’t have any TikTok reviews, so I was offered a $1 Promote coupon to leave one.




You can also see that when a place doesn’t have reviews, TikTok pulls them from TripAdvisor. I’ve also seen it pull in Google reviews. The Places tab provides a convenient way for users to compare reviews, videos, and comments before deciding whether to visit a local business. 
Visual Search bar
TikTok increasingly adds automated search links and related query prompts beneath videos.
This example shows the search bar at the bottom of a video, where users can click to see more content about Glen Ivy. Although this example tags both the location and the Glen Ivy account in the description, these search bars also appear under videos with fewer location or product mentions.
TikTok can connect a video to a broader topic, place, or product discovery path even when the creator hasn’t used exact-match keywords throughout.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
TikTok Shop
With TikTok Shop, someone can see a product in a video, search for it, compare it through comments and creator content, and then buy it without leaving the app. TikTok Shop search relies heavily on how well a shopper’s query matches the product information available to TikTok, including titles, categories, attributes, and content context.
Recently, TikTok Shop released a Shoppable Photos feature in beta for select sellers. Eligible sellers can create image-based posts with multiple photos and tag products directly in the post. These posts can appear in the For You feed, Search, and the Shop tab. The feature gives sellers a simpler format for showcasing inventory without filming or editing videos.
AI
TikTok is testing and expanding several AI-assisted discovery and content creation features, although availability varies by market, account, and experiment.
Tako
AI Overviews
Quick Highlights
AI summaries
Content Studio
Tako is TikTok’s chatbot, which lets users search much like they do with the app’s standard search bar.




Tako surfaces TikTok videos relevant to a user’s query and external sources, such as articles.
Like Google, TikTok now offers AI Overviews. When users search for a topic, they may see an AI Overview summarizing the results. If they click a visual search bar, they may also see Quick Highlights, which provide AI-generated summaries for that search. 




The Places tab now includes AI summaries as well. Users can also see how many posts were used to generate a summary for a place they’re viewing.




On the creator side, AI features can help generate captions, hashtags, and even videos for sellers. According to TikTok: 
“Content Studio leverages industry-leading AIGC models trained specifically on high engagement TikTok videos to automatically generate top-tier content. This ensures products are showcased through professional-grade videos designed to mirror the style and pacing of the platform’s most successful trends.”
It’s important to note that Content Studio isn’t available to everyone and remains in testing.
How to improve your visibility on TikTok
On TikTok, visibility comes from what people can search for, what TikTok can understand, and what the camera actually shows. Make sure you’re showing people what they actually need to see.
For restaurants:




Menu items.
Exterior signage.
Dining room.
Takeout packaging.
Seasonal dishes.
Neighborhood cues.
For retail:
Product displays.
Packaging.
Try-ons.
Shelf layout.
Gift ideas.
Storefront.
Start building habits that make your content useful in both the main feed and location-based feeds:
Use location context naturally in your videos.
Show the product clearly.
Show the storefront, interior, menu, service process, products, or neighborhood.
Mention the city or neighborhood when it’s relevant.
Create timely content around events, launches, seasonal offers, weekends, and local moments.
Tag the physical location when appropriate.
Work with creators who already produce discovery-driven content.
Keyword research
You need to use the app to understand the topics and types of content people are searching for on TikTok.
Start with a seed topic:
Best brunch.
World Cup outfits.
Things to do in [location].
Wedding inspiration.
Gluten-free bakery.
Then:
Search the phrase on TikTok.
Document autocomplete suggestions.
Review the suggested filters.
Look at “Others searched for” prompts when available.
Check the top videos and their comment themes.
Search city and neighborhood modifiers.
Compare what you find with Google Search Console, Google autocomplete, Reddit, YouTube, and your site’s search data.
TikTok’s Creator Search Insights can also provide personalized information about search topics, content gaps, and the performance of content tied to searched topics. 
Keyword placement 
Use your core topic in:
The first few seconds of the video.
The first text overlay.
The opening of the caption.
Relevant hashtags.
Location tags.
Pinned comments.
Reply videos.
Profile bio.
Playlist names.
Creator briefs.
Comments and reviews
Your comments and reviews strategy should be simple:
Pin genuinely helpful comments.
Reply to repeated questions with videos.
Correct misinformation when it could affect trust.
Watch for objections that keep resurfacing.
Turn recurring objections and questions into FAQs, landing page content, GBP posts, and future videos.
Treat TikTok comments and reviews as visibility assets. A creator saying, “This is the best gluten-free bakery in Portland because they actually avoid cross-contamination,” may be more valuable for your website copy, FAQ strategy, and customer messaging than a generic five-star review.
Track referrals and branded search
Keep track of referral traffic from TikTok and monitor branded searches. Try to correlate branded search with TikTok virality. Annotate major TikTok posts and compare branded search trends against a baseline. 
Look for directional movement in branded clicks, branded impressions, TikTok referral traffic, GBP actions, and related-page engagement. Don’t assume TikTok caused every increase. Account for PR, paid campaigns, email, promotions, seasonality, and other marketing activity.
You may not be able to attribute every action perfectly, but that doesn’t make TikTok’s influence meaningless.
before your competitors.
      
Start your free trial
    
Don’t ignore it. Explore it.
Someone might find you on TikTok before they ever search for your brand in Google or ChatGPT. Or they might turn to TikTok midway through their journey to decide whether you’re worth the trip.
Either way, the platform has earned a meaningful role in how people discover, compare, and decide. Between Local Feed, the Places tab, Tako, AI summaries, and TikTok Shop, TikTok keeps adding new ways for your business to be discovered, and many of those opportunities remain largely untapped.

TikTok is adding new ways for businesses to be discovered, from Local Feed and AI summaries to creator reviews and TikTok Shop.

Why TikTok deserves a place in your SEO strategy

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>You can now quickly add a line to the chart to show previous time period comparison.

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/09/google-trends-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google Trends now lets you quickly add previous time period data to your chart, so you can visualize how the Google Trends data compares to the same time period, the period before.</p>



<p>Google announced on <a href="https://www.linkedin.com/posts/analyzing-search-interest-just-got-more-powerful-share-7478017577340968960-qsi3/">LinkedIn</a>, &#8220;You can now quickly compare a trend&#8217;s change against preceding periods directly within Google Trends.&#8221;</p>



<p><strong>What it looks like. </strong>Here is a GIF Google shared of this in action:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="549" src="https://searchengineland.com/wp-content/seloads/2026/07/google-trends-previous-compare.gif" alt="" class="wp-image-481410" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-trends-previous-compare.gif 800w, https://searchengineland.com/wp-content/seloads/2026/07/google-trends-previous-compare-768x527.gif 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p><strong>How it works. </strong>Go to Google Trends, add a search term or topic to the search box.  Then you will see &#8220;new chips&#8221; above your timeline, click on those chips to see the added data. These chips show percentage changes for various periods—whether you&#8217;re looking at Month-over-Month, Week-over-Week, or even specific Year-over-Year benchmarks. A single click overlays the historical comparison line onto your graph for instant visual context, Google explained.</p>



<p><strong>Why we care. </strong>Google Trends can be a useful source of data for topics and keywords that may interest your content team or data marketing team.  Knowing seasonal or trending data can be useful when planning your content strategy and marketing efforts.</p>



<p>This new feature gives you more historical perspective of this data.</p>


Google Trends now lets you quickly add previous time period data to your chart, so you can visualize how the Google Trends data compares to the same time period, the period before.
Google announced on LinkedIn, “You can now quickly compare a trend’s change against preceding periods directly within Google Trends.”
What it looks like. Here is a GIF Google shared of this in action:




How it works. Go to Google Trends, add a search term or topic to the search box.  Then you will see “new chips” above your timeline, click on those chips to see the added data. These chips show percentage changes for various periods—whether you’re looking at Month-over-Month, Week-over-Week, or even specific Year-over-Year benchmarks. A single click overlays the historical comparison line onto your graph for instant visual context, Google explained.
Why we care. Google Trends can be a useful source of data for topics and keywords that may interest your content team or data marketing team.  Knowing seasonal or trending data can be useful when planning your content strategy and marketing efforts.
This new feature gives you more historical perspective of this data.

You can now quickly add a line to the chart to show previous time period comparison.

Google Trends adds previous time period comparison

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance.png" class="attachment-large size-large wp-post-image" alt="How campaign structure shapes Google Ads performance" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Better automation starts with better account architecture. Learn how to strengthen signals, reduce overlap, and improve results.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance.png" class="attachment-large size-large wp-post-image" alt="How campaign structure shapes Google Ads performance" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-campaign-structure-shapes-Google-Ads-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Most Google Ads audits focus on the obvious: keywords, bids, ad copy, and Quality Scores. But one of the most significant — and most overlooked — performance barriers isn&#8217;t a setting you can find in a single campaign tab. It&#8217;s how the account is structured in the first place.</p>



<p>Campaign structure determines how Google&#8217;s machine learning interprets your data, how budget flows across your goals, and whether your data gets collected in one place or scattered across too many campaigns. Get it wrong, and you&#8217;re not just leaving performance on the table — you&#8217;re actively working against the algorithms you&#8217;re paying to optimize for.</p>



<p>Here&#8217;s how campaign structure affects performance across standard Search campaigns, <a href="https://searchengineland.com/google-performance-max-431821">Performance Max</a>, and <a href="https://searchengineland.com/your-guide-to-google-ads-smart-bidding-453765">Smart Bidding</a>, and what you can do about it.</p>



<h2 class="wp-block-heading" id="h-how-campaign-structure-shapes-google-s-learning">How campaign structure shapes Google&#8217;s learning</h2>



<p>Many advertisers treat campaign structure as a matter of tidiness: neatly sorted ad groups, logical naming conventions, and campaigns carved up by product line or geography. But to Google&#8217;s systems, structure means something else entirely.</p>



<p>Every campaign is a data container. The way you segment campaigns determines what signals Google pools together to make bidding and targeting decisions. A scattered structure means scattered learning, which leads to slower, less accurate optimization.</p>



<p>Google&#8217;s Smart Bidding and automation work better with more data concentrated in fewer campaigns. The algorithm needs volume — typically 30 to 50 conversions per campaign per month — to exit the learning phase and make reliable predictions. A structure that disperses conversions across too many campaigns starves each one of the data it needs to perform.</p>



<p>A common scenario: An ecommerce account has 12 separate Search campaigns, one for each product category. Each campaign averages 8-12 conversions per month. Smart Bidding is enabled across all of them, but none consistently exit the learning phase. </p>



<p>The fix is consolidation.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0101ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        See exactly how <span>your competitors win</span>.
      </div>
      <p id="semrush-one-subhead">
        Uncover the keywords, ads, landing pages, and strategies driving your competitors&#8217; paid search success—and find your next opportunity to outperform them.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">Analyze your competitors</span>
    </div>
    </div>
    </a>




<h2 class="wp-block-heading" id="h-over-segmentation-breaks-smart-bidding">Over-segmentation breaks Smart Bidding</h2>



<p>Smart Bidding strategies — Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value — rely on real-time signals: device, location, time of day, audience, search query, and more. Google weighs these signals together to predict which auctions are worth winning and at what price.</p>



<p>When campaigns are over-segmented, several problems can occur:</p>



<ul class="wp-block-list">
<li><strong>Insufficient conversion volume:</strong> Each campaign operates below the threshold Google needs to make confident bidding decisions, leading to unstable CPAs and CPCs.</li>



<li><strong>Extended learning phases:</strong> Every budget change, bid strategy switch, or structural edit triggers a new learning period. Over-segmented accounts are constantly stuck in a learning phase, never reaching their full potential.</li>



<li><strong>Missed signal consolidation:</strong> Bidding signals can&#8217;t be shared across campaigns. A campaign targeting branded terms can&#8217;t inform the bidding algorithm in a campaign targeting non-branded terms, even if they share a conversion goal.</li>



<li><strong>Bid cannibalization:</strong> Multiple campaigns competing in the same or overlapping auctions drive up your own costs and create internal inefficiency.</li>
</ul>



<p>The result is an account that appears fully optimized on the surface — with Smart Bidding enabled, audiences attached, and conversion tracking active — but underperforms because its structural foundation undermines every optimization built on top of it.</p>



<h2 class="wp-block-heading" id="h-the-impact-of-performance-max">The impact of Performance Max</h2>



<p>Performance Max has introduced an entirely new dimension to campaign structure. Unlike Search campaigns, PMax operates across all Google inventory — Search, Display, YouTube, Gmail, Discover, and Maps — using asset groups and audience signals to guide automation. This makes campaign setup both more important and trickier to get right.</p>



<h3 class="wp-block-heading" id="h-asset-group-segmentation">Asset group segmentation</h3>



<p>Asset groups within PMax function like mini-campaigns. Google uses them to understand context, match creative to searches, and optimize delivery. When asset groups are too broadly constructed — mixing unrelated products, audiences, or themes — the algorithm struggles to identify the right creative for the right context.</p>



<p>Best practice is to segment asset groups by:</p>



<ul class="wp-block-list">
<li>Product category or service line.</li>



<li>Audience intent level (prospecting vs. retargeting).</li>



<li>Creative theme or offer type.</li>
</ul>



<p>This gives Google clearer signals about what each group is trying to accomplish, improving both creative matching and bidding efficiency.</p>



<h3 class="wp-block-heading" id="h-pmax-and-search-campaign-overlap">PMax and Search campaign overlap</h3>



<p>One of the most damaging structural mistakes in accounts running both Search and Performance Max is failing to define clear boundaries between them. PMax is designed to serve ads across all placements — including branded and non-branded searches — which means it will compete with your Search campaigns if boundaries aren&#8217;t established.</p>



<p>Without proper segmentation:</p>



<ul class="wp-block-list">
<li>PMax can cannibalize high-intent branded search traffic, inflating costs on terms you would&#8217;ve won cheaply.</li>



<li>Your Search campaigns lose impression share they would&#8217;ve otherwise captured.</li>



<li>Attribution becomes muddied, making it difficult to understand which campaign is actually driving performance.</li>
</ul>



<p>The solution is to use campaign-level negative keywords, brand exclusions, and clear audience segmentation to define where each campaign type operates. PMax should complement your Search campaigns — not compete with them.</p>



<h3 class="wp-block-heading" id="h-budget-allocation-and-automation-conflict">Budget allocation and automation conflict</h3>



<p>PMax operates as a single campaign with a single budget, but its delivery across multiple channels means budget is allocated dynamically. When PMax and Search campaigns aren&#8217;t organized around clear goals, Google ends up spending budget on the easiest placements rather than the best ones.</p>



<p>Structural decisions — such as whether to run one PMax campaign or segment by product line — directly affect how budget is distributed and how well automation can support your business goals.</p>



<h2 class="wp-block-heading" id="h-match-type-strategy-and-its-structural-implications">Match type strategy and its structural implications</h2>



<p>Match types are often treated as a keyword-level decision, but they have structural consequences that extend across your entire account. Running broad match, phrase match, and exact match across separate campaigns — or even separate ad groups — without a coherent strategy can create significant overlap and budget waste.</p>



<p>Google Ads looks very different from what it did a few years ago. Broad match now casts a much wider net, and Google increasingly pushes advertisers to pair it with Smart Bidding. But that combination only works well when your campaign structure is set up to support it.</p>



<p>Broad match with Smart Bidding only works when there&#8217;s enough conversion data, a clear goal, and enough traffic for Google to learn from. In a fragmented account structure, broad match just adds to the problem. It brings in more searches, but the algorithm doesn&#8217;t have what it needs to make good use of them.</p>



<p>The safer approach is to keep your match types within fewer campaigns, use negative keywords to prevent your campaigns from bidding against each other, and regularly review your search term reports to identify where boundaries need tightening.</p>



<h3 class="wp-block-heading" id="h-keyword-and-ad-group-architecture-when-granularity-becomes-an-obstacle">Keyword and ad group architecture: When granularity becomes an obstacle</h3>



<p>Single Keyword Ad Groups (SKAGs) are largely a thing of the past, but many accounts still carry their legacy: hundreds of micro-segmented ad groups, each with one or two keywords and near-identical ads. This level of detail made sense when advertisers set bids manually. Today, it actively works against Smart Bidding.</p>



<p>Too many ad groups create the same data problem, just at a smaller scale. Google&#8217;s responsive search ads perform better when they have more to learn from, including which headlines get clicked, which asset combinations work, and how auctions play out. That learning happens faster when ad groups are consolidated and built around broader themes.</p>



<p>Aim for three to five tightly themed ad groups per campaign rather than dozens of micro-segmented groups. Each ad group should contain enough keyword variation to generate meaningful data while remaining focused enough to maintain message relevance.</p>



<p>The goal is maximum signal quality. Structural granularity that doesn&#8217;t improve data consolidation is unnecessary complexity.</p>



<div class="nl-inline-form border py-2 px-1 my-2">
	
	<div class="row align-items-center nl-inline-container">
		<div class="col-12 col-lg-3 col-xl-4 pe-md-0 pb-2 pb-lg-0">
			<p class="inline-form-text text-center mb-0">Get the newsletter search marketers rely on.</p>
		</div>
		<div class="col-12 col-lg-7 col-xl-6  pe-lg-0">
			<div class="form-nl-inline">
				<form id="mktoForm_16298"></form> 
				
				
			</div>
		</div>
		<div class="col-12 col-lg-2 col-xl-2 ps-1">
			<p class="text-center mb-0"><a class="nl-terms" href="https://searchengineland.com/termsofuse" target="_blank" aria-label="opens in a new tab">See terms.</a>
		</div>
	</div>
</div>




<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h3 class="wp-block-heading" id="h-conversion-goals-and-campaign-alignment">Conversion goals and campaign alignment</h3>



<p>Structure also determines which conversion actions each campaign optimizes toward, and misalignment here is one of the quietest performance killers in Google Ads.</p>



<p>If multiple campaigns share a conversion goal but that goal is poorly defined — or if different campaigns optimize toward different actions without a clear hierarchy — Smart Bidding receives conflicting instructions. It may optimize toward micro-conversions (page views, add-to-carts) when the account&#8217;s real objective is form fills and phone calls. Or it may treat equally weighted goals as equivalent when one is significantly more valuable than the other.</p>



<p>A structurally sound account aligns:</p>



<ul class="wp-block-list">
<li><strong>Campaign goals with business objectives:</strong> Not just platform metrics.</li>



<li><strong>Primary and secondary conversions:</strong> Clearly distinguish optimization targets from informational tracking.</li>



<li><strong>Conversion values:</strong> Ensure campaigns optimizing toward revenue have accurate value inputs for value-based bidding to function correctly.</li>
</ul>



<p>Performance Max is especially sensitive to conversion goal quality. Because PMax controls its own bidding and placement decisions, it will optimize aggressively toward whatever you tell it matters most. If that signal is inaccurate or misaligned, the campaign will optimize effectively — just not toward the right outcome.</p>



<h2 class="wp-block-heading" id="h-signs-your-structure-is-hurting-performance">Signs your structure is hurting performance</h2>



<p>Structural problems rarely announce themselves clearly. Instead, they show up as problems that are easy to blame on your ads, bids, or audiences:</p>



<ul class="wp-block-list">
<li><strong>Persistent learning phase warnings:</strong> Campaigns are frequently flagged as &#8220;limited by learning&#8221; despite consistent budgets.</li>



<li><strong>Unstable CPAs or ROAS:</strong> Wide variance in performance metrics that doesn&#8217;t stabilize over time.</li>



<li><strong>High impression share lost to budget:</strong> Particularly when total budgets appear adequate.</li>



<li><strong>Disproportionate spend:</strong> Budget funnels toward a small number of campaigns while others receive minimal delivery.</li>



<li><strong>Poor PMax search term visibility:</strong> An inability to understand which queries PMax is actually serving.</li>



<li><strong>Declining Quality Scores at scale:</strong> As accounts grow, relevance signals diminish across too many ad groups.</li>
</ul>



<p>If two or more of these symptoms are present at the same time, structure is the most likely root cause, and no amount of bid adjustments or creative testing will resolve it until the foundation is corrected.</p>



<h2 class="wp-block-heading" id="h-a-framework-for-structural-audits-and-consolidation">A framework for structural audits and consolidation</h2>



<p>Restructuring an active account carries risk. Any significant structural change can trigger learning phases and temporary performance disruption. The goal is to consolidate thoughtfully, using data as a guide.</p>



<h3 class="wp-block-heading" id="h-step-1-assess-conversion-volume-by-campaign">Step 1: Assess conversion volume by campaign</h3>



<p>Identify which campaigns consistently generate 30 or more conversions per month and which fall below this threshold. Underperforming campaigns are consolidation candidates.</p>



<h3 class="wp-block-heading" id="h-step-2-map-audience-and-intent-overlap">Step 2: Map audience and intent overlap</h3>



<p>Determine where campaigns compete against each other for similar searches or audiences. Overlap is waste, and structural waste is the most expensive kind.</p>



<h3 class="wp-block-heading" id="h-step-3-evaluate-pmax-and-search-boundaries">Step 3: Evaluate PMax and Search boundaries</h3>



<p>Audit how PMax and Search campaigns interact.</p>



<ul class="wp-block-list">
<li>Are brand terms being captured by the right campaign type?</li>



<li>Are negative keywords in place to prevent cannibalization?</li>
</ul>



<h3 class="wp-block-heading" id="h-step-4-simplify-ad-group-architecture">Step 4: Simplify ad group architecture</h3>



<p>Move from SKAG-style granularity toward theme-based groupings. Consolidate ad groups that serve overlapping intent into broader, theme-based groups.</p>



<h3 class="wp-block-heading" id="h-step-5-align-conversion-goals">Step 5: Align conversion goals</h3>



<p>Audit conversion actions across all campaigns. Ensure primary goals align with actual business outcomes and that value-based bidding inputs reflect real revenue data where applicable.</p>



<p><strong>Important:</strong> Restructuring should be staged, not executed all at once. Prioritize the highest-spend campaigns first, monitor performance through learning phases, and validate results before proceeding to the next round of consolidation.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0102ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        Every click they win is <span>a customer you lose</span>.
      </div>
      <p id="semrush-one-subhead">
        See where competitors are investing, which keywords drive their results, and how to capture more of the market.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See who’s stealing your traffic</span>
    </div>
    </div>
    </a>




<h2 class="wp-block-heading" id="h-campaign-structure-comes-first">Campaign structure comes first</h2>



<p>Campaign structure is the foundation of Google Ads performance. When it&#8217;s right, Smart Bidding, Performance Max, and audience targeting all work as intended, with enough signals, clear goals, and efficient budget allocation to drive real outcomes.</p>



<p>When it&#8217;s wrong, no optimization above it will fix the situation. Bids can&#8217;t correct fragmented data. Creative can&#8217;t fix misaligned conversion goals. Performance Max can&#8217;t prioritize efficiently when its boundaries with Search aren&#8217;t clearly defined.</p>



<p>The most impactful performance improvements in Google Ads often don&#8217;t come from a new bid strategy or a better headline. They come from stepping back, auditing your account architecture, and rebuilding the foundation that everything else depends on.</p>



<p>Structure first. Optimization second.</p>

Most Google Ads audits focus on the obvious: keywords, bids, ad copy, and Quality Scores. But one of the most significant — and most overlooked — performance barriers isn’t a setting you can find in a single campaign tab. It’s how the account is structured in the first place.
Campaign structure determines how Google’s machine learning interprets your data, how budget flows across your goals, and whether your data gets collected in one place or scattered across too many campaigns. Get it wrong, and you’re not just leaving performance on the table — you’re actively working against the algorithms you’re paying to optimize for.
Here’s how campaign structure affects performance across standard Search campaigns, Performance Max, and Smart Bidding, and what you can do about it.
How campaign structure shapes Google’s learning
Many advertisers treat campaign structure as a matter of tidiness: neatly sorted ad groups, logical naming conventions, and campaigns carved up by product line or geography. But to Google’s systems, structure means something else entirely.
Every campaign is a data container. The way you segment campaigns determines what signals Google pools together to make bidding and targeting decisions. A scattered structure means scattered learning, which leads to slower, less accurate optimization.
Google’s Smart Bidding and automation work better with more data concentrated in fewer campaigns. The algorithm needs volume — typically 30 to 50 conversions per campaign per month — to exit the learning phase and make reliable predictions. A structure that disperses conversions across too many campaigns starves each one of the data it needs to perform.
A common scenario: An ecommerce account has 12 separate Search campaigns, one for each product category. Each campaign averages 8-12 conversions per month. Smart Bidding is enabled across all of them, but none consistently exit the learning phase. 
The fix is consolidation.
your competitors win.
      
Analyze your competitors
    
Over-segmentation breaks Smart Bidding
Smart Bidding strategies — Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value — rely on real-time signals: device, location, time of day, audience, search query, and more. Google weighs these signals together to predict which auctions are worth winning and at what price.
When campaigns are over-segmented, several problems can occur:
Insufficient conversion volume: Each campaign operates below the threshold Google needs to make confident bidding decisions, leading to unstable CPAs and CPCs.
Extended learning phases: Every budget change, bid strategy switch, or structural edit triggers a new learning period. Over-segmented accounts are constantly stuck in a learning phase, never reaching their full potential.
Missed signal consolidation: Bidding signals can’t be shared across campaigns. A campaign targeting branded terms can’t inform the bidding algorithm in a campaign targeting non-branded terms, even if they share a conversion goal.
Bid cannibalization: Multiple campaigns competing in the same or overlapping auctions drive up your own costs and create internal inefficiency.
The result is an account that appears fully optimized on the surface — with Smart Bidding enabled, audiences attached, and conversion tracking active — but underperforms because its structural foundation undermines every optimization built on top of it.
The impact of Performance Max
Performance Max has introduced an entirely new dimension to campaign structure. Unlike Search campaigns, PMax operates across all Google inventory — Search, Display, YouTube, Gmail, Discover, and Maps — using asset groups and audience signals to guide automation. This makes campaign setup both more important and trickier to get right.
Asset group segmentation
Asset groups within PMax function like mini-campaigns. Google uses them to understand context, match creative to searches, and optimize delivery. When asset groups are too broadly constructed — mixing unrelated products, audiences, or themes — the algorithm struggles to identify the right creative for the right context.
Best practice is to segment asset groups by:
Product category or service line.
Audience intent level (prospecting vs. retargeting).
Creative theme or offer type.
This gives Google clearer signals about what each group is trying to accomplish, improving both creative matching and bidding efficiency.
PMax and Search campaign overlap
One of the most damaging structural mistakes in accounts running both Search and Performance Max is failing to define clear boundaries between them. PMax is designed to serve ads across all placements — including branded and non-branded searches — which means it will compete with your Search campaigns if boundaries aren’t established.
Without proper segmentation:
PMax can cannibalize high-intent branded search traffic, inflating costs on terms you would’ve won cheaply.
Your Search campaigns lose impression share they would’ve otherwise captured.
Attribution becomes muddied, making it difficult to understand which campaign is actually driving performance.
The solution is to use campaign-level negative keywords, brand exclusions, and clear audience segmentation to define where each campaign type operates. PMax should complement your Search campaigns — not compete with them.
Budget allocation and automation conflict
PMax operates as a single campaign with a single budget, but its delivery across multiple channels means budget is allocated dynamically. When PMax and Search campaigns aren’t organized around clear goals, Google ends up spending budget on the easiest placements rather than the best ones.
Structural decisions — such as whether to run one PMax campaign or segment by product line — directly affect how budget is distributed and how well automation can support your business goals.
Match type strategy and its structural implications
Match types are often treated as a keyword-level decision, but they have structural consequences that extend across your entire account. Running broad match, phrase match, and exact match across separate campaigns — or even separate ad groups — without a coherent strategy can create significant overlap and budget waste.
Google Ads looks very different from what it did a few years ago. Broad match now casts a much wider net, and Google increasingly pushes advertisers to pair it with Smart Bidding. But that combination only works well when your campaign structure is set up to support it.
Broad match with Smart Bidding only works when there’s enough conversion data, a clear goal, and enough traffic for Google to learn from. In a fragmented account structure, broad match just adds to the problem. It brings in more searches, but the algorithm doesn’t have what it needs to make good use of them.
The safer approach is to keep your match types within fewer campaigns, use negative keywords to prevent your campaigns from bidding against each other, and regularly review your search term reports to identify where boundaries need tightening.
Keyword and ad group architecture: When granularity becomes an obstacle
Single Keyword Ad Groups (SKAGs) are largely a thing of the past, but many accounts still carry their legacy: hundreds of micro-segmented ad groups, each with one or two keywords and near-identical ads. This level of detail made sense when advertisers set bids manually. Today, it actively works against Smart Bidding.
Too many ad groups create the same data problem, just at a smaller scale. Google’s responsive search ads perform better when they have more to learn from, including which headlines get clicked, which asset combinations work, and how auctions play out. That learning happens faster when ad groups are consolidated and built around broader themes.
Aim for three to five tightly themed ad groups per campaign rather than dozens of micro-segmented groups. Each ad group should contain enough keyword variation to generate meaningful data while remaining focused enough to maintain message relevance.
The goal is maximum signal quality. Structural granularity that doesn’t improve data consolidation is unnecessary complexity.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Conversion goals and campaign alignment
Structure also determines which conversion actions each campaign optimizes toward, and misalignment here is one of the quietest performance killers in Google Ads.
If multiple campaigns share a conversion goal but that goal is poorly defined — or if different campaigns optimize toward different actions without a clear hierarchy — Smart Bidding receives conflicting instructions. It may optimize toward micro-conversions (page views, add-to-carts) when the account’s real objective is form fills and phone calls. Or it may treat equally weighted goals as equivalent when one is significantly more valuable than the other.
A structurally sound account aligns:
Campaign goals with business objectives: Not just platform metrics.
Primary and secondary conversions: Clearly distinguish optimization targets from informational tracking.
Conversion values: Ensure campaigns optimizing toward revenue have accurate value inputs for value-based bidding to function correctly.
Performance Max is especially sensitive to conversion goal quality. Because PMax controls its own bidding and placement decisions, it will optimize aggressively toward whatever you tell it matters most. If that signal is inaccurate or misaligned, the campaign will optimize effectively — just not toward the right outcome.
Signs your structure is hurting performance
Structural problems rarely announce themselves clearly. Instead, they show up as problems that are easy to blame on your ads, bids, or audiences:
Persistent learning phase warnings: Campaigns are frequently flagged as “limited by learning” despite consistent budgets.
Unstable CPAs or ROAS: Wide variance in performance metrics that doesn’t stabilize over time.
High impression share lost to budget: Particularly when total budgets appear adequate.
Disproportionate spend: Budget funnels toward a small number of campaigns while others receive minimal delivery.
Poor PMax search term visibility: An inability to understand which queries PMax is actually serving.
Declining Quality Scores at scale: As accounts grow, relevance signals diminish across too many ad groups.
If two or more of these symptoms are present at the same time, structure is the most likely root cause, and no amount of bid adjustments or creative testing will resolve it until the foundation is corrected.
A framework for structural audits and consolidation
Restructuring an active account carries risk. Any significant structural change can trigger learning phases and temporary performance disruption. The goal is to consolidate thoughtfully, using data as a guide.
Step 1: Assess conversion volume by campaign
Identify which campaigns consistently generate 30 or more conversions per month and which fall below this threshold. Underperforming campaigns are consolidation candidates.
Step 2: Map audience and intent overlap
Determine where campaigns compete against each other for similar searches or audiences. Overlap is waste, and structural waste is the most expensive kind.
Step 3: Evaluate PMax and Search boundaries
Audit how PMax and Search campaigns interact.
Are brand terms being captured by the right campaign type?
Are negative keywords in place to prevent cannibalization?
Step 4: Simplify ad group architecture
Move from SKAG-style granularity toward theme-based groupings. Consolidate ad groups that serve overlapping intent into broader, theme-based groups.
Step 5: Align conversion goals
Audit conversion actions across all campaigns. Ensure primary goals align with actual business outcomes and that value-based bidding inputs reflect real revenue data where applicable.
Important: Restructuring should be staged, not executed all at once. Prioritize the highest-spend campaigns first, monitor performance through learning phases, and validate results before proceeding to the next round of consolidation.
a customer you lose.
      
See who’s stealing your traffic
    
Campaign structure comes first
Campaign structure is the foundation of Google Ads performance. When it’s right, Smart Bidding, Performance Max, and audience targeting all work as intended, with enough signals, clear goals, and efficient budget allocation to drive real outcomes.
When it’s wrong, no optimization above it will fix the situation. Bids can’t correct fragmented data. Creative can’t fix misaligned conversion goals. Performance Max can’t prioritize efficiently when its boundaries with Search aren’t clearly defined.
The most impactful performance improvements in Google Ads often don’t come from a new bid strategy or a better headline. They come from stepping back, auditing your account architecture, and rebuilding the foundation that everything else depends on.
Structure first. Optimization second.

Better automation starts with better account architecture. Learn how to strengthen signals, reduce overlap, and improve results.

How campaign structure shapes Google Ads performance

<div><img width="1920" height="1181" src="https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920-768x472.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920-1536x945.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Canel worked at Microsoft for about 30 years and was responsible for indexing, Bing Webmaster Tools, IndexNow and the foundation of Bing Search.

<div><img width="1920" height="1181" src="https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920-768x472.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/fabrice-canel-1920-1536x945.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>After almost 30 years at Microsoft, Fabrice Canel announced he is retiring from the company. Fabrice Canel <a href="https://www.linkedin.com/posts/fabricecanel_my-dear-followers-my-dear-fellow-microsoftees-share-7477980221443391488-cUnO/">wrote</a>, &#8220;I am retiring from Microsoft, effective today July 1st.&#8221; He added, &#8220;Today marks nearly 30 years with Microsoft. Thirty years…&#8221;</p>



<p><strong>Who is Fabrice Canel</strong>. Fabrice Canel is a 30-year veteran at Microsoft. He was responsible for indexing at Microsoft Bing, including crawling, URL discovery, content selection, and content processing.  He was the individual who came up with the <a href="https://searchengineland.com/indexnow-new-initiative-by-microsoft-and-yandex-to-push-content-to-search-engines-375247">IndexNow initiative</a> and helped to create and power Bing Webmaster Tools.</p>



<p>Fabrice Canel has spoken at countless conferences over the years, including <a href="https://searchengineland.com/smx">SMX</a>. He has written countless articles on how search works, how to perform better in Bing and how search is adapting with generative AI.  Fabrice not only helped run one of the most powerful and important search engines in the world, but also devoted significant time and effort to advancing the search industry through tooling and education.</p>



<p><strong>Message from Fabrice Canel. </strong>Here is what Fabrice Canel wrote on social media about his retirement:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>My dear fellow Microsoftees, engineers, attorneys, marketers, webmasters, publishers, SEO champions, product leaders, journalists, and all the brilliant minds across search, AI, and yes, even my friends at Google.</p>



<p>Today marks nearly 30 years with Microsoft. Thirty years… far too short a time to spend among such excellent and admirable people. As Bilbo once said, “I don’t know half of you half as well as I should like; and I like less than half of you half as well as you deserve”.</p>



<p>I stand before you for three purposes.</p>



<p>First, to tell you that I am immensely fond of you all. These three decades have been a wonderful adventure: from solving real business problems with IndexNow to helping webmasters and publishers thrive in the ever-changing world of SEO and AI. It has truly been an incredible journey.</p>



<p>Second, to celebrate this milestone and to thank every one of you: the colleagues I worked alongside daily, the partners and publishers I collaborated with, and especially the talented people I’ve trained and mentored over the past two years. You are ready, like young hobbits setting out with Sting and courage, to carry the team and our mission forward with excellence.</p>



<p>Third and finally, after many evenings by the fireside with my family and friends (who wisely reminded me that even the longest journey must eventually turn toward home), I have decided to take advantage of&nbsp;<a href="https://finance.yahoo.com/markets/stocks/articles/microsoft-voluntary-retirement-buyout-package-131245489.html">Microsoft’s Voluntary Retirement Program</a>.</p>



<p>So, though thirty years is far too short a time to spend among such excellent and admirable people… this is the end.</p>



<p>I am going.</p>



<p>I am retiring from Microsoft, effective today July 1st.</p>



<p>GOODBYE!</p>



<p>As Bilbo said, the road goes ever on… but for me it now turns west, toward a place of rest, starlight, song, and new tales after many adventures. I leave with a heart full of gratitude, memories brighter than mithril, and complete confidence in the bright future of the team.</p>



<p>Thank you, Microsoft.</p>



<p>Thank you to every colleague, partner, and webmaster who made this journey so special. Stay curious. Keep innovating. And may your content always be found; swift as eagles and sure as the stars.</p>



<p>Fabrice Canel<br>Retiring after 30 wonderful years at Microsoft (Bing)</p>
</blockquote>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">

</blockquote>
</blockquote>
</blockquote>



<p><strong>Why we care. </strong>Fabrice is a true friend to the industry; his contributions are endless, and the truth is, they will live on in the products he created for many years to come<strong>.</strong>  His shared wisdom and knowledge over the years are beyond awesome and breathtaking.  </p>



<p>While Fabrice has trained a team to continue his work, I would be lying if I said I am not sad that he is retiring. It has been an honor working with him over the years, but I know he has left Bing in good hands.</p>


After almost 30 years at Microsoft, Fabrice Canel announced he is retiring from the company. Fabrice Canel wrote, “I am retiring from Microsoft, effective today July 1st.” He added, “Today marks nearly 30 years with Microsoft. Thirty years…”
Who is Fabrice Canel. Fabrice Canel is a 30-year veteran at Microsoft. He was responsible for indexing at Microsoft Bing, including crawling, URL discovery, content selection, and content processing.  He was the individual who came up with the IndexNow initiative and helped to create and power Bing Webmaster Tools.
Fabrice Canel has spoken at countless conferences over the years, including SMX. He has written countless articles on how search works, how to perform better in Bing and how search is adapting with generative AI.  Fabrice not only helped run one of the most powerful and important search engines in the world, but also devoted significant time and effort to advancing the search industry through tooling and education.
Message from Fabrice Canel. Here is what Fabrice Canel wrote on social media about his retirement:
My dear fellow Microsoftees, engineers, attorneys, marketers, webmasters, publishers, SEO champions, product leaders, journalists, and all the brilliant minds across search, AI, and yes, even my friends at Google.
Today marks nearly 30 years with Microsoft. Thirty years… far too short a time to spend among such excellent and admirable people. As Bilbo once said, “I don’t know half of you half as well as I should like; and I like less than half of you half as well as you deserve”.
I stand before you for three purposes.
First, to tell you that I am immensely fond of you all. These three decades have been a wonderful adventure: from solving real business problems with IndexNow to helping webmasters and publishers thrive in the ever-changing world of SEO and AI. It has truly been an incredible journey.
Second, to celebrate this milestone and to thank every one of you: the colleagues I worked alongside daily, the partners and publishers I collaborated with, and especially the talented people I’ve trained and mentored over the past two years. You are ready, like young hobbits setting out with Sting and courage, to carry the team and our mission forward with excellence.
Third and finally, after many evenings by the fireside with my family and friends (who wisely reminded me that even the longest journey must eventually turn toward home), I have decided to take advantage of Microsoft’s Voluntary Retirement Program.
So, though thirty years is far too short a time to spend among such excellent and admirable people… this is the end.
I am going.
I am retiring from Microsoft, effective today July 1st.
GOODBYE!
As Bilbo said, the road goes ever on… but for me it now turns west, toward a place of rest, starlight, song, and new tales after many adventures. I leave with a heart full of gratitude, memories brighter than mithril, and complete confidence in the bright future of the team.
Thank you, Microsoft.
Thank you to every colleague, partner, and webmaster who made this journey so special. Stay curious. Keep innovating. And may your content always be found; swift as eagles and sure as the stars.
Fabrice Canel
Retiring after 30 wonderful years at Microsoft (Bing)
Why we care. Fabrice is a true friend to the industry; his contributions are endless, and the truth is, they will live on in the products he created for many years to come.  His shared wisdom and knowledge over the years are beyond awesome and breathtaking.  
While Fabrice has trained a team to continue his work, I would be lying if I said I am not sad that he is retiring. It has been an honor working with him over the years, but I know he has left Bing in good hands.

Canel worked at Microsoft for about 30 years and was responsible for indexing, Bing Webmaster Tools, IndexNow and the foundation of Bing Search.

All About
Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

Free Consultation

Reach out with any questions you might have regarding how RSO can help you with digital marketing.

All About Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

Free Consultation

Reach out with any questions you might have regarding how RSO can help you with digital marketing.

All About
Digital Marketing