<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action.png" class="attachment-large size-large wp-post-image" alt="Ways AI can turn Google Search Console data into action" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Find hidden patterns, classify search intent, spot trends, and prioritize SEO opportunities without digging through thousands of queries.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action.png" class="attachment-large size-large wp-post-image" alt="Ways AI can turn Google Search Console data into action" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Ways-AI-can-turn-Google-Search-Console-data-into-action-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><a href="https://searchengineland.com/google-search-console-seo-guide-443942">Google Search Console</a> has never been better at collecting data. It just hasn&#8217;t gotten much better at helping us interpret it.</p>



<p>Open almost any property, and you&#8217;ll find thousands of queries, landing pages, and performance metrics. That&#8217;s great until you&#8217;re trying to answer a deceptively simple question: What should I do with this?</p>



<p>For years, the answer has been to export the data into Excel or Google Sheets, build a few pivot tables, apply some filters, and start digging for patterns. It&#8217;s effective, but it&#8217;s also slow. More often than not, you&#8217;re hunting for insights you don&#8217;t even know exist.</p>



<p>That&#8217;s where AI fits into the workflow. It can accelerate the part that takes the longest: finding meaningful patterns hidden across thousands of rows of search data.</p>



<p>Think of Google Search Console as the source of truth and AI — whether you prefer ChatGPT or Claude — as the analyst sitting beside you. GSC tells you what happened. Your AI tool of choice can help you figure out why it happened, uncover opportunities you might overlook, and organize messy data into something you can actually act on.</p>



<h2 class="wp-block-heading" id="h-a-quick-note-on-regex">A quick note on regex</h2>



<p>Every example below starts in the same place in Google Search Console: <em>Performance → Queries → + Add Filter → Query → Custom (regex).</em>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1999" height="991" src="https://searchengineland.com/wp-content/seloads/2026/07/GSC-Regex.png" alt="GSC - Regex" class="wp-image-481858" srcset="https://searchengineland.com/wp-content/seloads/2026/07/GSC-Regex.png 1999w, https://searchengineland.com/wp-content/seloads/2026/07/GSC-Regex-768x381.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/GSC-Regex-1536x761.png 1536w" sizes="(max-width: 1999px) 100vw, 1999px" /></figure>
</div>


<p>From there, you&#8217;ll enter a regular expression to filter your query data.</p>



<p>The good news is you don&#8217;t have to memorize regex syntax anymore. Instead, let ChatGPT write it for you. You can prompt:</p>



<p>Create a regex for Google Search Console that matches queries beginning with question words.</p>



<p>ChatGPT will return something similar to <code>(?i)^(who|what|why|how|can|does|will|should)\b</code>



<p>Need something more specific? Just describe the pattern you&#8217;re looking for.</p>



<p>For example:</p>



<ul class="wp-block-list">
<li>Create a regex for Google Search Console that matches queries containing five or more words.</li>



<li>Create a regex for Google Search Console that identifies comparison searches.</li>



<li>Create a regex for Google Search Console that finds branded queries containing product names.</li>
</ul>



<p>The better you describe the pattern, the better the regex.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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<p>Here are seven ways to combine GSC with AI to spend less time sifting through data and more time making decisions.</p>



<h2 class="wp-block-heading" id="h-1-stop-looking-at-queries-and-start-looking-at-intent">1. Stop looking at queries and start looking at intent</h2>



<p>Most GSC analysis still happens at the keyword level. The problem? Users don&#8217;t search by keyword. They search with intent.</p>



<p>Instead of reviewing thousands of individual queries, use regex to isolate investigation-focused queries before exporting.</p>



<p>Use the regex: <code>(?i)^(best|top|vs|review|reviews|compare|comparison)</code>



<p>Next, export your query data and ask Claude or ChatGPT to classify search intent with the prompt:</p>



<ul class="wp-block-list">
<li>&#8220;Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.&#8221;</li>
</ul>



<p>Maybe informational traffic is growing while commercial investigation queries are declining. Maybe transactional queries have strong rankings but weak click-through rates. Maybe a group of comparison-related queries is driving impressions but lacks dedicated content.</p>



<p>Those insights are difficult to spot one keyword at a time. Intent segmentation makes them obvious.</p>



<h2 class="wp-block-heading" id="h-2-discover-questions-your-audience-is-already-asking">2. Discover questions your audience is already asking</h2>



<p>Question-based keyword research isn&#8217;t new. What is new is how quickly AI can help identify themes across hundreds of question-oriented searches.</p>



<p>Use the regex: <code>(?i)^(who|what|where|when|why|how|can|does|should|will)\b</code>



<p>Export the results. Then ask Claude or ChatGPT:</p>



<ul class="wp-block-list">
<li>&#8220;Group these questions into common themes and identify unanswered topics.&#8221;</li>
</ul>



<p>Instead of reviewing hundreds of individual questions, you&#8217;ll start seeing broader patterns, from pricing concerns to product comparisons, implementation challenges, and industry-specific use cases.</p>



<p>This quickly becomes more than a content exercise. These themes can influence FAQ development, support resources, sales enablement, and AI Overview optimization.</p>



<p>The best opportunities often aren&#8217;t hidden in individual queries. They&#8217;re hidden in clusters of related questions.</p>



<h2 class="wp-block-heading" id="h-3-find-queries-most-likely-to-trigger-ai-overviews">3. Find queries most likely to trigger AI Overviews</h2>



<p>While Google doesn&#8217;t provide a filter for &#8220;queries likely to trigger AI Overviews,&#8221; you can create your own approximation.</p>



<p>Start by isolating common informational and comparison patterns with regex: <code>(?i)^(what is|how to|best|vs|difference between|guide to)</code>



<p>Export the matching queries and ask Claude or ChatGPT:</p>



<ul class="wp-block-list">
<li>&#8220;Review these queries and group them by the content format needed to answer them effectively.&#8221;</li>
</ul>



<p>The resulting themes often include definitions, tutorials, comparisons, or expert recommendations.</p>



<p>With this process, you&#8217;re identifying where your content may need to shift from ranking for keywords to becoming the best source for answering questions. Increasingly, those aren&#8217;t always the same thing.</p>



<h2 class="wp-block-heading" id="h-4-track-emerging-trends">4. Track emerging trends</h2>



<p>Traditional keyword research tends to be reactive. By the time a trend becomes obvious in your keyword tools, your competitors are already targeting it.</p>



<p>Google Search Console is a great resource for identifying those shifts early. You just have to know how to find them.</p>



<p>Instead of searching for individual keywords, use ChatGPT to build regex around broader concepts.</p>



<p>You&#8217;ll need to craft a prompt specific to changes in your industry. For example:</p>



<ul class="wp-block-list">
<li>&#8220;Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.&#8221;</li>
</ul>



<p>The result: <code>(?i)(ai agent|agentic|copilot|assistant|automation)</code>



<p>This same approach works for new technologies, product categories, competitors, industry buzzwords, or changing customer concerns.</p>



<p>Once you&#8217;ve filtered and exported the data, let your AI analyst do the heavy lifting.</p>



<p>Try a prompt like:</p>



<ul class="wp-block-list">
<li>&#8220;Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.&#8221;</li>
</ul>



<p>Rather than simply confirming that a trend exists, AI can help determine whether it&#8217;s meaningful enough to act on and what your next move should be.</p>



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



<h2 class="wp-block-heading" id="h-5-surface-conversion-intent-hiding-in-informational-traffic">5. Surface conversion intent hiding in informational traffic</h2>



<p>One of the most overlooked opportunities in Search Console is identifying bottom-of-funnel signals in queries that appear informational at first glance.</p>



<p>Ask ChatGPT:</p>



<ul class="wp-block-list">
<li>&#8220;Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.&#8221;</li>
</ul>



<p>Example output: <code>(?i)(cost|pricing|price|vs|alternative|compare|implementation|migration)</code>



<p>Apply that regex to your query report and export the filtered data.</p>



<p>Then ask Claude or ChatGPT to analyze the results:</p>



<ul class="wp-block-list">
<li>&#8220;Review these Google Search Console queries and identify recurring buying signals. Group them into themes (e.g., pricing, comparisons, implementation, vendor evaluation), recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.&#8221;</li>
</ul>



<p>You may discover that pages built for top-of-funnel education are already attracting visitors who are evaluating solutions. Instead of creating new content, the better opportunity may be to refine what already exists, making it easier for users to take the next step without disrupting the informational experience.</p>



<p>Sometimes the biggest content opportunity isn&#8217;t publishing another page. It&#8217;s recognizing the conversion intent that&#8217;s already finding its way to the ones you have.</p>



<h2 class="wp-block-heading" id="h-6-find-audience-specific-opportunities">6. Find audience-specific opportunities</h2>



<p>One of my favorite ways to uncover new content opportunities is by filtering queries for specific industries, audiences, or customer segments. It&#8217;s a quick way to see whether your content is resonating with the audiences you intended to reach or revealing opportunities you hadn&#8217;t considered.</p>



<p>Start by asking ChatGPT to create a regex based on the audience segments that matter most to your business.</p>



<p>Example prompt:</p>



<ul class="wp-block-list">
<li>&#8220;Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.&#8221;</li>
</ul>



<p>Example output: <code>(?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit)</code>



<p>Apply that filter in Google Search Console and export the results.</p>



<p>Then ask Claude or ChatGPT:</p>



<ul class="wp-block-list">
<li>&#8220;Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.&#8221;</li>
</ul>



<p>Maybe healthcare-related searches consistently focus on compliance, while manufacturing queries revolve around implementation. Maybe retailers are searching for entirely different use cases than financial services organizations.</p>



<h2 class="wp-block-heading" id="h-7-uncover-striking-distance-opportunities-at-scale">7. Uncover &#8216;striking distance&#8217; opportunities at scale</h2>



<p>Every SEO knows the classic recommendation: &#8220;Look at keywords ranking in positions 5-15 to identify opportunities within striking distance.&#8221;</p>



<p>The challenge, again, is doing this at scale. A report with hundreds of queries where your site is within striking distance of a top-ranking position can quickly become overwhelming.</p>



<p>Take any of the regex patterns above a step further. Use the same filters based on your needs and goals, then filter your data to positions 5-15 before exporting the queries.</p>



<p>Then ask your AI analyst:</p>



<ul class="wp-block-list">
<li>&#8220;Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.&#8221;</li>
</ul>



<p>Rather than recommending tweaks to individual keywords, AI often surfaces larger opportunities. You may identify missing subtopics or incomplete comparison content. Maybe you have relevant content, but weak internal linking or missing use cases.</p>



<p>The result is often fewer optimizations, but significantly more impactful ones.</p>


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<h2 class="wp-block-heading" id="h-turn-google-search-console-data-into-decisions">Turn Google Search Console data into decisions</h2>



<p>As SEOs, we don&#8217;t have a problem with a lack of data. We have a prioritization problem.</p>



<p>Google Search Console has always been one of the richest sources of insight into how people discover your business. The challenge has long been turning thousands of rows into something actionable.</p>



<p>That&#8217;s where AI fits into the workflow. It helps uncover patterns, organize information, and surface opportunities you might have otherwise missed. It&#8217;s not an SEO strategist or a replacement for experience and critical thinking.</p>



<p>The real advantage isn&#8217;t writing better regex or exporting cleaner spreadsheets. It&#8217;s spending less time searching for insights and more time acting on them.</p>



<p>Because data doesn&#8217;t improve SEO. Better decisions do.</p>

Google Search Console has never been better at collecting data. It just hasn’t gotten much better at helping us interpret it.
Open almost any property, and you’ll find thousands of queries, landing pages, and performance metrics. That’s great until you’re trying to answer a deceptively simple question: What should I do with this?
For years, the answer has been to export the data into Excel or Google Sheets, build a few pivot tables, apply some filters, and start digging for patterns. It’s effective, but it’s also slow. More often than not, you’re hunting for insights you don’t even know exist.
That’s where AI fits into the workflow. It can accelerate the part that takes the longest: finding meaningful patterns hidden across thousands of rows of search data.
Think of Google Search Console as the source of truth and AI — whether you prefer ChatGPT or Claude — as the analyst sitting beside you. GSC tells you what happened. Your AI tool of choice can help you figure out why it happened, uncover opportunities you might overlook, and organize messy data into something you can actually act on.
A quick note on regex
Every example below starts in the same place in Google Search Console: Performance → Queries → + Add Filter → Query → Custom (regex).







From there, you’ll enter a regular expression to filter your query data.
The good news is you don’t have to memorize regex syntax anymore. Instead, let ChatGPT write it for you. You can prompt:
Create a regex for Google Search Console that matches queries beginning with question words.
ChatGPT will return something similar to (?i)^(who|what|why|how|can|does|will|should)\b



Need something more specific? Just describe the pattern you’re looking for.
For example:
Create a regex for Google Search Console that matches queries containing five or more words.
Create a regex for Google Search Console that identifies comparison searches.
Create a regex for Google Search Console that finds branded queries containing product names.
The better you describe the pattern, the better the regex.
AI recommends.
      
See your AI visibility
    
Here are seven ways to combine GSC with AI to spend less time sifting through data and more time making decisions.
1. Stop looking at queries and start looking at intent
Most GSC analysis still happens at the keyword level. The problem? Users don’t search by keyword. They search with intent.
Instead of reviewing thousands of individual queries, use regex to isolate investigation-focused queries before exporting.
Use the regex: (?i)^(best|top|vs|review|reviews|compare|comparison)



Next, export your query data and ask Claude or ChatGPT to classify search intent with the prompt:
“Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.”
Maybe informational traffic is growing while commercial investigation queries are declining. Maybe transactional queries have strong rankings but weak click-through rates. Maybe a group of comparison-related queries is driving impressions but lacks dedicated content.
Those insights are difficult to spot one keyword at a time. Intent segmentation makes them obvious.
2. Discover questions your audience is already asking
Question-based keyword research isn’t new. What is new is how quickly AI can help identify themes across hundreds of question-oriented searches.
Use the regex: (?i)^(who|what|where|when|why|how|can|does|should|will)\b



Export the results. Then ask Claude or ChatGPT:
“Group these questions into common themes and identify unanswered topics.”
Instead of reviewing hundreds of individual questions, you’ll start seeing broader patterns, from pricing concerns to product comparisons, implementation challenges, and industry-specific use cases.
This quickly becomes more than a content exercise. These themes can influence FAQ development, support resources, sales enablement, and AI Overview optimization.
The best opportunities often aren’t hidden in individual queries. They’re hidden in clusters of related questions.
3. Find queries most likely to trigger AI Overviews
While Google doesn’t provide a filter for “queries likely to trigger AI Overviews,” you can create your own approximation.
Start by isolating common informational and comparison patterns with regex: (?i)^(what is|how to|best|vs|difference between|guide to)



Export the matching queries and ask Claude or ChatGPT:
“Review these queries and group them by the content format needed to answer them effectively.”
The resulting themes often include definitions, tutorials, comparisons, or expert recommendations.
With this process, you’re identifying where your content may need to shift from ranking for keywords to becoming the best source for answering questions. Increasingly, those aren’t always the same thing.
4. Track emerging trends
Traditional keyword research tends to be reactive. By the time a trend becomes obvious in your keyword tools, your competitors are already targeting it.
Google Search Console is a great resource for identifying those shifts early. You just have to know how to find them.
Instead of searching for individual keywords, use ChatGPT to build regex around broader concepts.
You’ll need to craft a prompt specific to changes in your industry. For example:
“Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.”
The result: (?i)(ai agent|agentic|copilot|assistant|automation)



This same approach works for new technologies, product categories, competitors, industry buzzwords, or changing customer concerns.
Once you’ve filtered and exported the data, let your AI analyst do the heavy lifting.
Try a prompt like:
“Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.”
Rather than simply confirming that a trend exists, AI can help determine whether it’s meaningful enough to act on and what your next move should be.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
5. Surface conversion intent hiding in informational traffic
One of the most overlooked opportunities in Search Console is identifying bottom-of-funnel signals in queries that appear informational at first glance.
Ask ChatGPT:
“Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.”
Example output: (?i)(cost|pricing|price|vs|alternative|compare|implementation|migration)



Apply that regex to your query report and export the filtered data.
Then ask Claude or ChatGPT to analyze the results:
“Review these Google Search Console queries and identify recurring buying signals. Group them into themes (e.g., pricing, comparisons, implementation, vendor evaluation), recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.”
You may discover that pages built for top-of-funnel education are already attracting visitors who are evaluating solutions. Instead of creating new content, the better opportunity may be to refine what already exists, making it easier for users to take the next step without disrupting the informational experience.
Sometimes the biggest content opportunity isn’t publishing another page. It’s recognizing the conversion intent that’s already finding its way to the ones you have.
6. Find audience-specific opportunities
One of my favorite ways to uncover new content opportunities is by filtering queries for specific industries, audiences, or customer segments. It’s a quick way to see whether your content is resonating with the audiences you intended to reach or revealing opportunities you hadn’t considered.
Start by asking ChatGPT to create a regex based on the audience segments that matter most to your business.
Example prompt:
“Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.”
Example output: (?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit)



Apply that filter in Google Search Console and export the results.
Then ask Claude or ChatGPT:
“Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.”
Maybe healthcare-related searches consistently focus on compliance, while manufacturing queries revolve around implementation. Maybe retailers are searching for entirely different use cases than financial services organizations.
7. Uncover ‘striking distance’ opportunities at scale
Every SEO knows the classic recommendation: “Look at keywords ranking in positions 5-15 to identify opportunities within striking distance.”
The challenge, again, is doing this at scale. A report with hundreds of queries where your site is within striking distance of a top-ranking position can quickly become overwhelming.
Take any of the regex patterns above a step further. Use the same filters based on your needs and goals, then filter your data to positions 5-15 before exporting the queries.
Then ask your AI analyst:
“Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.”
Rather than recommending tweaks to individual keywords, AI often surfaces larger opportunities. You may identify missing subtopics or incomplete comparison content. Maybe you have relevant content, but weak internal linking or missing use cases.
The result is often fewer optimizations, but significantly more impactful ones.
before your competitors.
      
Start your free trial
    
Turn Google Search Console data into decisions
As SEOs, we don’t have a problem with a lack of data. We have a prioritization problem.
Google Search Console has always been one of the richest sources of insight into how people discover your business. The challenge has long been turning thousands of rows into something actionable.
That’s where AI fits into the workflow. It helps uncover patterns, organize information, and surface opportunities you might have otherwise missed. It’s not an SEO strategist or a replacement for experience and critical thinking.
The real advantage isn’t writing better regex or exporting cleaner spreadsheets. It’s spending less time searching for insights and more time acting on them.
Because data doesn’t improve SEO. Better decisions do.

Find hidden patterns, classify search intent, spot trends, and prioritize SEO opportunities without digging through thousands of queries.

7 ways AI can turn Google Search Console data into action

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right.png" class="attachment-large size-large wp-post-image" alt="Why marketing mix modeling is still hard to get right" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and human expertise.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right.png" class="attachment-large size-large wp-post-image" alt="Why marketing mix modeling is still hard to get right" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-marketing-mix-modeling-is-still-hard-to-get-right-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Marketing mix modeling (MMM) is becoming more accessible, but getting started remains a challenge.</p>



<p>After several conversations about MMM adoption, I noticed the same question kept coming up: &#8220;We believe in the concept of MMM, but we don&#8217;t know how to get started.&#8221;</p>



<p>The answer is that viable open-source platforms have dramatically lowered the barrier to entry. They haven&#8217;t lowered the level of expertise required to produce trustworthy, actionable results.</p>



<h2 class="wp-block-heading" id="h-open-source-mmm-has-changed-the-starting-point">Open-source MMM has changed the starting point</h2>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1200" src="https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-one-spectrum.png" alt="The floor dropped" class="wp-image-481853" srcset="https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-one-spectrum.png 2048w, https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-one-spectrum-768x450.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-one-spectrum-1536x900.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>MMM adoption is accelerating. <a href="https://www.digitalapplied.com/blog/marketing-mix-modeling-2026-mmm-vs-attribution-playbook" target="_blank" rel="noopener">Almost half (46.9%) of U.S. marketers</a> will invest more in MMM over the next year, and they ranked MMM as the most reliable measurement methodology (27.6%).</p>



<p>The open-source revolution in MMM is real. Three production-grade libraries now cover the full methodological spectrum:</p>



<ul class="wp-block-list">
<li><strong>Robyn (Meta, R):</strong> Automated hyperparameter search via Nevergrad, Pareto frontier model selection, and built-in decomposition and response curve plots — the most approachable entry point. It&#8217;s the one I use most because it&#8217;s highly customizable.</li>



<li><strong>Meridian (Google, Python/TensorFlow):</strong> Bayesian inference with geo-level priors and principled uncertainty quantification — more rigorous, with a steeper learning curve.</li>



<li><strong>PyMC-Marketing (PyMC Labs, Python):</strong> The most flexible option, offering a full probabilistic model that&#8217;s closest to academic-grade Bayesian MMM — but it also requires the most statistical fluency.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1123" src="https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-and-one-spectrum.png" alt="3 open-source MM libraries and one spectrum" class="wp-image-481854" srcset="https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-and-one-spectrum.png 2048w, https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-and-one-spectrum-768x421.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/3-open-source-MM-libraries-and-one-spectrum-1536x842.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>This generation of tools has eliminated the $150,000 to $500,000 consulting gate that used to be the only path into MMM. Any team with R or Python expertise and relatively clean historical data can now run a model in-house.</p>



<p>The key caveat worth making explicit in any conversation with those exploring MMM is this: &#8220;Free tool&#8221; doesn&#8217;t mean &#8220;free model.&#8221; The software is free. The domain expertise required to configure it correctly — a hugely important part of the process — isn’t.</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">
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        See exactly how <span>your competitors win</span>.
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        Uncover the keywords, ads, landing pages, and strategies driving your competitors&#8217; paid search success—and find your next opportunity to outperform them.
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<h2 class="wp-block-heading" id="h-a-crowded-vendor-landscape-with-an-interesting-power-dynamic">A crowded vendor landscape with an interesting power dynamic</h2>



<p>The SaaS layer built on top of open-source MMM has proliferated quickly. It&#8217;s worth distinguishing a few tiers.</p>



<h3 class="wp-block-heading" id="h-data-layer-first-vendors">Data-layer-first vendors</h3>



<p>Platforms like Rockerbox and Northbeam started as attribution and data collection platforms, then added MMM. Their edge is data pipelines and speed, not modeling depth or customization.</p>



<h3 class="wp-block-heading" id="h-measurement-first-vendors">Measurement-first vendors</h3>



<p>Platforms like Measured, Analytic Partners, Ekimetrics, and Nielsen Gracenote offer more rigorous modeling at a higher price point, with enterprise-grade capabilities.</p>



<h3 class="wp-block-heading" id="h-google-meridian-and-ga360">Google Meridian and GA360</h3>



<p>One point is worth calling out. Google&#8217;s open-sourcing of Meridian was a generous contribution to the field and, at the same time, a strategic one. When a walled garden funds and packages the measurement methodology used to evaluate its own channels, it&#8217;s worth maintaining healthy skepticism about model priors and default assumptions, even with transparent code.</p>



<p>The practical question when evaluating vendors is: who owns your data layer, and does that create conflicts in the modeling layer?</p>



<h2 class="wp-block-heading" id="h-challenge-1-data-access-is-the-silent-mmm-killer">Challenge 1: Data access is the silent MMM killer</h2>



<p>This is the most underappreciated implementation blocker, and it rarely gets the attention it deserves. A well-specified MMM needs:</p>



<ul class="wp-block-list">
<li>Two to three years of weekly data as a baseline — enough to capture at least two full seasonality cycles and a meaningful range of spend variation.</li>



<li>Consistent channel-level spend granularity — not just &#8220;digital,&#8221; but search, social, display, and video broken out separately.</li>



<li>Offline channels (TV, OOH, radio, events, direct mail — which typically live in different systems) are owned by different teams, and often use incompatible time granularities.</li>



<li>External covariates — macro indicators, competitor activity, pricing data, and product launch calendars.</li>



<li>For B2B specifically, longer sales cycles and lower conversion volumes make the data requirements even more demanding. You often need more history.</li>
</ul>



<p>In practice, what blocks most MMM projects is the six-week data archaeology exercise that comes before model building. Finance owns revenue. The brand team owns TV. The agency owns digital spend. The spreadsheet someone built in 2021 is the only record of trade promotions.</p>



<p>The model is only as good as the data archaeology that precedes it, and nobody tells you that in the vendor demo.</p>



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



<h2 class="wp-block-heading" id="h-challenge-2-you-still-need-to-roll-up-your-sleeves">Challenge 2: You still need to roll up your sleeves</h2>



<p>AI assistants have meaningfully lowered the syntax barrier. They can scaffold a Robyn run, generate a Meridian config, or help debug a PyMC model. What they can&#8217;t yet do is navigate the judgment calls that make an MMM trustworthy:</p>



<ul class="wp-block-list">
<li>Choose where to sit on a Pareto frontier of hundreds of model solutions (NRMSE vs. DECOMP.RSSD tradeoffs).</li>



<li>Know when Nevergrad&#8217;s optimizer has meaningfully converged versus landed in a local minimum.</li>



<li>Configure adstock transformation parameters (Weibull shape/scale, geometric decay) to match realistic channel dynamics.</li>



<li>Diagnose why a model assigns an implausible contribution to a channel, and whether to address it with a prior, a data correction, or a variable exclusion.</li>
</ul>



<p>In other words, <a href="https://searchengineland.com/vibe-coding-with-ai-tools-a-marketers-guide-455134">vibe coding</a> your way to an MMM will produce a model that appears to work but is wrong in ways you won&#8217;t catch. The scripting isn&#8217;t the hard part. The domain expertise required to validate the output includes running channel-specific incrementality experiments to calibrate your MMM.</p>



<h2 class="wp-block-heading" id="h-challenge-3-the-human-expertise-layer-isn-t-optional">Challenge 3: The human expertise layer isn&#8217;t optional</h2>



<p>Even when the tooling matures to the point where AI can run a competent default MMM, the irreplaceable human contribution is encoding business context — things no model can infer from the data alone:</p>



<ul class="wp-block-list">
<li><strong>Adstock and carryover context:</strong> Your TV buy has a four-week carryover. Your paid search has a three-day carryover. Your branded awareness campaign has a decay that spans months. This information isn&#8217;t found in the data. It&#8217;s in the minds of the channel experts.</li>



<li><strong>Saturation curve shape:</strong> Knowing a channel is likely approaching diminishing returns before the model tells you so, and questioning the results when the model suggests otherwise.</li>



<li><strong>Guardrails and anomaly handling:</strong> Factors like COVID troughs, product launches, pricing shifts, and macro disruptions need to be modeled explicitly or flagged as structural breaks. AI doesn&#8217;t know your client had a pricing crisis in Q3 2022.</li>



<li><strong>Interpretation sanity checks:</strong> A modeled TV contribution of 40% for a brand spending $2 million on TV may &#8220;feel wrong&#8221; and warrant investigation. That intuition is earned, not computed.</li>



<li><strong>Organizational translation:</strong> The most technically correct model is worthless if you can&#8217;t explain why it recommends shifting 15% of the search budget to CTV in terms a CMO and CFO will act on.</li>
</ul>



<h2 class="wp-block-heading" id="h-lay-the-groundwork-before-you-build-a-model">Lay the groundwork before you build a model</h2>



<p>The best place to begin is understanding what data you need to fuel the model and who needs to help contextualize and translate that data into effective marketing decisions. Neither is easy or fast, but both are essential if you want to get meaningful insights from your model, regardless of whether you choose an open-source or subscription-based platform.</p>



<p>A practical first step is to <a href="https://github.com/facebookexperimental/Robyn/tree/main/demo" target="_blank" rel="noopener">download Robyn&#8217;s demo script and experiment</a> with the sample data before applying it to your own.</p>

Marketing mix modeling (MMM) is becoming more accessible, but getting started remains a challenge.
After several conversations about MMM adoption, I noticed the same question kept coming up: “We believe in the concept of MMM, but we don’t know how to get started.”
The answer is that viable open-source platforms have dramatically lowered the barrier to entry. They haven’t lowered the level of expertise required to produce trustworthy, actionable results.
Open-source MMM has changed the starting point




MMM adoption is accelerating. Almost half (46.9%) of U.S. marketers will invest more in MMM over the next year, and they ranked MMM as the most reliable measurement methodology (27.6%).
The open-source revolution in MMM is real. Three production-grade libraries now cover the full methodological spectrum:
Robyn (Meta, R): Automated hyperparameter search via Nevergrad, Pareto frontier model selection, and built-in decomposition and response curve plots — the most approachable entry point. It’s the one I use most because it’s highly customizable.
Meridian (Google, Python/TensorFlow): Bayesian inference with geo-level priors and principled uncertainty quantification — more rigorous, with a steeper learning curve.
PyMC-Marketing (PyMC Labs, Python): The most flexible option, offering a full probabilistic model that’s closest to academic-grade Bayesian MMM — but it also requires the most statistical fluency.




This generation of tools has eliminated the $150,000 to $500,000 consulting gate that used to be the only path into MMM. Any team with R or Python expertise and relatively clean historical data can now run a model in-house.
The key caveat worth making explicit in any conversation with those exploring MMM is this: “Free tool” doesn’t mean “free model.” The software is free. The domain expertise required to configure it correctly — a hugely important part of the process — isn’t.
your competitors win.
      
Analyze your competitors
    
A crowded vendor landscape with an interesting power dynamic
The SaaS layer built on top of open-source MMM has proliferated quickly. It’s worth distinguishing a few tiers.
Data-layer-first vendors
Platforms like Rockerbox and Northbeam started as attribution and data collection platforms, then added MMM. Their edge is data pipelines and speed, not modeling depth or customization.
Measurement-first vendors
Platforms like Measured, Analytic Partners, Ekimetrics, and Nielsen Gracenote offer more rigorous modeling at a higher price point, with enterprise-grade capabilities.
Google Meridian and GA360
One point is worth calling out. Google’s open-sourcing of Meridian was a generous contribution to the field and, at the same time, a strategic one. When a walled garden funds and packages the measurement methodology used to evaluate its own channels, it’s worth maintaining healthy skepticism about model priors and default assumptions, even with transparent code.
The practical question when evaluating vendors is: who owns your data layer, and does that create conflicts in the modeling layer?
Challenge 1: Data access is the silent MMM killer
This is the most underappreciated implementation blocker, and it rarely gets the attention it deserves. A well-specified MMM needs:
Two to three years of weekly data as a baseline — enough to capture at least two full seasonality cycles and a meaningful range of spend variation.
Consistent channel-level spend granularity — not just “digital,” but search, social, display, and video broken out separately.
Offline channels (TV, OOH, radio, events, direct mail — which typically live in different systems) are owned by different teams, and often use incompatible time granularities.
External covariates — macro indicators, competitor activity, pricing data, and product launch calendars.
For B2B specifically, longer sales cycles and lower conversion volumes make the data requirements even more demanding. You often need more history.
In practice, what blocks most MMM projects is the six-week data archaeology exercise that comes before model building. Finance owns revenue. The brand team owns TV. The agency owns digital spend. The spreadsheet someone built in 2021 is the only record of trade promotions.
The model is only as good as the data archaeology that precedes it, and nobody tells you that in the vendor demo.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Challenge 2: You still need to roll up your sleeves
AI assistants have meaningfully lowered the syntax barrier. They can scaffold a Robyn run, generate a Meridian config, or help debug a PyMC model. What they can’t yet do is navigate the judgment calls that make an MMM trustworthy:
Choose where to sit on a Pareto frontier of hundreds of model solutions (NRMSE vs. DECOMP.RSSD tradeoffs).
Know when Nevergrad’s optimizer has meaningfully converged versus landed in a local minimum.
Configure adstock transformation parameters (Weibull shape/scale, geometric decay) to match realistic channel dynamics.
Diagnose why a model assigns an implausible contribution to a channel, and whether to address it with a prior, a data correction, or a variable exclusion.
In other words, vibe coding your way to an MMM will produce a model that appears to work but is wrong in ways you won’t catch. The scripting isn’t the hard part. The domain expertise required to validate the output includes running channel-specific incrementality experiments to calibrate your MMM.
Challenge 3: The human expertise layer isn’t optional
Even when the tooling matures to the point where AI can run a competent default MMM, the irreplaceable human contribution is encoding business context — things no model can infer from the data alone:
Adstock and carryover context: Your TV buy has a four-week carryover. Your paid search has a three-day carryover. Your branded awareness campaign has a decay that spans months. This information isn’t found in the data. It’s in the minds of the channel experts.
Saturation curve shape: Knowing a channel is likely approaching diminishing returns before the model tells you so, and questioning the results when the model suggests otherwise.
Guardrails and anomaly handling: Factors like COVID troughs, product launches, pricing shifts, and macro disruptions need to be modeled explicitly or flagged as structural breaks. AI doesn’t know your client had a pricing crisis in Q3 2022.
Interpretation sanity checks: A modeled TV contribution of 40% for a brand spending $2 million on TV may “feel wrong” and warrant investigation. That intuition is earned, not computed.
Organizational translation: The most technically correct model is worthless if you can’t explain why it recommends shifting 15% of the search budget to CTV in terms a CMO and CFO will act on.
Lay the groundwork before you build a model
The best place to begin is understanding what data you need to fuel the model and who needs to help contextualize and translate that data into effective marketing decisions. Neither is easy or fast, but both are essential if you want to get meaningful insights from your model, regardless of whether you choose an open-source or subscription-based platform.
A practical first step is to download Robyn’s demo script and experiment with the sample data before applying it to your own.

Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and human expertise.

Why marketing mix modeling is still hard to get right

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image.png" class="attachment-large size-large wp-post-image" alt="Stop saying ‘best practice’ and start bringing proof" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>SEO stops sounding like black magic when you can point to the exact Google doc that backs it up. Here's how to use it to earn buy-in.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image.png" class="attachment-large size-large wp-post-image" alt="Stop saying ‘best practice’ and start bringing proof" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Stop-saying-‘best-practice-and-start-bringing-proof-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Let’s get real. SEO advice often sounds completely made up. <em>There, I said it!</em>



<p>A lot of recommendations sound ridiculous to people who do not live in search every single day. <em>“Change this canonical.” “Don’t block that resource.” “We need this content exposed in the rendered HTML.”</em>



<p><strong>This is exactly why SEO still gets labeled as black magic in many organizations.</strong>



<p>I’ve been an advocate for “<em>un-nerding SEO</em>” for years, but that’s a different story. Today, we’re talking about something much more practical: using Google’s own documentation to win approval, build trust, and prioritize tasks for action.</p>



<p>Not because Google tells us everything. Not because every sentence should be treated as gospel. But because hard-documented resources are hard to blow off.</p>



<p>When you need buy-in, sometimes the best argument is not <em>“trust me.”</em>



<p>It’s <em>“Google already documented how we should approach this.”</em>



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



<h2 class="wp-block-heading" id="h-the-buy-in-problem-is-usually-not-the-recommendation-itself">The buy-in problem is usually not the recommendation itself</h2>



<p>Most SEO recommendations do not die because they are wrong. They die because they have to compete with everything else <a href="https://www.seoforlunch.com/p/ai-shortcut" target="_blank" rel="noopener">happening inside the business</a>.</p>



<p>Dev sprints, product timelines, CMS limitations, legal concerns, brand standards, executive assumptions, and the ever-popular… <em>(close your eyes!)</em> <em>“we’ve always done it this way”, it</em> all <a href="https://www.seoforlunch.com/p/its-time-to-invite-your-seo-to-the" target="_blank" rel="noopener">has a seat at the table.</a> SEO is rarely the only priority in the room, even when the recommendation is technically correct.</p>



<p>That is why <em>“best practice says”</em> or <a href="https://www.seoforlunch.com/p/old-hat-seo" target="_blank" rel="noopener"><em>“from an SEO perspective”</em></a> is simply not enough to close the deal. It sounds optional, especially to teams already managing risk, deadlines, and competing requests.</p>



<p>But <em>“Google has official documentation to guide us”</em> lands differently.</p>



<p>It may not automatically <em>win</em> the argument, and it definitely does not mean the recommendation gets prioritized tomorrow. But it does change the conversation from <em>“the SEO gal told us…”</em> to <em>“Suzzie provided us official Google documentation explaining why this matters.”</em>



<h2 class="wp-block-heading" id="h-google-documentation-is-not-gospel">Google documentation is not gospel</h2>



<p>I know what some of you are thinking.</p>



<p><em>“Nick, are we really pretending Google tells us the full truth about how search works?”</em>



<p><s>Fuck No.</s> I mean… absolutely not.</p>



<p>Google’s documentation is not the complete truth of search. It has omissions. It simplifies complicated systems. Sometimes it explains how <a href="https://www.seoforlunch.com/p/dear-google" target="_blank" rel="noopener">Google wants site owners to behave</a>, not necessarily every nerdy factor that goes into organic visibility.</p>



<p>Google also writes documentation for a very broad audience. That means nuance gets sanded down, edge cases get skipped, and the answer is often technically true without being the entire story. <em>(Want proof? Log in to X, and you’ll read SEOs with too much time on their hands, complaining and “calling out” Google for this)</em>



<p>So no, I am not suggesting we treat every Google statement as if it were carved into stone tablets and carried down from Mountain View.</p>



<p><strong>But that does not make the documentation useless.</strong>



<p>It makes it a starting point. A receipt. An official reference point.</p>



<p>It moves the conversation away from <em>“Nick thinks this matters”</em> and toward <em>“Google has explicitly documented why this matters.”</em> That distinction matters a lot when you’re trying to get someone else to approve (and prioritize) the work.</p>



<h2 class="wp-block-heading" id="h-documentation-is-especially-useful-with-developers">Documentation is especially useful with developers</h2>



<p>This is probably where Google documentation earns its keep the fastest. SEOs need developers, and the fastest way to lose their support is to treat every recommendation as a command rather than a requirement.</p>



<p>but… just in case this works…</p>



<p><em>google.exe /disable-ai-overviews /please</em>



<p>Bummer, no dice.</p>



<p>Developers are not wrong just because they disagree with an SEO recommendation. Most of the time, they are optimizing for completely different (but valid) things, such as performance, code quality, technical debt, security, and let’s not forget, simply not being the person who takes the entire website down with one wrong push to production.</p>



<p>But sometimes <em>they are wrong</em> about how Google discovers, crawls, renders, indexes, or interprets content.</p>



<p>And telling a developer <em>“you’re wrong”</em> is a great way to make sure your ticket never sees the light of day.</p>



<p>This is where Google documentation helps. It removes subjectivity on the purpose/value. It aligns the conversation on how best to implement within your own ecosystem, rather than the recommendation being “right vs wrong”</p>



<ul class="wp-block-list">
<li><a href="https://developers.google.com/search/docs/crawling-indexing/consolidate-duplicate-urls" target="_blank" rel="noopener">Canonical tags are signals, not directives</a>.</li>



<li><a href="https://developers.google.com/search/docs/crawling-indexing/robots/intro" target="_blank" rel="noopener">Robots.txt blocks crawling, not always indexing</a>.</li>



<li><a href="https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics" target="_blank" rel="noopener">JavaScript rendering has limitations.</a></li>



<li><a href="https://developers.google.com/search/docs/crawling-indexing/links-crawlable" target="_blank" rel="noopener">Internal links need to be discoverable</a>.</li>



<li><a href="https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data" target="_blank" rel="noopener">Structured data has eligibility requirements.</a></li>



<li><a href="https://developers.google.com/search/docs/crawling-indexing/301-redirects" target="_blank" rel="noopener">Why status codes matter and a 302 doesn’t <em>“do the same thing.”</em></a></li>
</ul>



<p><strong>The point is never </strong><strong><em>“SEO wins and dev loses.”</em></strong>



<p>The point is that you now have an external source of truth to discuss. That is a much better conversation than two teams arguing from preference.</p>



<h2 class="wp-block-heading" id="h-documentation-is-also-a-client-management-tool">Documentation is also a client management tool</h2>



<p>For client-facing SEOs, documentation helps separate serious recommendations from “<em>trust me, bro, I have a contact at Google</em>” consulting.</p>



<p>This is especially useful when a client has been burned by bad SEO advice before.</p>



<p>Instead of saying:</p>



<p>“We need to change this because it’s better for SEO.”</p>



<p>You can say:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“Here’s what Google documents. Here’s where your current setup conflicts with that. Here’s the risk. Here’s the recommendation. Here is the estimated reward.”</p>
</blockquote>



<p>That framing builds trust because it shows the recommendation is not reliant on 100% blind faith.</p>



<p>It also makes the SEO look less like a magician and more like an interpreter.</p>



<p>That is the real role of an SEO: translating Google’s needs from a website (confirmed via documentation) into business and technical decisions.</p>



<h2 class="wp-block-heading" id="h-less-black-magic-more-receipts">Less black magic, more receipts</h2>



<p>SEO has a reputation problem, and <a href="https://www.seoforlunch.com/p/ai-guarantees" target="_blank" rel="noopener">some of it is earned</a>.</p>



<p>Too much of our work is still explained in vague terminology with low confidence. We say things like <em>“Google likes this”</em> or <em>“this needs to exist for the bots”</em> when we should be saying, <em>“Google documents this behavior here, and here’s how it applies to our situation.”</em>



<p><strong>That does not mean documentation alone creates buy-in.</strong>



<p>Dropping a Google link into a ticket or Slack thread is not a strategy. You still have to translate what it means, explain the risk, connect it to business outcomes, and help the team understand why the recommendation deserves attention.</p>



<p>Google documentation will never replace experience, testing, or judgment. It will not tell us everything, and it should not be treated like the final answer to every SEO debate.</p>



<p>But it can make SEO easier to defend, easier to prioritize, and much harder for divisional leaders to dismiss.</p>



<p><a href="https://www.seoforlunch.com/p/mom-love" target="_blank" rel="noopener">The best SEOs</a> are not just the ones who know what to recommend. They are the ones who can prove why the recommendation deserves to be taken seriously.</p>



<p>Less black magic. More receipts. More results.</p>



<p>Google documentation may not be the whole truth, but it sure beats showing up to a buy-in conversation with “my buddy from Google told me….” Suuuure they did.</p>



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


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

Let’s get real. SEO advice often sounds completely made up. There, I said it!



A lot of recommendations sound ridiculous to people who do not live in search every single day. “Change this canonical.” “Don’t block that resource.” “We need this content exposed in the rendered HTML.”



This is exactly why SEO still gets labeled as black magic in many organizations.



I’ve been an advocate for “un-nerding SEO” for years, but that’s a different story. Today, we’re talking about something much more practical: using Google’s own documentation to win approval, build trust, and prioritize tasks for action.
Not because Google tells us everything. Not because every sentence should be treated as gospel. But because hard-documented resources are hard to blow off.
When you need buy-in, sometimes the best argument is not “trust me.”



It’s “Google already documented how we should approach this.”



View embedded content



The buy-in problem is usually not the recommendation itself
Most SEO recommendations do not die because they are wrong. They die because they have to compete with everything else happening inside the business.
Dev sprints, product timelines, CMS limitations, legal concerns, brand standards, executive assumptions, and the ever-popular… (close your eyes!) “we’ve always done it this way”, it all has a seat at the table. SEO is rarely the only priority in the room, even when the recommendation is technically correct.
That is why “best practice says” or “from an SEO perspective” is simply not enough to close the deal. It sounds optional, especially to teams already managing risk, deadlines, and competing requests.
But “Google has official documentation to guide us” lands differently.
It may not automatically win the argument, and it definitely does not mean the recommendation gets prioritized tomorrow. But it does change the conversation from “the SEO gal told us…” to “Suzzie provided us official Google documentation explaining why this matters.”



Google documentation is not gospel
I know what some of you are thinking.
“Nick, are we really pretending Google tells us the full truth about how search works?”



Fuck No. I mean… absolutely not.
Google’s documentation is not the complete truth of search. It has omissions. It simplifies complicated systems. Sometimes it explains how Google wants site owners to behave, not necessarily every nerdy factor that goes into organic visibility.
Google also writes documentation for a very broad audience. That means nuance gets sanded down, edge cases get skipped, and the answer is often technically true without being the entire story. (Want proof? Log in to X, and you’ll read SEOs with too much time on their hands, complaining and “calling out” Google for this)



So no, I am not suggesting we treat every Google statement as if it were carved into stone tablets and carried down from Mountain View.
But that does not make the documentation useless.



It makes it a starting point. A receipt. An official reference point.
It moves the conversation away from “Nick thinks this matters” and toward “Google has explicitly documented why this matters.” That distinction matters a lot when you’re trying to get someone else to approve (and prioritize) the work.
Documentation is especially useful with developers
This is probably where Google documentation earns its keep the fastest. SEOs need developers, and the fastest way to lose their support is to treat every recommendation as a command rather than a requirement.
but… just in case this works…
google.exe /disable-ai-overviews /please



Bummer, no dice.
Developers are not wrong just because they disagree with an SEO recommendation. Most of the time, they are optimizing for completely different (but valid) things, such as performance, code quality, technical debt, security, and let’s not forget, simply not being the person who takes the entire website down with one wrong push to production.
But sometimes they are wrong about how Google discovers, crawls, renders, indexes, or interprets content.
And telling a developer “you’re wrong” is a great way to make sure your ticket never sees the light of day.
This is where Google documentation helps. It removes subjectivity on the purpose/value. It aligns the conversation on how best to implement within your own ecosystem, rather than the recommendation being “right vs wrong”
Canonical tags are signals, not directives.
Robots.txt blocks crawling, not always indexing.
JavaScript rendering has limitations.
Internal links need to be discoverable.
Structured data has eligibility requirements.
Why status codes matter and a 302 doesn’t “do the same thing.”
The point is never “SEO wins and dev loses.”



The point is that you now have an external source of truth to discuss. That is a much better conversation than two teams arguing from preference.
Documentation is also a client management tool
For client-facing SEOs, documentation helps separate serious recommendations from “trust me, bro, I have a contact at Google” consulting.
This is especially useful when a client has been burned by bad SEO advice before.
Instead of saying:
“We need to change this because it’s better for SEO.”
You can say:
“Here’s what Google documents. Here’s where your current setup conflicts with that. Here’s the risk. Here’s the recommendation. Here is the estimated reward.”
That framing builds trust because it shows the recommendation is not reliant on 100% blind faith.
It also makes the SEO look less like a magician and more like an interpreter.
That is the real role of an SEO: translating Google’s needs from a website (confirmed via documentation) into business and technical decisions.
Less black magic, more receipts
SEO has a reputation problem, and some of it is earned.
Too much of our work is still explained in vague terminology with low confidence. We say things like “Google likes this” or “this needs to exist for the bots” when we should be saying, “Google documents this behavior here, and here’s how it applies to our situation.”



That does not mean documentation alone creates buy-in.



Dropping a Google link into a ticket or Slack thread is not a strategy. You still have to translate what it means, explain the risk, connect it to business outcomes, and help the team understand why the recommendation deserves attention.
Google documentation will never replace experience, testing, or judgment. It will not tell us everything, and it should not be treated like the final answer to every SEO debate.
But it can make SEO easier to defend, easier to prioritize, and much harder for divisional leaders to dismiss.
The best SEOs are not just the ones who know what to recommend. They are the ones who can prove why the recommendation deserves to be taken seriously.
Less black magic. More receipts. More results.
Google documentation may not be the whole truth, but it sure beats showing up to a buy-in conversation with “my buddy from Google told me….” Suuuure they did.
This post first appeared on the author’s website and is republished here with permission.

SEO stops sounding like black magic when you can point to the exact Google doc that backs it up. Here's how to use it to earn buy-in.

Stop saying ‘best practice’ and start bringing proof

<div><img width="800" height="505" src="https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-800x505.jpg" class="attachment-large size-large wp-post-image" alt="google merchant center" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-800x505.jpg 800w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-535x338.jpg 535w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-179x113.jpg 179w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-768x485.jpg 768w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-1536x970.jpg 1536w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-150x95.jpg 150w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1.jpg 1900w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google Merchant Center Next is now know as just Google Merchant Center.

<div><img width="800" height="505" src="https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-800x505.jpg" class="attachment-large size-large wp-post-image" alt="google merchant center" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-800x505.jpg 800w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-535x338.jpg 535w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-179x113.jpg 179w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-768x485.jpg 768w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-1536x970.jpg 1536w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1-150x95.jpg 150w, https://searchengineland.com/wp-content/seloads/2020/10/google-merchant-center-screen-SS-1175461114-1920x1080-1.jpg 1900w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google has dropped &#8220;Next&#8221; from Merchant Center Next and it will now be known as just Google Merchant Center. &#8220;The platform you use today will simply be referred to as Google Merchant Center,&#8221; Google <a href="https://support.google.com/merchants/answer/17252069">announced</a>.</p>



<p><strong>Merchant Center Next. </strong><a href="https://searchengineland.com/google-merchant-center-next-replace-merchant-center-427437">Google Merchant Center Next</a> was the new version of the old Google Merchant Center, which was announced in 2023. Over the past few years, more and more merchants, site owners and advertisers moved from the old Google Merchant Center to Merchant Center Next. </p>



<p>At this point, it seems everyone is on Merchant Center Next, so Google has decided to drop &#8220;Next&#8221; from the name and just give it the old name back of just Merchant Center.</p>



<p><strong>What Google said. </strong>Google wrote:</p>



<ul class="wp-block-list">
<li>&#8220;You&#8217;ll begin to notice the &#8220;Next&#8221; branding removed from our Help Center articles, email communications, and the Merchant Center interface.&#8221; </li>



<li>&#8220;The platform you use today will simply be referred to as Google Merchant Center.&#8221;</li>



<li>&#8220;No action is required. This name change doesn&#8217;t affect your account&#8221;<br></li>
</ul>



<p><strong>Why we care. </strong>When you are talking about Google Merchant Center, maybe now leave off &#8220;Next&#8221; at the end. Truth is, most of us have just been calling this Google Merchant Center without the next.</p>


Google has dropped “Next” from Merchant Center Next and it will now be known as just Google Merchant Center. “The platform you use today will simply be referred to as Google Merchant Center,” Google announced.
Merchant Center Next. Google Merchant Center Next was the new version of the old Google Merchant Center, which was announced in 2023. Over the past few years, more and more merchants, site owners and advertisers moved from the old Google Merchant Center to Merchant Center Next. 
At this point, it seems everyone is on Merchant Center Next, so Google has decided to drop “Next” from the name and just give it the old name back of just Merchant Center.
What Google said. Google wrote:
“You’ll begin to notice the “Next” branding removed from our Help Center articles, email communications, and the Merchant Center interface.” 
“The platform you use today will simply be referred to as Google Merchant Center.”
“No action is required. This name change doesn’t affect your account”





Why we care. When you are talking about Google Merchant Center, maybe now leave off “Next” at the end. Truth is, most of us have just been calling this Google Merchant Center without the next.

Google Merchant Center Next is now know as just Google Merchant Center.

Google drops Next from Merchant Center Next

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>One version of this story is AI replacing your entire PPC team. The truer one: Humans direct, the machines execute, and then the numbers move.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature.jpg" alt="" class="wp-image-481737" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Groas-20260709-feature-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p>Paid search has changed. Auction dynamics shift by the hour, competitor bids adjust in real time, and search behavior varies across devices, times of day, and audience segments in ways monthly reporting cycles weren’t built to capture.&nbsp;</p>



<p>The gap between a performance signal and a human response has always carried a cost. groas closes that gap every hour of every day. Here’s what the data showed when it did.</p>



<p>When you sign up with <a href="https://www.groas.com/" target="_blank" rel="noopener sponsored nofollow">groas</a>, the process starts with a human account manager conducting a full audit of your existing Google Ads account. This isn’t a surface-level review — campaign structure, keyword strategy, bidding logic, budget allocation, conversion tracking, quality scores, search term reports, and auction insights all come under scrutiny.</p>



<p>The quality of that audit shapes everything that follows. groas’s system optimizes toward the goals and structure defined in the roadmap, so a well-structured account with a clear conversion hierarchy and accurate tracking gives it stronger signals to work with. That’s why the human work at the start matters so much: it forms the foundation for everything that follows.</p>



<p>From there, the rollout is paced over the first 60 days, so nothing moves before the system understands the account it’s working in.</p>



<ul class="wp-block-list">
<li><strong>Weeks 1 to 2 — Observation:</strong> groas ingests historical performance data, establishes baselines, and maps patterns across search terms, device performance, time-of-day variance, and audience behavior. No changes are made while the system learns the account.</li>



<li><strong>Weeks 3 to 4 — Calibration:</strong> The system begins targeted optimizations: bid adjustments, negative keyword additions, match type refinements, and budget reallocation between campaigns. These are deliberate interventions, working campaign by campaign so each change builds on the last.</li>



<li><strong>Weeks 5 to 6 — Traction:</strong> Early changes start to show. Performance shifts become visible across ROAS, conversion value, and wasted spend as the optimizations compound.</li>



<li><strong>Weeks 7 to 8 — Scaling:</strong> Around the 60-day mark, the account has stabilized, and groas begins scaling: pushing more budget into the campaigns and keywords with the strongest conversion history and expanding from a proven base.</li>
</ul>



<p>Once running, groas works across the full account the way a skilled team would, except it never stops. It writes and tests ad copy, deploys dynamic landing pages that reshape around each search, turns ad groups on and off as performance dictates, moves budget where it earns the most, and adjusts bidding strategies in response to live signals.&nbsp;</p>



<p>Anything a person can do inside Google Ads, groas can do, too — around the clock.</p>



<p>Capability is one thing; results are another.&nbsp;</p>



<p>The clearest way to show what continuous, full-surface management is worth is to follow one real account groas took over: a high-spend search account in a notoriously unforgiving corner of paid search. Specifically, it was a U.S.-based online mobile recharge platform that lets people instantly top up prepaid mobile phones across major U.S. carriers, without creating an account or paying added transaction fees. It operates in prepaid wireless, serving a large base of pay-as-you-go and underbanked customers who recharge monthly or more often, usually when their balance runs out.</p>



<p>That model puts Google Ads at the center of growth.&nbsp;</p>



<p>Demand is overwhelmingly intent-driven: when someone’s credit runs out, they search for a way to recharge and buy within minutes. Capturing that moment is the whole game. But it’s a punishing channel to run profitably: transactions are low-value and high-volume, margins are thin, and a crowded auction full of carrier brand terms and generic “recharge” and “top up” queries keeps click costs under constant pressure.&nbsp;</p>



<p>A few cents of wasted CPC, multiplied across hundreds of conversions a day, can be the difference between a profitable account and one that quietly bleeds.</p>



<p>In this account, a conversion is a completed recharge. The figures that follow aren’t abstract: every point of ROAS and every extra conversion per day means more recharges processed and more revenue moving through the platform on the same budget.</p>



<p>The comparison below draws on two account reporting periods: before groas assumed optimization and after.</p>



<ul class="wp-block-list">
<li><strong>Spend:</strong> Up 18% to $164,000</li>



<li><strong>ROAS:</strong> Up 30%</li>



<li><strong>Average CPC:</strong> Down 15%</li>



<li><strong>Conversions per day:</strong> Up 29%</li>



<li><strong>Conversion value:</strong> Up 44%</li>



<li><strong>Cost per conversion:</strong> Down 14%</li>
</ul>



<p>The clearest gain was return on ad spend. ROAS rose from 1.02x to 1.32x, roughly a 30% improvement in value returned for each dollar spent.</p>



<p>At the same time, average cost per click fell from $2.34 to $2. More important than click cost was how much more the account did with the clicks it paid for. Conversions and conversion value both grew faster than spend, so each dollar worked harder than it had under the previous setup.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1532" height="317" src="https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-1.png" alt="" class="wp-image-481741" srcset="https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-1.png 1532w, https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-1-768x159.png 768w" sizes="(max-width: 1532px) 100vw, 1532px" /></figure>
</div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1532" height="317" src="https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-2v2.jpg" alt="" class="wp-image-481743" srcset="https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-2v2.jpg 1532w, https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-2v2-768x159.jpg 768w" sizes="(max-width: 1532px) 100vw, 1532px" /></figure>
</div>


<p>Daily conversions rose from 571 to 739, around 29%. Daily conversion value rose faster, from $4,702 to $6,772, roughly 44%.&nbsp;</p>



<p>What makes this notable is that the gains came through consolidation, not expansion. groas focused spend into 10 active search campaigns, down from 17.&nbsp;</p>



<p>Budget spread thinly across underperforming campaigns was redirected into the keywords and campaigns with the strongest conversion history. Fewer campaigns, lower click costs, more value returned.&nbsp;</p>



<p>That’s the shape of an account with waste removed and budget focused.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1494" height="350" src="https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-3.png" alt="" class="wp-image-481744" srcset="https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-3.png 1494w, https://searchengineland.com/wp-content/seloads/2026/07/groas-20260709-3-768x180.png 768w" sizes="(max-width: 1494px) 100vw, 1494px" /></figure>
</div>


<p>The mechanism behind results like these is speed and breadth of attention. Under traditional management tied to weekly or monthly reporting cycles, an underperforming search term might run for 7 to 14 days before anyone acts, and a target CPA can drift far from its goal between reviews. An autonomous system narrows the gap between a performance signal and a response to hours, and watches every campaign at once instead of a few per day.</p>



<p>As groas accumulates more data on audience behavior, search patterns, and conversion value, its decisions become more precise and budget concentrates further into the highest-returning campaigns.</p>



<p>This is the structural difference between autonomous management and periodic manual review. Each optimization generates new data, and that data informs the next decision. A system running continuous observe-and-optimize cycles draws more signal from the same account over time.</p>



<p>Business context stays with the people who understand the business. When a client launches a new product line, repositions on price, or changes which conversions matter, that information has to come from a person. groas optimizes toward the goal it’s given, and setting that goal is strategic work.</p>



<p>Creative shows how the two layers work together. groas writes and tests ad copy and landing page variations at a volume and pace no human team could match, while the people on the account set the brand voice, positioning, and creative direction the system executes against. The strategist shapes the message; groas finds, at machine speed, the specific combinations of wording and layout that convert.</p>



<h2 class="wp-block-heading" id="h-for-businesses-ready-to-see-better-results">For businesses ready to see better results</h2>



<p>If your current setup runs on monthly reports and weekly changes, there’s a consistent gap between what the data says and what actually happens in the account. That gap is where budget gets wasted and opportunities close. In the account above, it showed up as more than 15 active search campaigns, many spending inefficiently, with budget spread too thin to compound.</p>



<p>groas’s onboarding is structured to keep the transition low-risk. The first two weeks are analysis only, measured changes follow, and meaningful performance shifts usually appear within the first month or two, with scaling beginning around day 60. Live campaigns keep running throughout calibration, and the initial audit grounds changes in context from the start.</p>



<p>For businesses that have stayed with the same agency for a long time without material improvement, the audit alone often surfaces things that have gone unaddressed.</p>



<p>Get started <a href="https://www.groas.com/google-ads" target="_blank" rel="noopener sponsored nofollow">here</a>.</p>



<h2 class="wp-block-heading" id="h-for-agencies-running-groas-white-label">For agencies running groas white-label</h2>



<p>Execution-layer account management doesn’t scale well.&nbsp;</p>



<p>Continuous optimization, bid management, negative keyword maintenance, and budget pacing are time-intensive at volume. As you add clients, you either add headcount or accept that some accounts get less attention than others — and most agencies know exactly which ones are underserved.</p>



<p>With groas handling execution autonomously across a client portfolio, your team can shift toward strategy, client relationships, and new business.&nbsp;</p>



<p>The work that differentiates an agency is also the hardest to automate. Clients see better results, and team capacity goes where it creates the most value.</p>



<p>Get started <a href="https://www.groas.com/white-label-google-ads" target="_blank" rel="noopener sponsored nofollow">here</a>.</p>


Paid search has changed. Auction dynamics shift by the hour, competitor bids adjust in real time, and search behavior varies across devices, times of day, and audience segments in ways monthly reporting cycles weren’t built to capture. 
The gap between a performance signal and a human response has always carried a cost. groas closes that gap every hour of every day. Here’s what the data showed when it did.
When you sign up with groas, the process starts with a human account manager conducting a full audit of your existing Google Ads account. This isn’t a surface-level review — campaign structure, keyword strategy, bidding logic, budget allocation, conversion tracking, quality scores, search term reports, and auction insights all come under scrutiny.
The quality of that audit shapes everything that follows. groas’s system optimizes toward the goals and structure defined in the roadmap, so a well-structured account with a clear conversion hierarchy and accurate tracking gives it stronger signals to work with. That’s why the human work at the start matters so much: it forms the foundation for everything that follows.
From there, the rollout is paced over the first 60 days, so nothing moves before the system understands the account it’s working in.
Weeks 1 to 2 — Observation: groas ingests historical performance data, establishes baselines, and maps patterns across search terms, device performance, time-of-day variance, and audience behavior. No changes are made while the system learns the account.
Weeks 3 to 4 — Calibration: The system begins targeted optimizations: bid adjustments, negative keyword additions, match type refinements, and budget reallocation between campaigns. These are deliberate interventions, working campaign by campaign so each change builds on the last.
Weeks 5 to 6 — Traction: Early changes start to show. Performance shifts become visible across ROAS, conversion value, and wasted spend as the optimizations compound.
Weeks 7 to 8 — Scaling: Around the 60-day mark, the account has stabilized, and groas begins scaling: pushing more budget into the campaigns and keywords with the strongest conversion history and expanding from a proven base.
Once running, groas works across the full account the way a skilled team would, except it never stops. It writes and tests ad copy, deploys dynamic landing pages that reshape around each search, turns ad groups on and off as performance dictates, moves budget where it earns the most, and adjusts bidding strategies in response to live signals. 
Anything a person can do inside Google Ads, groas can do, too — around the clock.
Capability is one thing; results are another. 
The clearest way to show what continuous, full-surface management is worth is to follow one real account groas took over: a high-spend search account in a notoriously unforgiving corner of paid search. Specifically, it was a U.S.-based online mobile recharge platform that lets people instantly top up prepaid mobile phones across major U.S. carriers, without creating an account or paying added transaction fees. It operates in prepaid wireless, serving a large base of pay-as-you-go and underbanked customers who recharge monthly or more often, usually when their balance runs out.
That model puts Google Ads at the center of growth. 
Demand is overwhelmingly intent-driven: when someone’s credit runs out, they search for a way to recharge and buy within minutes. Capturing that moment is the whole game. But it’s a punishing channel to run profitably: transactions are low-value and high-volume, margins are thin, and a crowded auction full of carrier brand terms and generic “recharge” and “top up” queries keeps click costs under constant pressure. 
A few cents of wasted CPC, multiplied across hundreds of conversions a day, can be the difference between a profitable account and one that quietly bleeds.
In this account, a conversion is a completed recharge. The figures that follow aren’t abstract: every point of ROAS and every extra conversion per day means more recharges processed and more revenue moving through the platform on the same budget.
The comparison below draws on two account reporting periods: before groas assumed optimization and after.
Spend: Up 18% to $164,000
ROAS: Up 30%
Average CPC: Down 15%
Conversions per day: Up 29%
Conversion value: Up 44%
Cost per conversion: Down 14%
The clearest gain was return on ad spend. ROAS rose from 1.02x to 1.32x, roughly a 30% improvement in value returned for each dollar spent.
At the same time, average cost per click fell from $2.34 to $2. More important than click cost was how much more the account did with the clicks it paid for. Conversions and conversion value both grew faster than spend, so each dollar worked harder than it had under the previous setup.








Daily conversions rose from 571 to 739, around 29%. Daily conversion value rose faster, from $4,702 to $6,772, roughly 44%. 
What makes this notable is that the gains came through consolidation, not expansion. groas focused spend into 10 active search campaigns, down from 17. 
Budget spread thinly across underperforming campaigns was redirected into the keywords and campaigns with the strongest conversion history. Fewer campaigns, lower click costs, more value returned. 
That’s the shape of an account with waste removed and budget focused.




The mechanism behind results like these is speed and breadth of attention. Under traditional management tied to weekly or monthly reporting cycles, an underperforming search term might run for 7 to 14 days before anyone acts, and a target CPA can drift far from its goal between reviews. An autonomous system narrows the gap between a performance signal and a response to hours, and watches every campaign at once instead of a few per day.
As groas accumulates more data on audience behavior, search patterns, and conversion value, its decisions become more precise and budget concentrates further into the highest-returning campaigns.
This is the structural difference between autonomous management and periodic manual review. Each optimization generates new data, and that data informs the next decision. A system running continuous observe-and-optimize cycles draws more signal from the same account over time.
Business context stays with the people who understand the business. When a client launches a new product line, repositions on price, or changes which conversions matter, that information has to come from a person. groas optimizes toward the goal it’s given, and setting that goal is strategic work.
Creative shows how the two layers work together. groas writes and tests ad copy and landing page variations at a volume and pace no human team could match, while the people on the account set the brand voice, positioning, and creative direction the system executes against. The strategist shapes the message; groas finds, at machine speed, the specific combinations of wording and layout that convert.
For businesses ready to see better results
If your current setup runs on monthly reports and weekly changes, there’s a consistent gap between what the data says and what actually happens in the account. That gap is where budget gets wasted and opportunities close. In the account above, it showed up as more than 15 active search campaigns, many spending inefficiently, with budget spread too thin to compound.
groas’s onboarding is structured to keep the transition low-risk. The first two weeks are analysis only, measured changes follow, and meaningful performance shifts usually appear within the first month or two, with scaling beginning around day 60. Live campaigns keep running throughout calibration, and the initial audit grounds changes in context from the start.
For businesses that have stayed with the same agency for a long time without material improvement, the audit alone often surfaces things that have gone unaddressed.
Get started here.
For agencies running groas white-label
Execution-layer account management doesn’t scale well. 
Continuous optimization, bid management, negative keyword maintenance, and budget pacing are time-intensive at volume. As you add clients, you either add headcount or accept that some accounts get less attention than others — and most agencies know exactly which ones are underserved.
With groas handling execution autonomously across a client portfolio, your team can shift toward strategy, client relationships, and new business. 
The work that differentiates an agency is also the hardest to automate. Clients see better results, and team capacity goes where it creates the most value.
Get started here.

One version of this story is AI replacing your entire PPC team. The truer one: Humans direct, the machines execute, and then the numbers move.

Human vs. machine: What happens when groas runs your Google Ads by groas

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png 1920w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>If you’re on top of the latest techniques and what’s to come in SEO, PPC, and AI search, submit a pitch to present at SMX Next.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png 1920w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><strong>SMX Next returns online Nov. 18</strong>, focusing on navigating today’s complex search landscape and mastering the tactics that will define success in 2027 and beyond.</p>



<p>Search marketing isn&#8217;t just changing; it has evolved into an entirely new beast. </p>



<ul class="wp-block-list">
<li>SEO has shifted to mastering AI Overviews, search everywhere optimization, and consideration for autonomous AI agents that browse on behalf of users. Demonstrating trustworthiness, digital authority, and precise alignment with user intent is even more important now.</li>



<li>On the PPC side, generative AI and deep automation are driving unprecedented personalization, but marketers are facing new challenges: How do you maintain strategic control, preserve data privacy, and prevent waste?</li>
</ul>



<p>If you’re an enthusiastic search marketer who has a passion for sharing knowledge please consider submitting a session pitch for SMX Next. We’re looking for subject matter experts to share insights, strategies, and tactics to help SEO and PPC marketers thrive in 2027.</p>



<p>Whether you’ve been speaking for years or you are a practitioner ready to share something new you&#8217;ve developed, we welcome your voice. We are actively seeking new speakers with diverse points of view and real-world experience.</p>



<p><strong>The deadline for SMX Next pitches is Aug. 7.</strong>



<p>Here are a few tips for submitting a compelling session proposal:</p>



<ul class="wp-block-list">
<li><strong>Present an original idea:</strong> Focus on advanced, forward-thinking topics or unique frameworks not commonly covered at other search events.</li>



<li><strong>Focus on actionability:</strong> Include explicit details about what attendees will be able to do better, faster, or differently as a result of your session.</li>



<li><strong>Bring the data:</strong> Back up your theories with a case study or specific examples, explaining how they can be scaled across different types of organizations.</li>



<li><strong>Keep it focused:</strong> Be realistic about what you can present in 30 minutes. Going deep on a narrow or specialized topic provides much more value than a broad overview.</li>



<li><strong>Provide tangible takeaways:</strong> Ensure attendees walk away with a clear action plan or framework they can implement immediately.</li>
</ul>



<p>Jump over to this <a href="https://marketinglandevents.com/smx/speaking/">page</a> for more details on how to submit a session idea, or directly to this <a href="https://thirddoorevents.com/dashboard">page</a> to create your profile and submit a session pitch.</p>



<p>If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!</p>


SMX Next returns online Nov. 18, focusing on navigating today’s complex search landscape and mastering the tactics that will define success in 2027 and beyond.
Search marketing isn’t just changing; it has evolved into an entirely new beast. 
SEO has shifted to mastering AI Overviews, search everywhere optimization, and consideration for autonomous AI agents that browse on behalf of users. Demonstrating trustworthiness, digital authority, and precise alignment with user intent is even more important now.
On the PPC side, generative AI and deep automation are driving unprecedented personalization, but marketers are facing new challenges: How do you maintain strategic control, preserve data privacy, and prevent waste?
If you’re an enthusiastic search marketer who has a passion for sharing knowledge please consider submitting a session pitch for SMX Next. We’re looking for subject matter experts to share insights, strategies, and tactics to help SEO and PPC marketers thrive in 2027.
Whether you’ve been speaking for years or you are a practitioner ready to share something new you’ve developed, we welcome your voice. We are actively seeking new speakers with diverse points of view and real-world experience.
The deadline for SMX Next pitches is Aug. 7.



Here are a few tips for submitting a compelling session proposal:
Present an original idea: Focus on advanced, forward-thinking topics or unique frameworks not commonly covered at other search events.
Focus on actionability: Include explicit details about what attendees will be able to do better, faster, or differently as a result of your session.
Bring the data: Back up your theories with a case study or specific examples, explaining how they can be scaled across different types of organizations.
Keep it focused: Be realistic about what you can present in 30 minutes. Going deep on a narrow or specialized topic provides much more value than a broad overview.
Provide tangible takeaways: Ensure attendees walk away with a clear action plan or framework they can implement immediately.
Jump over to this page for more details on how to submit a session idea, or directly to this page to create your profile and submit a session pitch.
If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!

If you’re on top of the latest techniques and what’s to come in SEO, PPC, and AI search, submit a pitch to present at SMX Next.

Want to present at SMX Next? Now’s the time to submit a pitch!

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png" class="attachment-large size-large wp-post-image" alt="ChatGPT search sources" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Two analyses found ChatGPT’s hidden search systems can change its sources, making citation tracking harder than answers suggest.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png" class="attachment-large size-large wp-post-image" alt="ChatGPT search sources" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Two new analyses found that ChatGPT’s cited sources changed as its search traffic shifted between hidden retrieval pipelines.</p>



<p><strong>The findings.</strong> Research from <a href="https://chrisgreenseo.substack.com/p/what-can-we-learn-from-chatgpt-search" target="_blank" rel="noopener">Chris Green</a> and <a href="https://suganthan.com/blog/how-chatgpt-picks-sources/" target="_blank" rel="noopener">Suganthan Mohanadasan</a> adds a new complication to AI visibility tracking: the final answer doesn’t reveal how ChatGPT selected its sources.</p>



<ul class="wp-block-list">
<li>Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP. </li>



<li>Those labels sit behind the answer, not in the citation cards users see.</li>
</ul>



<p><strong>Hidden pipelines changed sources</strong>. Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. Most prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.</p>



<ul class="wp-block-list">
<li>But 11.6% of prompts changed primary search source across repeated runs.</li>



<li>URL overlap dropped from 0.273 to 0.149 when the search source changed. Domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.</li>
</ul>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        Be the brand <span>AI recommends</span>.
      </div>
      <p id="semrush-one-subhead">
        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>




<p><strong>The labels behind results</strong>. Mohanadasan inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches.</p>



<ul class="wp-block-list">
<li>He found a <code>result_source</code> field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.</li>



<li>He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results.</li>



<li>Green’s repeated-prompt test found Labrador dominated his dataset. Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.</li>
</ul>



<p><strong>Some prompts skipped search</strong>. Mohanadasan also found that ChatGPT classified some queries before searching, using a <code>turn_use_case</code> field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.</p>



<ul class="wp-block-list">
<li>More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could fan out into many searches, including <code>site:</code> probes, pricing checks, and searches for unnamed competitors.</li>



<li>That behavior changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.</li>
</ul>



<p><strong>Fetched was not cited</strong>. Mohanadasan separated three outcomes: fetched, cited and mentioned.</p>



<ul class="wp-block-list">
<li>A page can be fetched into ChatGPT’s context without being shown to users. It can be cited as the source behind a specific sentence. Or a brand can be mentioned without being the source of the claim.</li>



<li>In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed the gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts.</li>



<li>Vendor pages were cited for their own facts, such as prices and specs. Third-party pages were more likely to support broader recommendation claims.</li>
</ul>



<p><strong>Why we care</strong>. There’s no single ChatGPT visibility result to measure. Your page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.</p>



<p><strong>Readable pages were easier to use</strong>. Both analyses showed that ChatGPT’s source selection depended partly on what it could retrieve and read.</p>



<ul class="wp-block-list">
<li>Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.</li>



<li>Green’s results showed that source routing changed which URLs and domains entered the answer set.</li>



<li>Plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages became more important when source selection depended on retrieval and readability.</li>
</ul>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
      </div>
      <p id="semrush-one-subhead">
       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>



Two new analyses found that ChatGPT’s cited sources changed as its search traffic shifted between hidden retrieval pipelines.
The findings. Research from Chris Green and Suganthan Mohanadasan adds a new complication to AI visibility tracking: the final answer doesn’t reveal how ChatGPT selected its sources.
Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP. 
Those labels sit behind the answer, not in the citation cards users see.
Hidden pipelines changed sources. Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. Most prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.
But 11.6% of prompts changed primary search source across repeated runs.
URL overlap dropped from 0.273 to 0.149 when the search source changed. Domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.
AI recommends.
      
See your AI visibility
    
The labels behind results. Mohanadasan inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches.
He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.
He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results.
Green’s repeated-prompt test found Labrador dominated his dataset. Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.
Some prompts skipped search. Mohanadasan also found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.
More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could fan out into many searches, including site: probes, pricing checks, and searches for unnamed competitors.
That behavior changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.
Fetched was not cited. Mohanadasan separated three outcomes: fetched, cited and mentioned.
A page can be fetched into ChatGPT’s context without being shown to users. It can be cited as the source behind a specific sentence. Or a brand can be mentioned without being the source of the claim.
In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed the gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts.
Vendor pages were cited for their own facts, such as prices and specs. Third-party pages were more likely to support broader recommendation claims.
Why we care. There’s no single ChatGPT visibility result to measure. Your page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.
Readable pages were easier to use. Both analyses showed that ChatGPT’s source selection depended partly on what it could retrieve and read.
Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.
Green’s results showed that source routing changed which URLs and domains entered the answer set.
Plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages became more important when source selection depended on retrieval and readability.
customers won’t either.
      
See your AI visibility

Two analyses found ChatGPT’s hidden search systems can change its sources, making citation tracking harder than answers suggest.

ChatGPT citations change when hidden search pipelines switch

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png" class="attachment-large size-large wp-post-image" alt="The Google Ads mistake that’s draining your budget" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Learn what Search Partners are, why they usually produce junk traffic, and how to see if you're spending money on these placements.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png" class="attachment-large size-large wp-post-image" alt="The Google Ads mistake that’s draining your budget" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-9-16 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/gEP-g00gzdU?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>







<p>If you’re running Google Ads in 2026, you’ve probably heard of “Search Partners.” It’s often checked by default in your campaign settings, promising to extend your reach beyond Google Search. Sounds like an easy way to get more traffic, right?</p>



<p>Traffic, yes. Quality traffic? Not so much. </p>



<p>Most advertisers shouldn’t advertise on Search Partners. Read on to learn why and what you should do instead.</p>



<h2 class="wp-block-heading" id="h-what-are-google-search-partners">What are Google Search Partners?</h2>



<p>Google Search Partners are third-party sites with search results powered by Google. When someone searches on those sites, your ad can appear. This includes YouTube, along with a massive list of other sites, directories, and even parked domains.</p>



<p>As you might imagine — or may have experienced firsthand — the traffic from these partner sites is often poor. You may get plenty of clicks, and the cost per click (CPC) is often much cheaper than Google Search, but you usually get what you pay for. Real conversions or meaningful business value from these placements are rare.</p>



<p>If you’re using conversion-focused Smart Bidding, you’ll often see Search Partners spend naturally drop to zero over time. Your bidding algorithm figures out it isn’t finding conversions there, so it stops spending your budget on those sites.</p>



<h2 class="wp-block-heading" id="h-how-are-google-search-partners-different-from-the-google-display-network">How are Google Search Partners different from the Google Display Network?</h2>



<p>People often confuse Search Partners with the Display Network. Websites can be part of both, so let’s clarify the difference.</p>



<p>The <a href="https://searchengineland.com/google-display-network-explained-456796">Google Display Network</a> is a collection of millions of websites and apps that use AdSense. You can show ads to users while they browse content. The GDN is a placement option in Demand Gen, Video, where it’s called “Video Partners,” and PMax campaigns.</p>



<p>Search Partners are specifically for search-based intent. That’s why they’re only compatible with Search, Shopping, and Performance Max campaigns.</p>



<h2 class="wp-block-heading" id="h-how-to-audit-your-search-partner-performance">How to audit your Search Partner performance</h2>



<p>Don’t take my word for it. See what Search Partners are doing in your Google Ads campaigns.</p>



<h3 class="wp-block-heading" id="h-for-search-or-shopping-campaigns">For Search or Shopping campaigns</h3>



<p>In your Google Ads account, go to your campaign view and click <em>Segment</em>. Choose <em>Network (with search partners)</em>. This breaks performance into two rows: <em>Google Search</em> and <em>Search Partners.</em>



<p>You’ll typically see plenty of impressions and clicks from Search Partners, with lower CPCs than Google Search. Conversions? Only if you’re tracking something shallow or bot-susceptible, like a page view or easy form fill.</p>



<h3 class="wp-block-heading" id="h-for-performance-max-campaigns">For Performance Max campaigns</h3>



<p>Performance Max is different. Search Partners are required for this campaign type; you can’t opt out. However, you can still monitor activity through your Channel Performance report.</p>



<p>If you see high Search Partners spend in a PMax campaign, it often indicates an issue with your conversion tracking or bid strategy settings.</p>



<h3 class="wp-block-heading" id="h-search-partner-transparency-the-content-suitability-report">Search Partner transparency: The Content Suitability report</h3>



<p>For more transparency, check the Content Suitability report under <em>Insights and reports.</em> It shows the actual websites or YouTube channels where your ads appeared on the Search Partner network. Review that list, and you’ll likely agree that much of it is junk and spam.</p>



<p>In Google Ads, the answer to almost every question is “it depends.” This is one of the few exceptions.</p>



<p>If you’re building a new Search or Shopping campaign, leave Search Partners unchecked. Once the campaign performs well and you have solid conversion data, you can consider testing Search Partners for more volume. Until then, keep your ads on the main Google SERP.</p>



<p><em>This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.</em>






Watch this video on YouTube








If you’re running Google Ads in 2026, you’ve probably heard of “Search Partners.” It’s often checked by default in your campaign settings, promising to extend your reach beyond Google Search. Sounds like an easy way to get more traffic, right?
Traffic, yes. Quality traffic? Not so much. 
Most advertisers shouldn’t advertise on Search Partners. Read on to learn why and what you should do instead.
What are Google Search Partners?
Google Search Partners are third-party sites with search results powered by Google. When someone searches on those sites, your ad can appear. This includes YouTube, along with a massive list of other sites, directories, and even parked domains.
As you might imagine — or may have experienced firsthand — the traffic from these partner sites is often poor. You may get plenty of clicks, and the cost per click (CPC) is often much cheaper than Google Search, but you usually get what you pay for. Real conversions or meaningful business value from these placements are rare.
If you’re using conversion-focused Smart Bidding, you’ll often see Search Partners spend naturally drop to zero over time. Your bidding algorithm figures out it isn’t finding conversions there, so it stops spending your budget on those sites.
How are Google Search Partners different from the Google Display Network?
People often confuse Search Partners with the Display Network. Websites can be part of both, so let’s clarify the difference.
The Google Display Network is a collection of millions of websites and apps that use AdSense. You can show ads to users while they browse content. The GDN is a placement option in Demand Gen, Video, where it’s called “Video Partners,” and PMax campaigns.
Search Partners are specifically for search-based intent. That’s why they’re only compatible with Search, Shopping, and Performance Max campaigns.
How to audit your Search Partner performance
Don’t take my word for it. See what Search Partners are doing in your Google Ads campaigns.
For Search or Shopping campaigns
In your Google Ads account, go to your campaign view and click Segment. Choose Network (with search partners). This breaks performance into two rows: Google Search and Search Partners.



You’ll typically see plenty of impressions and clicks from Search Partners, with lower CPCs than Google Search. Conversions? Only if you’re tracking something shallow or bot-susceptible, like a page view or easy form fill.
For Performance Max campaigns
Performance Max is different. Search Partners are required for this campaign type; you can’t opt out. However, you can still monitor activity through your Channel Performance report.
If you see high Search Partners spend in a PMax campaign, it often indicates an issue with your conversion tracking or bid strategy settings.
Search Partner transparency: The Content Suitability report
For more transparency, check the Content Suitability report under Insights and reports. It shows the actual websites or YouTube channels where your ads appeared on the Search Partner network. Review that list, and you’ll likely agree that much of it is junk and spam.
In Google Ads, the answer to almost every question is “it depends.” This is one of the few exceptions.
If you’re building a new Search or Shopping campaign, leave Search Partners unchecked. Once the campaign performs well and you have solid conversion data, you can consider testing Search Partners for more volume. Until then, keep your ads on the main Google SERP.
This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.

Learn what Search Partners are, why they usually produce junk traffic, and how to see if you're spending money on these placements.

Why you should opt out of Google Search Partners

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png" class="attachment-large size-large wp-post-image" alt="Why most original data never gets cited - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Primary research is rare, but it earns 3.3x more AI citations per page. Learn why benchmark studies outperform other original data.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png" class="attachment-large size-large wp-post-image" alt="Why most original data never gets cited - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Part 1 tackled those all-important <a href="https://www.growth-memo.com/p/topics-matter-for-third-party-authority" target="_blank" rel="noopener">third-party citation signals</a>, while Part 2 made the case for <a href="https://www.growth-memo.com/p/why-proprietary-data-is-your-most" target="_blank" rel="noopener">publishing original data</a>: It is the strongest single predictor of page originality, and the bar to earning visibility/authority via this play is low.</p>



<p>This memo has more ammo to back up your use of proprietary data in content creation.</p>



<p>Publishing the number is necessary. But it’s not always what gets cited. We pulled Gauge&#8217;s citation data to find out what AI actually rewards when it comes to publishing first-party data, and the answer is narrower and more useful than “original data wins.” (Although original data does, in fact, win.)</p>



<p>AI rewards one format almost to the exclusion of everything else: The benchmark that answers “which is best.”</p>



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



<h2 class="wp-block-heading" id="h-first-party-research-is-scarce-and-punches-above-its-weight">First-party research is scarce and punches above its weight</h2>



<p>We worked from Gauge&#8217;s cited-URL set: 301 live pages that AI systems cited (316 unique prompts across 7 verticals), carrying 1,075 citations between them.&nbsp;</p>



<p>After a full URL audit,<strong> only 8 of those 301 pages qualified as primary research</strong>, meaning the original source of the data and methodology are on the page… rather than a writeup of someone else&#8217;s numbers.</p>



<p>Eight pages out of 301 is 2.7% of the set. Those same 8 pages earned 90 of 1,075 citations, or 8.4% of citation volume. First-party research shows up rarely, then over-indexes 3x on citation share when it does.</p>



<p>The cleaner way to see it is density.&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1639" height="1000" src="https://searchengineland.com/wp-content/seloads/2026/07/image-23.png" alt="" class="wp-image-481678" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-23.png 1639w, https://searchengineland.com/wp-content/seloads/2026/07/image-23-768x469.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-23-1536x937.png 1536w" sizes="(max-width: 1639px) 100vw, 1639px" /></figure>
</div>


<p>Primary research averaged 11.3 citations per page. Everything else averaged 3.4. A primary-research page was 3.3x as citation-dense as a non-primary one.</p>



<p>Primary research compounds citations.</p>



<p>This is the same shape as the <a href="https://www.growth-memo.com/p/why-proprietary-data-is-your-most" target="_blank" rel="noopener">information gain finding discussed in Part 2</a>, viewed from the AI side instead of the classic 10 blue links side.&nbsp;</p>



<p>There, original data correlated with page originality more than any other trait. Here, original data correlates with citation density. Both point the same direction: The number only you can produce is the lever.&nbsp;</p>



<h2 class="wp-block-heading" id="h-original-research-wins-when-the-question-has-a-benchmark">Original research wins when the question has a benchmark</h2>



<p>Here’s where the “original data wins” filter gets sharper.</p>



<p>The 90 primary-research citations are not spread across the 8 pages evenly, and they are not spread across topics evenly.&nbsp;</p>



<p>75 of the 90 came from one cluster: cloud data warehouse benchmarks. <a href="https://www.fivetran.com/blog/warehouse-benchmark">Fivetran&#8217;s warehouse benchmark</a> alone took 44 citations, just under half of every primary-research citation in the set. (More on that below.)</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1639" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/image-24.png" alt="" class="wp-image-481679" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-24.png 1639w, https://searchengineland.com/wp-content/seloads/2026/07/image-24-768x506.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-24-1536x1012.png 1536w" sizes="(max-width: 1639px) 100vw, 1639px" /></figure>
</div>


<p>Reality: Strip the benchmark cluster out and first-party research barely registers in the citation set. The win is not “we published original data.”&nbsp;</p>



<p>The win is “we published a benchmark that answers a buying comparison,” and almost nobody builds one. (“Benchmark” meaning you measure a set of named things against each other on a specific yardstick, and publish the results as numbers.)</p>



<p>Original research is most effective when it is packaged in a way that directly answers commercial comparison queries.</p>



<p>This is what Google is after with non-commodity content: new and helpful information that is hard to get.</p>



<p>Primary-research citations clustered where the prompt asked AI to compare options on measurable specs: speed, cost, latency, yield, or performance.</p>



<p>That explains the warehouse benchmark spike. The “HR Tech / Compensation” label is noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1639" height="1040" src="https://searchengineland.com/wp-content/seloads/2026/07/image-25.png" alt="" class="wp-image-481681" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-25.png 1639w, https://searchengineland.com/wp-content/seloads/2026/07/image-25-768x487.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-25-1536x975.png 1536w" sizes="(max-width: 1639px) 100vw, 1639px" /></figure>
</div>


<p>Crypto / Solana shows the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need first-hand ecosystem data, not generic explainers.</p>



<p>The pattern disappears in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning, none of those topics produced a cited primary-research page.</p>



<h3 class="wp-block-heading" id="h-a-closer-look-at-the-content-that-held-44-of-the-citations">A closer look at the content that held 44 of the citations</h3>



<p><a href="https://www.fivetran.com/blog/warehouse-benchmark" target="_blank" rel="noopener">Fivetran&#8217;s warehouse benchmark</a> took 44 of this data set’s citations on its own, and Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations in the set. Why?</p>



<p>It’s a piece of content from 2022, but when you examine it, it’s easy to see why LLMs prefer it.&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="557" src="https://searchengineland.com/wp-content/seloads/2026/07/image-3.jpeg" alt="" class="wp-image-481680" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-3.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/07/image-3-768x535.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>
</div>


<ol class="wp-block-list">
<li><strong>It answers a measurable comparison head-on.</strong> Named warehouses, BigQuery, Redshift, Snowflake, and Databricks, ranked on speed and cost. It’s entity-rich and not afraid to name all the major players.</li>



<li><strong>It runs on real first-party data.</strong> Fivetran tested against actual customer usage rather than synthetic assumptions, and called out that choice directly. </li>



<li><strong>It shows the method, step by step.</strong> Trust signals. Separate sections walk through what data they queried, what queries they ran, and how they configured and tuned each warehouse. A reader (or a model) can see exactly how the numbers were produced. </li>



<li><strong>The structure is built to be lifted.</strong> Descriptive headings (&#8220;Results,&#8221; &#8220;How much did performance improve?,&#8221; &#8220;Why are our results different from previous benchmarks?&#8221;) let AI map a question to the one passage that answers it.</li>



<li><strong>It links to its raw data and sources.</strong> The page footnotes its references, including the C-Store paper, and points to the underlying data, so every claim is verifiable. Not many brands put this much work into a data-backed content piece, let alone offer the full data set for transparency.</li>



<li><strong>It shows its seams.</strong> Dated correction notes from December 2022, named qualitative limits, and an honest &#8220;performance floor&#8221; caveat make the quantitative claims more credible… not less. They also note corrections.</li>



<li><strong>The URL never moved.</strong> A 2022 page is still collecting citations in 2026 because it stayed put at one canonical address.</li>
</ol>



<p>The data behind a page like this is easier to pull and analyze than it has ever been. What is not easy is everything around it: the clean method, the linked sources, the corrections, the navigable structure, the willingness to name what the numbers do not prove. That’s craft, and that’s the moat here.&nbsp;</p>



<p>This first-party data-focused piece isn’t a sloppy press release with half-assed pulled data. It took a lot of work, and it’s holding authority for 4 years out. The takeaway: AI does not reward “original data” by default. It rewards first-party research when the page provides a clear answer to a measurable comparison that signals depth of expertise and trust.</p>



<p>The open opportunity here is to publish a retrievable dataset for a buyer question where AI currently has no clean benchmark source. This maps onto the <a href="https://www.growth-memo.com/p/query-fan-out">unanswered-questions finding</a> from Part 2: the open door exists, and in these verticals nobody has walked through it with a real dataset.</p>



<h2 class="wp-block-heading" id="h-original-data-needs-a-citation-ready-package">Original data needs a citation-ready package</h2>



<p>Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the question.</p>



<p>That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists. The citation does not.</p>



<p>The pages that won in this dataset had both: original numbers and a clean citation shape. Stable URL. Clear method. Named comparison. Results that answered a buyer question directly.</p>



<ul class="wp-block-list">
<li>Who wins: Brands sitting on proprietary product, usage, or pricing data who package it into a comparison a buyer can act on, one that informs LLM outputs for recommendations.&nbsp;</li>



<li>Who loses: Brands publishing original numbers buried in narrative, on slow or unstable pages, with no comparison frame for AI to lift.</li>
</ul>



<p>A citation-ready research page has four parts:</p>



<ul class="wp-block-list">
<li><strong>Lead with the comparison result.</strong> The headline finding (“X is fastest, Y is cheapest at scale”) goes in the first 30% of the page. Result, then method, then nuance.</li>



<li><strong>Box the methodology.</strong> Sample, time window, what was measured, how. Attribution confidence is part of what makes a number citable. Make your methodology clear on the page.</li>



<li><strong>Explicitly frame it as a comparison if it is one.</strong> AI reaches for benchmarks on “which is best” prompts. A table that compares named options on named specs is the shape it lifts.</li>



<li><strong>Keep the URL stable. </strong>One canonical page, kept live, not migrated or renamed every redesign. The citation you earn this quarter only compounds if the page is still there next quarter. Of 365 cited URLs in this data set, 64 were dead, redirected, or otherwise broken, taking 203 citations down with them.</li>
</ul>



<p>This is the work behind a citable benchmark, and it is more involved than it looks.&nbsp;</p>



<p>HockeyStack documented its own version in a <a href="https://www.hockeystack.com/resources/playbook/how-to-launch-research-reports" target="_blank" rel="noopener">playbook on launching research reports</a>: they published 18 original reports built entirely on anonymized first-party customer data, the kind no competitor could replicate. </p>



<p>Their process names every step the Fivetran page demonstrates: list the data points you need, get a teammate to pull them with SQL, define and document the method so the numbers hold up to scrutiny, then structure the report around a real ICP question. They call methodology non-negotiable for a reason, noting that without it, someone will always dispute your data.&nbsp;</p>



<p>With AI analysis, the data is the easy part now. Building the content into something that is citable, demonstrates E-E-A-T, and is still earning visibility 4 years out for commercial queries is where the hard work lies.</p>



<p><strong>What sites are already trusted for your topic? </strong>When a benchmark you did not publish is taking the citations in your category, the <a href="https://www.growth-memo.com/i/202107502/premium-citation-source-mapper-tool-build-your-targeted-off-site-authority" target="_blank" rel="noopener">Citation Source Mapper</a> maps that trusted set into a ranked, pitchable target list. It is in the <a href="https://www.growth-memo.com/p/exclusive-growth-premium-resources" target="_blank" rel="noopener">premium library</a>.</p>



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


Part 1 tackled those all-important third-party citation signals, while Part 2 made the case for publishing original data: It is the strongest single predictor of page originality, and the bar to earning visibility/authority via this play is low.
This memo has more ammo to back up your use of proprietary data in content creation.
Publishing the number is necessary. But it’s not always what gets cited. We pulled Gauge’s citation data to find out what AI actually rewards when it comes to publishing first-party data, and the answer is narrower and more useful than “original data wins.” (Although original data does, in fact, win.)
AI rewards one format almost to the exclusion of everything else: The benchmark that answers “which is best.”
View embedded content



First-party research is scarce and punches above its weight
We worked from Gauge’s cited-URL set: 301 live pages that AI systems cited (316 unique prompts across 7 verticals), carrying 1,075 citations between them. 
After a full URL audit, only 8 of those 301 pages qualified as primary research, meaning the original source of the data and methodology are on the page… rather than a writeup of someone else’s numbers.
Eight pages out of 301 is 2.7% of the set. Those same 8 pages earned 90 of 1,075 citations, or 8.4% of citation volume. First-party research shows up rarely, then over-indexes 3x on citation share when it does.
The cleaner way to see it is density. 




Primary research averaged 11.3 citations per page. Everything else averaged 3.4. A primary-research page was 3.3x as citation-dense as a non-primary one.
Primary research compounds citations.
This is the same shape as the information gain finding discussed in Part 2, viewed from the AI side instead of the classic 10 blue links side. 
There, original data correlated with page originality more than any other trait. Here, original data correlates with citation density. Both point the same direction: The number only you can produce is the lever. 
Original research wins when the question has a benchmark
Here’s where the “original data wins” filter gets sharper.
The 90 primary-research citations are not spread across the 8 pages evenly, and they are not spread across topics evenly. 
75 of the 90 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone took 44 citations, just under half of every primary-research citation in the set. (More on that below.)




Reality: Strip the benchmark cluster out and first-party research barely registers in the citation set. The win is not “we published original data.” 
The win is “we published a benchmark that answers a buying comparison,” and almost nobody builds one. (“Benchmark” meaning you measure a set of named things against each other on a specific yardstick, and publish the results as numbers.)
Original research is most effective when it is packaged in a way that directly answers commercial comparison queries.
This is what Google is after with non-commodity content: new and helpful information that is hard to get.
Primary-research citations clustered where the prompt asked AI to compare options on measurable specs: speed, cost, latency, yield, or performance.
That explains the warehouse benchmark spike. The “HR Tech / Compensation” label is noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.




Crypto / Solana shows the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need first-hand ecosystem data, not generic explainers.
The pattern disappears in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning, none of those topics produced a cited primary-research page.
A closer look at the content that held 44 of the citations
Fivetran’s warehouse benchmark took 44 of this data set’s citations on its own, and Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations in the set. Why?
It’s a piece of content from 2022, but when you examine it, it’s easy to see why LLMs prefer it. 





It answers a measurable comparison head-on. Named warehouses, BigQuery, Redshift, Snowflake, and Databricks, ranked on speed and cost. It’s entity-rich and not afraid to name all the major players.
It runs on real first-party data. Fivetran tested against actual customer usage rather than synthetic assumptions, and called out that choice directly. 
It shows the method, step by step. Trust signals. Separate sections walk through what data they queried, what queries they ran, and how they configured and tuned each warehouse. A reader (or a model) can see exactly how the numbers were produced. 
The structure is built to be lifted. Descriptive headings (“Results,” “How much did performance improve?,” “Why are our results different from previous benchmarks?”) let AI map a question to the one passage that answers it.
It links to its raw data and sources. The page footnotes its references, including the C-Store paper, and points to the underlying data, so every claim is verifiable. Not many brands put this much work into a data-backed content piece, let alone offer the full data set for transparency.
It shows its seams. Dated correction notes from December 2022, named qualitative limits, and an honest “performance floor” caveat make the quantitative claims more credible… not less. They also note corrections.
The URL never moved. A 2022 page is still collecting citations in 2026 because it stayed put at one canonical address.
The data behind a page like this is easier to pull and analyze than it has ever been. What is not easy is everything around it: the clean method, the linked sources, the corrections, the navigable structure, the willingness to name what the numbers do not prove. That’s craft, and that’s the moat here. 
This first-party data-focused piece isn’t a sloppy press release with half-assed pulled data. It took a lot of work, and it’s holding authority for 4 years out. The takeaway: AI does not reward “original data” by default. It rewards first-party research when the page provides a clear answer to a measurable comparison that signals depth of expertise and trust.
The open opportunity here is to publish a retrievable dataset for a buyer question where AI currently has no clean benchmark source. This maps onto the unanswered-questions finding from Part 2: the open door exists, and in these verticals nobody has walked through it with a real dataset.
Original data needs a citation-ready package
Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the question.
That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists. The citation does not.
The pages that won in this dataset had both: original numbers and a clean citation shape. Stable URL. Clear method. Named comparison. Results that answered a buyer question directly.
Who wins: Brands sitting on proprietary product, usage, or pricing data who package it into a comparison a buyer can act on, one that informs LLM outputs for recommendations. 
Who loses: Brands publishing original numbers buried in narrative, on slow or unstable pages, with no comparison frame for AI to lift.
A citation-ready research page has four parts:
Lead with the comparison result. The headline finding (“X is fastest, Y is cheapest at scale”) goes in the first 30% of the page. Result, then method, then nuance.
Box the methodology. Sample, time window, what was measured, how. Attribution confidence is part of what makes a number citable. Make your methodology clear on the page.
Explicitly frame it as a comparison if it is one. AI reaches for benchmarks on “which is best” prompts. A table that compares named options on named specs is the shape it lifts.
Keep the URL stable. One canonical page, kept live, not migrated or renamed every redesign. The citation you earn this quarter only compounds if the page is still there next quarter. Of 365 cited URLs in this data set, 64 were dead, redirected, or otherwise broken, taking 203 citations down with them.
This is the work behind a citable benchmark, and it is more involved than it looks. 
HockeyStack documented its own version in a playbook on launching research reports: they published 18 original reports built entirely on anonymized first-party customer data, the kind no competitor could replicate. 
Their process names every step the Fivetran page demonstrates: list the data points you need, get a teammate to pull them with SQL, define and document the method so the numbers hold up to scrutiny, then structure the report around a real ICP question. They call methodology non-negotiable for a reason, noting that without it, someone will always dispute your data. 
With AI analysis, the data is the easy part now. Building the content into something that is citable, demonstrates E-E-A-T, and is still earning visibility 4 years out for commercial queries is where the hard work lies.
What sites are already trusted for your topic? When a benchmark you did not publish is taking the citations in your category, the Citation Source Mapper maps that trusted set into a ranked, pitchable target list. It is in the premium library.
This post first appeared on the author’s website and is republished here with permission.

Primary research is rare, but it earns 3.3x more AI citations per page. Learn why benchmark studies outperform other original data.

Why most original data never gets cited

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool.png" class="attachment-large size-large wp-post-image" alt="5 questions to ask AI vendors before buying a tool" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Separate genuine solutions from AI hype by evaluating business value, data policies, customer proof, and implementation before you invest.


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<p>There are many ways to use AI in marketing, and it feels like for every smart initiative, 10 AI vendors have cropped up with a tool to address it.</p>



<p>At the beginning of this wave, I took more calls and answered more emails than I do these days. Over time, I realized I was asking vendors the same handful of questions to assess whether their tools were worth deploying.</p>



<p>If you&#8217;re in the same boat and overwhelmed with vendor outreach, here are five questions to help you figure out whether they&#8217;re worth your time, along with my rationale for asking them and what I&#8217;m looking to hear — or not hear.</p>



<h2 class="wp-block-heading" id="h-1-what-problem-does-your-tool-solve-nbsp">1. What problem does your tool solve?&nbsp;</h2>



<p>This question should help you understand the purpose of the tool and — crucially — whether the value it creates maps to real business outcomes.&nbsp;</p>



<p>If the vendor can&#8217;t clearly state the challenges or use cases the tool addresses, it wasn&#8217;t purpose-built to solve a real problem your team faces, whether you&#8217;re in-house or at an agency. Beware of vendors who try to dazzle you with feature-heavy language but can&#8217;t explain the business benefits those features deliver.</p>



<p>If a vendor identifies at least one existing team problem the tool solves and explains how it improves business outcomes, it&#8217;s a good idea to keep talking. A great follow-up question is to ask for a case study showing how the tool was used and the outcomes it delivered for an organization similar to yours in size and vertical.</p>



<p>Look for benefits such as &#8220;increases output&#8221; or &#8220;identifies gaps in tracking to speed up troubleshooting.&#8221; But don&#8217;t rush to invest in tools that promise to &#8220;save time&#8221; (even if they truly do) unless you have a plan for how you&#8217;ll use that extra time.</p>


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<h2 class="wp-block-heading" id="h-2-what-expertise-do-you-have-in-the-space-where-this-tool-solves-a-problem">2. What expertise do you have in the space where this tool solves a problem?</h2>



<p>The answer to this question should tell you whether the vendor built this tool <strong>for</strong> advertisers or just <strong>at</strong> advertisers. </p>



<p>Technical chops matter, but so does understanding how a media buyer actually spends their day. If the vendor doesn&#8217;t have personal experience in media buying, they need to explain how they researched the media-buying market and incorporated those insights into the tool.</p>



<p>If they have a shallow understanding or lack expertise, that&#8217;s a red flag. It&#8217;s fine if a rep doesn&#8217;t have that expertise directly, but someone on their team should, and you should have access to that person as soon as possible if you intend to keep talking.</p>



<p>If a vendor has a story or background that led them to identify a problem you can relate to firsthand and decide to build the solution themselves, that&#8217;s compelling. A founding mission that addresses your existing challenges is a solid foundation for a tool that can make a difference in your team&#8217;s performance.</p>



<h2 class="wp-block-heading" id="h-3-what-case-studies-real-use-cases-and-results-can-you-share">3. What case studies, real use cases, and results can you share?</h2>



<p>I touched on case studies a few paragraphs above, and they&#8217;re a must-have in a new, fast-developing industry. I&#8217;d be looking to understand whether the vendor has an appealing track record with customers like me or whether we&#8217;d be an early adopter.</p>



<p>If you fall into the latter camp, there are pros and cons, just as there are with any advertising beta you can access before your competitors. You may get a leg up by finding an important growth accelerator before your competitors do, you may spin your wheels trying to work through bugs, or you may find that the tool simply doesn&#8217;t deliver on its promises. </p>



<p>If you can&#8217;t trust a tool or there&#8217;s a risk you&#8217;ll need to provide detailed feedback to get it working, it might not be the best use of your time and money unless you believe what it could eventually deliver would be a game-changer.</p>



<p>If you&#8217;re clearly going to be an early adopter and the vendor isn&#8217;t willing to be flexible on contract terms that help mitigate risk, that&#8217;s a nonstarter. More established tools will likely deliver more value consistently, although they&#8217;ll have less wiggle room on pricing because of it. Newer tools that take a hard line on pricing and contract terms, however, likely won&#8217;t be good long-term partners.</p>



<p>For established vendors, you need to see specific, relevant case studies with real numbers from advertisers in a similar space, of a similar size, or with a similar use case. </p>



<p>If they&#8217;re early-stage companies, the best answer is honesty: &#8220;You&#8217;d be one of our first clients in this vertical. Here&#8217;s what we&#8217;ve seen elsewhere, and here&#8217;s what that partnership would look like.&#8221; That transparency is a green flag.</p>



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<h2 class="wp-block-heading" id="h-4-who-owns-my-data-and-how-is-it-being-used-to-train-models">4. Who owns my data, and how is it being used to train models?</h2>



<p>It&#8217;s interesting how easily people share data with AI and AI tools in the rush to find a competitive edge. This is something I&#8217;d strongly caution potential buyers to consider before signing anything.</p>



<p>Watch for any answer that suggests your data is used to train shared or third-party models without your explicit consent. Another red flag is vague or deflecting answers or terms of service that contradict or muddy what the salesperson tells you verbally.</p>



<p>You own your data, full stop. </p>



<p>The vendor should be able to clearly explain its data-handling practices, including where your data is stored, how long it&#8217;s retained, whether it&#8217;s used for model training (and, if so, only to refine your own instance), and what happens to your data if you stop using the tool. This needs to be in the contract, not just a verbal assurance. If it&#8217;s not there, insist that it be included before you sign.</p>



<h2 class="wp-block-heading" id="h-5-what-does-implementation-actually-look-like-and-what-does-success-require-from-our-team">5. What does implementation actually look like, and what does success require from our team?</h2>



<p>Before you commit money, you need to understand the real cost of adopting this tool. That cost includes more than the price. It&#8217;s the time, the internal lift (including integration, training, and QA), and any potential disruption to your existing martech stack.</p>



<p>If this requires resources your team doesn&#8217;t have, or if you realistically can&#8217;t dedicate the time to make good use of the tool, it&#8217;s not worth the investment just yet. Many marketers could avoid wasted martech spend if they asked this question and seriously considered the answer.</p>



<p>No tool should be one-size-fits-all, but easy implementation and intuitive design are crucial to getting your team to adopt and stick with it.</p>


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<h2 class="wp-block-heading" id="h-don-t-let-ai-hype-rush-your-decision">Don&#8217;t let AI hype rush your decision</h2>



<p>I know firsthand that a lot of these tools sound too good to be true — and often, they are. You need to balance growth ambition and curiosity with a bit of caution. </p>



<p>Remember that we&#8217;re still in the early stages of AI adoption. If a tool seems too expensive or hard to onboard, or the contract is more rigid than it should be given the tool&#8217;s track record, a more attractive solution will likely emerge in the next few months.</p>



<p>When in doubt, ask for a free trial. Assuming it won&#8217;t create too much work for your team to integrate the tool, that could be the right next step in determining whether you&#8217;ve found your next competitive advantage.</p>

There are many ways to use AI in marketing, and it feels like for every smart initiative, 10 AI vendors have cropped up with a tool to address it.
At the beginning of this wave, I took more calls and answered more emails than I do these days. Over time, I realized I was asking vendors the same handful of questions to assess whether their tools were worth deploying.
If you’re in the same boat and overwhelmed with vendor outreach, here are five questions to help you figure out whether they’re worth your time, along with my rationale for asking them and what I’m looking to hear — or not hear.
1. What problem does your tool solve? 
This question should help you understand the purpose of the tool and — crucially — whether the value it creates maps to real business outcomes. 
If the vendor can’t clearly state the challenges or use cases the tool addresses, it wasn’t purpose-built to solve a real problem your team faces, whether you’re in-house or at an agency. Beware of vendors who try to dazzle you with feature-heavy language but can’t explain the business benefits those features deliver.
If a vendor identifies at least one existing team problem the tool solves and explains how it improves business outcomes, it’s a good idea to keep talking. A great follow-up question is to ask for a case study showing how the tool was used and the outcomes it delivered for an organization similar to yours in size and vertical.
Look for benefits such as “increases output” or “identifies gaps in tracking to speed up troubleshooting.” But don’t rush to invest in tools that promise to “save time” (even if they truly do) unless you have a plan for how you’ll use that extra time.
AI recommends.
      
See your AI visibility
    
2. What expertise do you have in the space where this tool solves a problem?
The answer to this question should tell you whether the vendor built this tool for advertisers or just at advertisers. 
Technical chops matter, but so does understanding how a media buyer actually spends their day. If the vendor doesn’t have personal experience in media buying, they need to explain how they researched the media-buying market and incorporated those insights into the tool.
If they have a shallow understanding or lack expertise, that’s a red flag. It’s fine if a rep doesn’t have that expertise directly, but someone on their team should, and you should have access to that person as soon as possible if you intend to keep talking.
If a vendor has a story or background that led them to identify a problem you can relate to firsthand and decide to build the solution themselves, that’s compelling. A founding mission that addresses your existing challenges is a solid foundation for a tool that can make a difference in your team’s performance.
3. What case studies, real use cases, and results can you share?
I touched on case studies a few paragraphs above, and they’re a must-have in a new, fast-developing industry. I’d be looking to understand whether the vendor has an appealing track record with customers like me or whether we’d be an early adopter.
If you fall into the latter camp, there are pros and cons, just as there are with any advertising beta you can access before your competitors. You may get a leg up by finding an important growth accelerator before your competitors do, you may spin your wheels trying to work through bugs, or you may find that the tool simply doesn’t deliver on its promises. 
If you can’t trust a tool or there’s a risk you’ll need to provide detailed feedback to get it working, it might not be the best use of your time and money unless you believe what it could eventually deliver would be a game-changer.
If you’re clearly going to be an early adopter and the vendor isn’t willing to be flexible on contract terms that help mitigate risk, that’s a nonstarter. More established tools will likely deliver more value consistently, although they’ll have less wiggle room on pricing because of it. Newer tools that take a hard line on pricing and contract terms, however, likely won’t be good long-term partners.
For established vendors, you need to see specific, relevant case studies with real numbers from advertisers in a similar space, of a similar size, or with a similar use case. 
If they’re early-stage companies, the best answer is honesty: “You’d be one of our first clients in this vertical. Here’s what we’ve seen elsewhere, and here’s what that partnership would look like.” That transparency is a green flag.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
4. Who owns my data, and how is it being used to train models?
It’s interesting how easily people share data with AI and AI tools in the rush to find a competitive edge. This is something I’d strongly caution potential buyers to consider before signing anything.
Watch for any answer that suggests your data is used to train shared or third-party models without your explicit consent. Another red flag is vague or deflecting answers or terms of service that contradict or muddy what the salesperson tells you verbally.
You own your data, full stop. 
The vendor should be able to clearly explain its data-handling practices, including where your data is stored, how long it’s retained, whether it’s used for model training (and, if so, only to refine your own instance), and what happens to your data if you stop using the tool. This needs to be in the contract, not just a verbal assurance. If it’s not there, insist that it be included before you sign.
5. What does implementation actually look like, and what does success require from our team?
Before you commit money, you need to understand the real cost of adopting this tool. That cost includes more than the price. It’s the time, the internal lift (including integration, training, and QA), and any potential disruption to your existing martech stack.
If this requires resources your team doesn’t have, or if you realistically can’t dedicate the time to make good use of the tool, it’s not worth the investment just yet. Many marketers could avoid wasted martech spend if they asked this question and seriously considered the answer.
No tool should be one-size-fits-all, but easy implementation and intuitive design are crucial to getting your team to adopt and stick with it.
customers won’t either.
      
See your AI visibility
    
Don’t let AI hype rush your decision
I know firsthand that a lot of these tools sound too good to be true — and often, they are. You need to balance growth ambition and curiosity with a bit of caution. 
Remember that we’re still in the early stages of AI adoption. If a tool seems too expensive or hard to onboard, or the contract is more rigid than it should be given the tool’s track record, a more attractive solution will likely emerge in the next few months.
When in doubt, ask for a free trial. Assuming it won’t create too much work for your team to integrate the tool, that could be the right next step in determining whether you’ve found your next competitive advantage.

Separate genuine solutions from AI hype by evaluating business value, data policies, customer proof, and implementation before you invest.

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