<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png" class="attachment-large size-large wp-post-image" alt="ChatGPT ads" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>OpenAI also said ChatGPT Ads are live in Japan and South Korea.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png" class="attachment-large size-large wp-post-image" alt="ChatGPT ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>ChatGPT Ads has sent out an email to advertisers announcing new ChatGPT Ads Manager updates and ChatGPT Ads experience updates.  The updates include custom audiences, new overview tab, suggested ad drafts, as well as a refreshed ad card format and expanding ads in Japan and South Korea.</p>



<p>Here is what is new:</p>



<ul class="wp-block-list">
<li><strong>Custom audiences</strong>: Upload lists with 25K or more users to <a href="https://help.openai.com/en/articles/20001346-set-up-custom-audiences-for-your-campaign">include or suppress audiences from campaigns</a>. Bid multipliers can also be set for audiences at the ad group level. We covered this in <a href="https://searchengineland.com/chatgpt-ads-rolling-out-audience-lists-481712">more detail over here</a> earlier this week.</li>



<li><strong>Overview tab</strong>: You can monitor account health, review recommended tasks that can help improve campaign performance, and analyze key performance metrics in a larger, more flexible trend chart.</li>



<li><strong>Suggested ad drafts</strong>: If your campaign could benefit from broader content coverage to optimize delivery, you may see the option to select &#8216;Add new ad&#8217; in your campaign view. This feature uses existing website metadata to prefill an ad draft with an image, title, and description for you to review, edit, and assign to a campaign and ad group. It does not generate new copy or imagery with AI. We covered this in more detail <a href="https://searchengineland.com/openai-can-generate-chatgpt-ads-for-you-481651">over here earlier this week</a>.</li>



<li><strong>ChatGPT Ads are live in Japan and South Korea</strong>: Campaigns can now target users in Japan and South Korea, expanding reach for advertisers doing business in those markets.</li>



<li><strong>Refreshed static ad card format</strong>: OpenAI is starting to roll out a refreshed static ad card format across web and mobile that is more compact and easier to read, with larger visual elements. This actually rolled out in <a href="https://www.seroundtable.com/chatgpt-ads-drops-sponsored-label-41584.html">late June</a>. Here is the before and after:</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1352" height="344" src="https://searchengineland.com/wp-content/seloads/2026/07/new-chatgpt-ads-format.png" alt="" class="wp-image-481915" srcset="https://searchengineland.com/wp-content/seloads/2026/07/new-chatgpt-ads-format.png 1352w, https://searchengineland.com/wp-content/seloads/2026/07/new-chatgpt-ads-format-768x195.png 768w" sizes="(max-width: 1352px) 100vw, 1352px" /></figure>



<p><strong>Why we care. </strong>ChatGPT Ads are new, and OpenAI continues to add new features, expand to new markets and test new ad formats and treatements.</p>



<p>Make sure you stay on top of these changes, experiment and continue to fine-tune your ad creatives and campaigns. </p>


<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">
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       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
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ChatGPT Ads has sent out an email to advertisers announcing new ChatGPT Ads Manager updates and ChatGPT Ads experience updates.  The updates include custom audiences, new overview tab, suggested ad drafts, as well as a refreshed ad card format and expanding ads in Japan and South Korea.
Here is what is new:
Custom audiences: Upload lists with 25K or more users to include or suppress audiences from campaigns. Bid multipliers can also be set for audiences at the ad group level. We covered this in more detail over here earlier this week.
Overview tab: You can monitor account health, review recommended tasks that can help improve campaign performance, and analyze key performance metrics in a larger, more flexible trend chart.
Suggested ad drafts: If your campaign could benefit from broader content coverage to optimize delivery, you may see the option to select ‘Add new ad’ in your campaign view. This feature uses existing website metadata to prefill an ad draft with an image, title, and description for you to review, edit, and assign to a campaign and ad group. It does not generate new copy or imagery with AI. We covered this in more detail over here earlier this week.
ChatGPT Ads are live in Japan and South Korea: Campaigns can now target users in Japan and South Korea, expanding reach for advertisers doing business in those markets.
Refreshed static ad card format: OpenAI is starting to roll out a refreshed static ad card format across web and mobile that is more compact and easier to read, with larger visual elements. This actually rolled out in late June. Here is the before and after:




Why we care. ChatGPT Ads are new, and OpenAI continues to add new features, expand to new markets and test new ad formats and treatements.
Make sure you stay on top of these changes, experiment and continue to fine-tune your ad creatives and campaigns. 
customers won’t either.
      
See your AI visibility

OpenAI also said ChatGPT Ads are live in Japan and South Korea.

ChatGPT Ads new overview tab, suggested ad drafts, new ad formats and more

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube.png" class="attachment-large size-large wp-post-image" alt="Ask YouTube" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Signed-in U.S. viewers can now ask natural-language questions and get AI responses with videos, Shorts, and follow-up prompts.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube.png" class="attachment-large size-large wp-post-image" alt="Ask YouTube" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ask-youtube-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>YouTube expanded Ask YouTube to signed-in U.S. desktop viewers 13 and older, moving its conversational search experience beyond a Premium-only test.</p>



<p><strong>What is Ask YouTube? </strong>Ask YouTube lets users type natural-language questions into the YouTube search bar and receive responses that combine text, video clips, long-form videos, and Shorts. Users can also ask follow-up questions to refine results.</p>



<p><strong>Access expands.</strong> When <a href="https://searchengineland.com/youtube-testing-new-search-experience-ask-youtube-475786">YouTube announced the test</a> in April, Ask YouTube was limited to U.S. YouTube Premium members 18 and older who opted in through youtube.com/new. On July 6, YouTube expanded it to signed-in U.S. viewers 13 and older using English-language searches on desktop.</p>



<ul class="wp-block-list">
<li>Signed-out viewers and supervised accounts remain excluded.</li>



<li>YouTube said it will roll out the feature to more devices, languages, and users worldwide in the coming months.</li>
</ul>



<p id="h-regular-search-remains"><strong>YouTube standard Search isn&#8217;t going away.</strong> Users can switch back to traditional video results by clicking <em>All</em> on an Ask YouTube results page or by returning to the Home page. Ask YouTube remains a separate search option rather than a replacement for standard YouTube Search.</p>



<p id="h-regular-search-remains"><strong>Views count for creators. </strong>YouTube said videos featured in Ask YouTube responses give creators another way to be discovered.</p>



<ul class="wp-block-list">
<li>Views from Shorts, videos, and previews shown in Ask YouTube responses count toward total view metrics and YouTube Partner Program eligibility. Featured videos also display the video title and channel name.</li>



<li>YouTube said creators can improve their chances of appearing by publishing unique, high-quality content with clear chapters and descriptive titles. Those signals help its systems match video segments to viewer questions.</li>
</ul>



<p id="h-why-we-care"><strong>Why we care. </strong>YouTube is putting conversational search in front of a much larger group of U.S. desktop users. Your videos may need clear titles, chapters, and segments that answer specific questions well enough to appear in Ask YouTube responses.</p>



<p id="h-why-we-care"><strong>What it looks like. </strong>Here&#8217;s a GIF of Ask YouTube in action:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2026/04/ask-youtube-.gif" alt="" class="wp-image-475787" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ask-youtube-.gif 800w, https://searchengineland.com/wp-content/seloads/2026/04/ask-youtube--768x432.gif 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p id="h-why-we-care"><strong>The announcement.</strong> <a href="https://support.google.com/youtube/thread/448540943/try-a-new-conversational-search-experience-with-ask-youtube">Try a new conversational search experience with Ask YouTube</a>


<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">
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      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
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      <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>



YouTube expanded Ask YouTube to signed-in U.S. desktop viewers 13 and older, moving its conversational search experience beyond a Premium-only test.
What is Ask YouTube? Ask YouTube lets users type natural-language questions into the YouTube search bar and receive responses that combine text, video clips, long-form videos, and Shorts. Users can also ask follow-up questions to refine results.
Access expands. When YouTube announced the test in April, Ask YouTube was limited to U.S. YouTube Premium members 18 and older who opted in through youtube.com/new. On July 6, YouTube expanded it to signed-in U.S. viewers 13 and older using English-language searches on desktop.
Signed-out viewers and supervised accounts remain excluded.
YouTube said it will roll out the feature to more devices, languages, and users worldwide in the coming months.
YouTube standard Search isn’t going away. Users can switch back to traditional video results by clicking All on an Ask YouTube results page or by returning to the Home page. Ask YouTube remains a separate search option rather than a replacement for standard YouTube Search.
Views count for creators. YouTube said videos featured in Ask YouTube responses give creators another way to be discovered.
Views from Shorts, videos, and previews shown in Ask YouTube responses count toward total view metrics and YouTube Partner Program eligibility. Featured videos also display the video title and channel name.
YouTube said creators can improve their chances of appearing by publishing unique, high-quality content with clear chapters and descriptive titles. Those signals help its systems match video segments to viewer questions.
Why we care. YouTube is putting conversational search in front of a much larger group of U.S. desktop users. Your videos may need clear titles, chapters, and segments that answer specific questions well enough to appear in Ask YouTube responses.
What it looks like. Here’s a GIF of Ask YouTube in action:




The announcement. Try a new conversational search experience with Ask YouTube



  
customers won’t either.
      
See your AI visibility

Signed-in U.S. viewers can now ask natural-language questions and get AI responses with videos, Shorts, and follow-up prompts.

Ask YouTube AI search experience expands to U.S. desktop users

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure.png" class="attachment-large size-large wp-post-image" alt="Google Ads AI disclosure" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google will show whether ad creative was created or edited with AI. The AI-made ad disclosures are rolling out worldwide.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure.png" class="attachment-large size-large wp-post-image" alt="Google Ads AI disclosure" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-ai-disclosure-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is adding AI disclosures to ads across Search, YouTube, and Discover. The new <em>How this ad was made</em> section will appear in My Ad Center, Google announced today. </p>



<p>The panel will indicate whether an ad was created or modified with AI, Google said. The feature will roll out globally across Google’s main ad surfaces, expanding its ad transparency tools as more advertisers use generative AI to create ad assets.</p>



<p><strong>What it looks like.</strong> Users can access it from the three-dot menu or the info icon on ads. Here&#8217;s a screenshot Google shared with Search Engine Land:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-how-this-ad-was-made.jpg" alt="" class="wp-image-481892" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-how-this-ad-was-made.jpg 1600w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-how-this-ad-was-made-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-how-this-ad-was-made-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-how-this-ad-was-made-1200x675.jpg 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></figure>



<p><strong>Google will handle some disclosures</strong>. Google will automatically add the disclosure in My Ad Center when advertisers use its generative AI ad tools.</p>



<ul class="wp-block-list">
<li>Advertisers using third-party AI tools will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.</li>
</ul>



<p><strong>Why we care</strong>. AI-generated ads are becoming easier to create, making disclosure more important. You need to know when creative was created or modified with AI because disclosure requirements can vary by market and ad format.</p>



<p><strong>Existing ad rules still apply</strong>. Google said its ad policies continue to prohibit misleading or deceptive ads, whether AI was used or not. The update adds more transparency about how an ad was made, but doesn&#8217;t change Google’s requirement that advertisers clearly identify who they are and what they are promoting.</p>



<p><strong>Earlier AI safeguards</strong>. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers must disclose synthetic or digitally altered content in political ads, a policy Google introduced in 2023.</p>



<p><strong>The announcement. </strong><a href="https://blog.google/products/ads-commerce/google-ads-ai-transparency-labels/" target="_blank" rel="noopener">Expanding AI transparency in ads</a>


<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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        Be the brand <span>AI recommends</span>.
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        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
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Google is adding AI disclosures to ads across Search, YouTube, and Discover. The new How this ad was made section will appear in My Ad Center, Google announced today. 
The panel will indicate whether an ad was created or modified with AI, Google said. The feature will roll out globally across Google’s main ad surfaces, expanding its ad transparency tools as more advertisers use generative AI to create ad assets.
What it looks like. Users can access it from the three-dot menu or the info icon on ads. Here’s a screenshot Google shared with Search Engine Land:




Google will handle some disclosures. Google will automatically add the disclosure in My Ad Center when advertisers use its generative AI ad tools.
Advertisers using third-party AI tools will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.
Why we care. AI-generated ads are becoming easier to create, making disclosure more important. You need to know when creative was created or modified with AI because disclosure requirements can vary by market and ad format.
Existing ad rules still apply. Google said its ad policies continue to prohibit misleading or deceptive ads, whether AI was used or not. The update adds more transparency about how an ad was made, but doesn’t change Google’s requirement that advertisers clearly identify who they are and what they are promoting.
Earlier AI safeguards. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers must disclose synthetic or digitally altered content in political ads, a policy Google introduced in 2023.
The announcement. Expanding AI transparency in ads



  
AI recommends.
      
See your AI visibility

Google will show whether ad creative was created or edited with AI. The AI-made ad disclosures are rolling out worldwide.

Google expands AI ad disclosures across Search, YouTube, Discover

<div><img width="1920" height="1079" src="https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce.png" class="attachment-large size-large wp-post-image" alt="Winning the AI decision layer- From AI discovery to agentic commerce" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce-1536x863.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI engines now decide which brands to recommend, trust, and transact with. Learn the six steps to become AI's preferred choice.

<div><img width="1920" height="1079" src="https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce.png" class="attachment-large size-large wp-post-image" alt="Winning the AI decision layer- From AI discovery to agentic commerce" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce-1536x863.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Winning-the-AI-decision-layer-From-AI-discovery-to-agentic-commerce-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The next battleground for brands is being chosen by AI. Every day, AI engines and autonomous agents decide which brands to recommend, compare, cite, and transact with on behalf of consumers. Brands now need to become the trusted choice AI selects.</p>



<p>This shift is already underway. Adobe data shows that AI-referred traffic to U.S. retail websites grew <a href="https://business.adobe.com/blog/generative-ai-powered-shopping-rises-with-traffic-to-retail-sites">4,700%</a> year over year through mid-2025. Salesforce reports that AI and autonomous agents influenced <a href="https://investor.salesforce.com/news/news-details/2025/Salesforce-Data-AI-and-Agents-Propel-Cyber-Week-to-Record-336-6B-in-Global-Spend/">one in five online orders</a> globally during Cyber Week, driving an estimated $67 billion in sales.</p>



<p>As AI becomes the interface between consumers and brands across discovery, evaluation, and purchase, a new competitive layer is emerging. The AI decision layer is where AI systems evaluate trust, relevance, authority, and transaction readiness before deciding which brands make the shortlist. Brands that fail to influence this layer risk being excluded before a customer ever sees them.</p>



<p>To compete in this new environment, you need to understand how AI makes decisions and what influences whether your brand is discovered, understood, trusted, and ultimately chosen in the age of agentic commerce.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1819" height="490" src="https://searchengineland.com/wp-content/seloads/2026/07/02_ai_decision_layer.png" alt="AI agents decision layer" class="wp-image-481868" srcset="https://searchengineland.com/wp-content/seloads/2026/07/02_ai_decision_layer.png 1819w, https://searchengineland.com/wp-content/seloads/2026/07/02_ai_decision_layer-768x207.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/02_ai_decision_layer-1536x414.png 1536w" sizes="(max-width: 1819px) 100vw, 1819px" /></figure>
</div>


<h2 class="wp-block-heading" id="h-how-to-take-your-brand-from-found-to-actioned">How to take your brand from found to actioned</h2>



<p>Agentic commerce readiness follows a sequential path. Start by making sure AI engines can find your brand, then progress through the remaining stages to enable agentic transactions.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1810" height="486" src="https://searchengineland.com/wp-content/seloads/2026/07/03_six_stage_pipeline_1920x1080.png" alt="" class="wp-image-481869" srcset="https://searchengineland.com/wp-content/seloads/2026/07/03_six_stage_pipeline_1920x1080.png 1810w, https://searchengineland.com/wp-content/seloads/2026/07/03_six_stage_pipeline_1920x1080-768x206.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/03_six_stage_pipeline_1920x1080-1536x412.png 1536w" sizes="(max-width: 1810px) 100vw, 1810px" /></figure>
</div>


<h3 class="wp-block-heading" id="h-step-1-get-found-by-enabling-ai-discovery-and-access">Step 1: Get found by enabling AI discovery and access</h3>



<p>Machine accessibility is the foundation of AI visibility. To enable AI discovery and access, prioritize technical hygiene and token efficiency.</p>



<p>Start by allowing the right crawlers on your website. Google, OpenAI, Anthropic, and Bing must be able to reach your content without unintended restrictions.</p>



<p>Get the basics right. Set up XML sitemaps and robots.txt. Then address crawl errors, create canonical tags, and ensure strong Core Web Vitals. Render your website content server-side so agents can reliably navigate and reason over your pages.</p>



<p>Support token efficiency. Bloated HTML often consumes valuable tokens that AI systems could otherwise use to understand your content, products, and brand.</p>



<p>Publish AI-ready assets. An llms.txt file provides large language model (LLM) crawlers with a concise map of your website, while Markdown versions of your content can significantly reduce token consumption. These updates make it easier and more efficient for AI systems to process and understand your brand.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/enterprise-blueprint-ai-search-visibility-466262"><strong><em>The enterprise blueprint for winning visibility in AI search</em></strong></a>


<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>.
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      <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.
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      <span id="semrush-one-cta">See your AI visibility</span>
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<h3 class="wp-block-heading" id="h-step-2-be-understood-by-building-semantic-clarity">Step 2: Be understood by building semantic clarity</h3>



<p>To be understood by AI engines, you have to build <a href="https://searchengineland.com/entity-authority-ai-search-visibility-471619">entity authority</a>. This allows AI engines to interpret who you are, what you offer, and why you matter.</p>



<p>Structured data transforms web pages into machine-readable knowledge that AI systems can understand, trust, and use. Strengthen your entity graph with comprehensive schema, trusted citations, and linked references.&nbsp;</p>



<p>Deliver clean, server-rendered HTML that AI can access and interpret without friction. Use semantic HTML, structured @graph IDs, and consistent naming. This helps AI engines connect the right context to your brand.</p>



<h3 class="wp-block-heading" id="h-step-3-be-retrieved-by-structuring-content-for-ai-extraction">Step 3: Be retrieved by structuring content for AI extraction</h3>



<p>Traditional search ranks pages, while AI search retrieves and cites passages. Brands win on relevance, clarity, authority, and freshness rather than content length. Original expertise, proprietary data, and real-world experience stand out.</p>



<p>To structure your website content for retrieval, use a clear heading hierarchy that includes H1, H2, and H3. Create descriptive, self-contained sections under each heading.</p>



<p>Build interconnected topic clusters, not isolated pages. This helps AI assemble complete answers.</p>



<p>Front-load every section. Put the core answer and key metrics in the opening sentence before the model hits its token limit.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/chunk-cite-clarify-build-content-framework-ai-search-458884"><strong><em>Chunk, cite, clarify, build: A content framework for AI search</em></strong></a>



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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"/>



<h3 class="wp-block-heading" id="h-step-4-be-trusted-by-building-authority-and-grounding-signals">Step 4: Be trusted by building authority and grounding signals</h3>



<p>Just because AI engines retrieve your content doesn&#8217;t guarantee they&#8217;ll recommend your brand.</p>



<p>AI systems prioritize sources they can trust, making authority and credibility decisive factors. Google&#8217;s experience, expertise, authoritativeness, and trustworthiness (<a href="https://searchengineland.com/guide/google-e-e-a-t-for-seo">E-E-A-T</a>) principles remain some of the strongest signals influencing whether a brand is cited, referenced, or selected.</p>



<p>Yet trust extends far beyond your website. AI evaluates review sentiment, location accuracy, pricing consistency, product availability, and entity alignment across the web. When these signals conflict, AI engines&#8217; confidence decreases.</p>



<p>Credibility is now computational. Grounding – the process of validating responses against trusted evidence – is the bridge between visibility and recommendation.</p>



<p>To earn computational trust, create original, expert-driven content that shows real experience and unique value. Then align every external signal. Make sure reviews, listings, maps, and directories all tell one consistent story about your brand.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/seo-omnichannel-marketing-seamless-engagement-453949"><em>Integrating SEO into omnichannel marketing for seamless engagement</em></a><em>&nbsp;</em>



<h3 class="wp-block-heading" id="h-step-5-be-chosen-by-earning-machine-and-human-preference">Step 5: Be chosen by earning machine and human preference</h3>



<p>AI agents parse attributes, verify claims, and score confidence in milliseconds. That means a brand that can&#8217;t make its value clear to AI is invisible at the decision point.</p>



<p>But emotional preference still matters. Consumers readily delegate routine purchases yet hold tightly to choices tied to identity. Winning brands optimize both, creating content that&#8217;s machine-readable enough to make the shortlist, yet resonant enough to win the final choice.</p>



<p>To earn AI recommendations, measure AI visibility, citation, and recommendation rates through <a href="https://searchengineland.com/guide/query-fan-out">query fan-out</a> testing. Keep brand, product, and location data consistent across every channel. And earn trusted mentions and references that strengthen AI confidence in your brand.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/personalization-connectivity-data-revenue-440067"><strong><em>How to boost your marketing revenue with personalization, connectivity, and data</em></strong></a>



<h3 class="wp-block-heading" id="h-step-6-enable-agentic-transactions">Step 6: Enable agentic transactions</h3>



<p>Recommendation is no longer the finish line for AI search. Discovery, selection, and checkout can happen entirely inside an AI assistant, all without the customer ever visiting your site.</p>



<p>An agentic website is designed for AI agents to discover information, retrieve answers, and perform actions on behalf of users. NLWeb helps make website content conversational and machine-readable, improving how AI systems find and understand the site.&nbsp;</p>



<p>Web Model Context Protocol (MCP) extends this capability by providing a standardized way for AI agents to interact with website functions and complete tasks like retrieving data, initiating workflows, and submitting forms.</p>



<p>Agentic commerce moves the entire transaction inside the assistant. Google&#8217;s Universal Commerce Protocol (UCP) enables chat-based bookings, while OpenAI and Stripe&#8217;s Agentic Commerce Protocol (ACP) pushes your inventory so AI systems can easily surface it. Agent Payments Protocol (AP2) then lets the agent pay.</p>



<p>Underneath it all is MCP, which enables any LLM to read your products, content, and live data. This transforms your website from the destination into the source of truth. It supplies the inventory, pricing, and signals that drive every agent journey.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/how-to-select-a-cms-that-powers-seo-personalization-and-growth-450525"><strong><em>How to select a CMS that powers SEO, personalization, and growth</em></strong></a>



<h2 class="wp-block-heading" id="h-how-to-measure-performance-in-the-ai-decision-layer">How to measure performance in the AI decision layer</h2>



<p>Traditional search metrics like rankings, sessions, and clicks are still necessary to track. But they&#8217;re no longer sufficient measures of success. Instead, track two new layers:</p>



<ul class="wp-block-list">
<li><strong>Visibility:</strong> AI presence rate, AI share of voice, citation frequency, and agent recommendation rate.</li>



<li><strong>Commerce:</strong> AI-influenced revenue, agent conversion rate, autonomous transaction volume, and agentic wallet share.</li>
</ul>



<p>Traffic may decline even as revenue grows. As agents handle discovery, direct visits often fall. But AI-influenced transactions through machine-readable layers like WebMCP and schema endpoints can more than make up for that decrease.</p>



<p>With these changes in place, your website can become the trusted source AI systems rely on for information and actions.</p>


<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">
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       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
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<h2 class="wp-block-heading" id="h-from-seo-to-decision-architecture">From SEO to decision architecture</h2>



<p>SEO remains the foundation for winning search, but a deeper shift became concrete at Google I/O 2026. AI agents now parse raw HTML, distill the browser&#8217;s native accessibility tree, and capture visual screenshots through vision models.</p>



<p>Together, the three paths determine whether a site is truly actionable for AI. A page can be technically flawless yet still fail if its structure, semantics, or user experience break the chain. Miss any stage, and trust and transaction readiness suffer.</p>



<p>Get them right, and your brand becomes discoverable, understandable, trusted, and transactable when AI agents make decisions. The brands that build these capabilities today will be the brands AI surfaces, trusts, and recommends tomorrow.</p>

The next battleground for brands is being chosen by AI. Every day, AI engines and autonomous agents decide which brands to recommend, compare, cite, and transact with on behalf of consumers. Brands now need to become the trusted choice AI selects.
This shift is already underway. Adobe data shows that AI-referred traffic to U.S. retail websites grew 4,700% year over year through mid-2025. Salesforce reports that AI and autonomous agents influenced one in five online orders globally during Cyber Week, driving an estimated $67 billion in sales.
As AI becomes the interface between consumers and brands across discovery, evaluation, and purchase, a new competitive layer is emerging. The AI decision layer is where AI systems evaluate trust, relevance, authority, and transaction readiness before deciding which brands make the shortlist. Brands that fail to influence this layer risk being excluded before a customer ever sees them.
To compete in this new environment, you need to understand how AI makes decisions and what influences whether your brand is discovered, understood, trusted, and ultimately chosen in the age of agentic commerce.




How to take your brand from found to actioned
Agentic commerce readiness follows a sequential path. Start by making sure AI engines can find your brand, then progress through the remaining stages to enable agentic transactions.




Step 1: Get found by enabling AI discovery and access
Machine accessibility is the foundation of AI visibility. To enable AI discovery and access, prioritize technical hygiene and token efficiency.
Start by allowing the right crawlers on your website. Google, OpenAI, Anthropic, and Bing must be able to reach your content without unintended restrictions.
Get the basics right. Set up XML sitemaps and robots.txt. Then address crawl errors, create canonical tags, and ensure strong Core Web Vitals. Render your website content server-side so agents can reliably navigate and reason over your pages.
Support token efficiency. Bloated HTML often consumes valuable tokens that AI systems could otherwise use to understand your content, products, and brand.
Publish AI-ready assets. An llms.txt file provides large language model (LLM) crawlers with a concise map of your website, while Markdown versions of your content can significantly reduce token consumption. These updates make it easier and more efficient for AI systems to process and understand your brand.
Dig deeper: The enterprise blueprint for winning visibility in AI search



  
AI recommends.
      
See your AI visibility
    
Step 2: Be understood by building semantic clarity
To be understood by AI engines, you have to build entity authority. This allows AI engines to interpret who you are, what you offer, and why you matter.
Structured data transforms web pages into machine-readable knowledge that AI systems can understand, trust, and use. Strengthen your entity graph with comprehensive schema, trusted citations, and linked references. 
Deliver clean, server-rendered HTML that AI can access and interpret without friction. Use semantic HTML, structured @graph IDs, and consistent naming. This helps AI engines connect the right context to your brand.
Step 3: Be retrieved by structuring content for AI extraction
Traditional search ranks pages, while AI search retrieves and cites passages. Brands win on relevance, clarity, authority, and freshness rather than content length. Original expertise, proprietary data, and real-world experience stand out.
To structure your website content for retrieval, use a clear heading hierarchy that includes H1, H2, and H3. Create descriptive, self-contained sections under each heading.
Build interconnected topic clusters, not isolated pages. This helps AI assemble complete answers.
Front-load every section. Put the core answer and key metrics in the opening sentence before the model hits its token limit.
Dig deeper: Chunk, cite, clarify, build: A content framework for AI search




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Step 4: Be trusted by building authority and grounding signals
Just because AI engines retrieve your content doesn’t guarantee they’ll recommend your brand.
AI systems prioritize sources they can trust, making authority and credibility decisive factors. Google’s experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) principles remain some of the strongest signals influencing whether a brand is cited, referenced, or selected.
Yet trust extends far beyond your website. AI evaluates review sentiment, location accuracy, pricing consistency, product availability, and entity alignment across the web. When these signals conflict, AI engines’ confidence decreases.
Credibility is now computational. Grounding – the process of validating responses against trusted evidence – is the bridge between visibility and recommendation.
To earn computational trust, create original, expert-driven content that shows real experience and unique value. Then align every external signal. Make sure reviews, listings, maps, and directories all tell one consistent story about your brand.
Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement 



Step 5: Be chosen by earning machine and human preference
AI agents parse attributes, verify claims, and score confidence in milliseconds. That means a brand that can’t make its value clear to AI is invisible at the decision point.
But emotional preference still matters. Consumers readily delegate routine purchases yet hold tightly to choices tied to identity. Winning brands optimize both, creating content that’s machine-readable enough to make the shortlist, yet resonant enough to win the final choice.
To earn AI recommendations, measure AI visibility, citation, and recommendation rates through query fan-out testing. Keep brand, product, and location data consistent across every channel. And earn trusted mentions and references that strengthen AI confidence in your brand.
Dig deeper: How to boost your marketing revenue with personalization, connectivity, and data



Step 6: Enable agentic transactions
Recommendation is no longer the finish line for AI search. Discovery, selection, and checkout can happen entirely inside an AI assistant, all without the customer ever visiting your site.
An agentic website is designed for AI agents to discover information, retrieve answers, and perform actions on behalf of users. NLWeb helps make website content conversational and machine-readable, improving how AI systems find and understand the site. 
Web Model Context Protocol (MCP) extends this capability by providing a standardized way for AI agents to interact with website functions and complete tasks like retrieving data, initiating workflows, and submitting forms.
Agentic commerce moves the entire transaction inside the assistant. Google’s Universal Commerce Protocol (UCP) enables chat-based bookings, while OpenAI and Stripe’s Agentic Commerce Protocol (ACP) pushes your inventory so AI systems can easily surface it. Agent Payments Protocol (AP2) then lets the agent pay.
Underneath it all is MCP, which enables any LLM to read your products, content, and live data. This transforms your website from the destination into the source of truth. It supplies the inventory, pricing, and signals that drive every agent journey.
Dig deeper: How to select a CMS that powers SEO, personalization, and growth



How to measure performance in the AI decision layer
Traditional search metrics like rankings, sessions, and clicks are still necessary to track. But they’re no longer sufficient measures of success. Instead, track two new layers:
Visibility: AI presence rate, AI share of voice, citation frequency, and agent recommendation rate.
Commerce: AI-influenced revenue, agent conversion rate, autonomous transaction volume, and agentic wallet share.
Traffic may decline even as revenue grows. As agents handle discovery, direct visits often fall. But AI-influenced transactions through machine-readable layers like WebMCP and schema endpoints can more than make up for that decrease.
With these changes in place, your website can become the trusted source AI systems rely on for information and actions.
customers won’t either.
      
See your AI visibility
    
From SEO to decision architecture
SEO remains the foundation for winning search, but a deeper shift became concrete at Google I/O 2026. AI agents now parse raw HTML, distill the browser’s native accessibility tree, and capture visual screenshots through vision models.
Together, the three paths determine whether a site is truly actionable for AI. A page can be technically flawless yet still fail if its structure, semantics, or user experience break the chain. Miss any stage, and trust and transaction readiness suffer.
Get them right, and your brand becomes discoverable, understandable, trusted, and transactable when AI agents make decisions. The brands that build these capabilities today will be the brands AI surfaces, trusts, and recommends tomorrow.

AI engines now decide which brands to recommend, trust, and transact with. Learn the six steps to become AI's preferred choice.

Winning the AI decision layer: From AI discovery to agentic commerce

<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>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>


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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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<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.
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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.
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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>


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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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<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.
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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>


<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">
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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.
customers won’t either.
      
See your AI visibility

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.

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All About Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

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

Free Consultation

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

All About
Digital Marketing