<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO.png" class="attachment-large size-large wp-post-image" alt="When AI&#039;s efficiency starts working against your SEO" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The biggest gains come from using AI to accelerate strategy, not replace original thinking. See where to draw the line.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO.png" class="attachment-large size-large wp-post-image" alt="When AI&#039;s efficiency starts working against your SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/When-AIs-efficiency-starts-working-against-your-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">AI has made SEO more efficient than ever. Tasks that once took hours now take minutes, and it&#8217;s tempting to let that efficiency extend from research all the way through content production.</p>



<p class="wp-block-paragraph">But there&#8217;s a tradeoff. The faster AI makes it to publish, the easier it becomes to sacrifice the original thinking, firsthand experience, and distinctive perspective that Google increasingly rewards. At some point, efficiency stops helping your SEO and starts hurting it.</p>



<p class="wp-block-paragraph">The solution isn&#8217;t to stop using AI. It&#8217;s to use it where it creates leverage while keeping human expertise at the center of your content.</p>



<h2 id="h-where-ai-belongs-in-your-seo-workflow" class="wp-block-heading">Where AI belongs in your SEO workflow</h2>



<p class="wp-block-paragraph">Yes, you can use AI for SEO strategy and junior-level work, but your level of investment in a prompt or chat directly affects the quality of the output. The more context and data you provide, the more useful the results become. That makes AI highly effective for brainstorming strategy, analyzing data patterns, and organizing project frameworks.</p>



<p class="wp-block-paragraph">For example, Gemini grouped more than 2,000 declining Page 1 keywords into topical clusters, condensing hours of manual analysis into minutes.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="715" height="669" src="https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-1.png" alt="Gemini grouped more than 2,000 declining page-one keywords" class="wp-image-484009"/><figcaption class="wp-element-caption"><em>Source: Gemini, Keyword categorization for ecommerce website, keywords pulled from Ahrefs</em></figcaption></figure>
</div>


<p class="wp-block-paragraph">After importing Google Search Console data, Gemini mapped those keyword themes to the URLs losing visibility, producing a focused list of pages to optimize.</p>



<p class="wp-block-paragraph">That&#8217;s where the human takes over.</p>



<p class="wp-block-paragraph">The line between helpful efficiency and search problems depends on where you stop using the tool. When AI supports the background strategy, it gives you more time to create a better page. When you let AI write the entire copy, the strategy fails.</p>


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<h2 id="h-what-happens-when-ai-writes-the-page" class="wp-block-heading">What happens when AI writes the page?</h2>



<p class="wp-block-paragraph">To see the true cost of choosing quantity over quality, you can track performance across three common writing methods using first-party data from Google Analytics 4 (GA4) and Google Search Console (GSC). The following GSC data compares blog articles using each approach.</p>



<h3 id="h-1-pure-ai-content" class="wp-block-heading">1. Pure AI content</h3>



<p class="wp-block-paragraph">This content is written entirely by AI from a simple prompt, with no human review before publication.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1777" height="407" src="https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-2.png" alt="" class="wp-image-484010" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-2.png 1777w, https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-2-768x176.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-2-1536x352.png 1536w" sizes="(max-width: 1777px) 100vw, 1777px" /></figure>
</div>


<p class="wp-block-paragraph"><strong>The result:</strong> Published in April 2025, these three pages generated some organic performance after launch. By January 2026, however, they had almost completely disappeared from search results.&nbsp;</p>



<h3 id="h-2-ai-generated-human-edited-content" class="wp-block-heading">2. AI-generated, human-edited content</h3>



<p class="wp-block-paragraph">This is a common middle ground. AI generates the first draft, and a human editor updates the headings, cleans up the grammar, and adjusts the formatting.</p>



<p class="wp-block-paragraph">Industry data shows that <a href="https://seoprofy.com/blog/is-ai-content-good-for-seo/">more than 86%</a> of marketers use this editing workflow to reduce production costs.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1778" height="639" src="https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-3.png" alt="" class="wp-image-484011" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-3.png 1778w, https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-3-768x276.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-3-1536x552.png 1536w" sizes="(max-width: 1778px) 100vw, 1778px" /></figure>



<p class="wp-block-paragraph"><strong>The result: </strong>These five articles were originally published using this hybrid approach, then rewritten entirely by hand earlier this year. That human revision drove modest year-over-year growth over the last three months, increasing clicks by 12% and impressions by 27%.</p>



<h3 id="h-3-human-written-content" class="wp-block-heading">3. Human-written content</h3>



<p class="wp-block-paragraph">This content starts with a real human perspective, such as messy internal notes or a documented customer solution. AI is used only for brainstorming or proofreading.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1775" height="389" src="https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-4.png" alt="" class="wp-image-484012" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-4.png 1775w, https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-4-768x168.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Lynch-Too-Efficient-to-Rank-Image-4-1536x337.png 1536w" sizes="(max-width: 1775px) 100vw, 1775px" /></figure>



<p class="wp-block-paragraph"><strong>The result: </strong>Published with <a href="https://developers.google.com/search/docs/fundamentals/creating-helpful-content" target="_blank" rel="noopener">Google&#8217;s helpful content guidelines</a> in mind, this article demonstrates the value of original authorship.&nbsp;</p>



<p class="wp-block-paragraph">Instead of declining over time, it continued to gain visibility through the winter before peaking with substantial click growth in early April. Investing more time upfront created a long-term asset that was <a href="https://searchengineland.com/human-content-ai-rank-google-study-473697">eight times more likely</a> to rank in the No. 1 position.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/ai-generated-content-google-search-experiment-472234">How AI-generated content performs in Google Search: A 16-month experiment</a></em></strong>



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<h2 id="h-how-google-changed-the-rules-for-mass-content-production" class="wp-block-heading">How Google changed the rules for mass content production</h2>



<p class="wp-block-paragraph">The benchmark reflects a broader shift in how Google evaluates content. Since AI writing tools became common, Google has continued updating its ranking systems to address <a href="https://developers.google.com/search/docs/essentials/spam-policies" target="_blank" rel="noopener">scaled content abuse</a>. What started with the helpful content system is now part of Google&#8217;s core ranking systems.</p>



<p class="wp-block-paragraph">The <a href="https://searchengineland.com/google-march-2026-core-update-rollout-is-now-complete-473883">March</a> and <a href="https://searchengineland.com/google-may-2026-core-update-rollout-is-now-complete-479119">May 2026 core updates</a> reinforced that direction, rewarding sites with original research, firsthand experience, and distinctive brand voices while reducing the visibility of large volumes of low-value content.</p>



<p class="wp-block-paragraph">Google&#8217;s systems are designed to surface new information. When your publishing process prioritizes output over originality, your content becomes harder to distinguish from everything already published.</p>



<h2 id="h-human-readers-notice-the-same-things-search-engines-reward" class="wp-block-heading">Human readers notice the same things search engines reward</h2>



<p class="wp-block-paragraph">While search engines look for unique information, human readers can spot automated writing almost instantly. When every content team relies on the same tools, the same repetitive vocabulary starts to appear.</p>



<p class="wp-block-paragraph">Words like “delve,” “surge,” and “paramount” quickly signal that AI played a significant role in writing the copy. Corporate marketing was already full of buzzwords like “synergy,” “paradigm shift,” and “holistic” before AI arrived. AI has made that language more common, forcing readers to work through paragraphs of empty copy before they understand what a company actually does or sells.</p>



<p class="wp-block-paragraph">AI humanizer tools don&#8217;t solve the problem. While they may rewrite automated copy to avoid detection, they often replace one recognizable pattern with another. Readers still notice the awkward phrasing these tools create. Using one machine to hide another&#8217;s work doesn&#8217;t make the writing more useful.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/ai-write-seo-content-real-experience-479818">AI can write SEO content, but it can’t replace real experience</a></em></strong>



<h3 id="h-common-ai-terms-to-exclude-from-your-prompts" class="wp-block-heading">Common AI terms to exclude from your prompts</h3>



<p class="wp-block-paragraph">If you’re using an LLM to organize your thoughts or build an initial outline, include a negative prompt that bans these specific words and phrases. This prevents the machine&#8217;s repetitive habits from bleeding into your foundational ideas:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p class="wp-block-paragraph">delve, synthesize, semantic, surge, tapestry, paramount, vital, sustain, velocity, deploy, underscore, pivotal, synergy, realm, harness, illuminate, holistic, facilitate, refine, bolster, differentiate, streamline, revolutionize, innovative, cutting-edge, game-changing, transformative, robust, arguably, typically, at its core, that being said, to put it simply, key takeaway, broader perspective, generally speaking, to some extent, broadly speaking, seamless integration, scalable solution, indelible mark, stark reminder, nuanced understanding, complex interplay, unwavering commitment, pivotal moment, navigate the complex, turning point, transformative power, relentless pursuit, multi-faceted approach, significant milestone, pave the way, step forward, far-reaching implications, comprehensive framework, unique blend, delicate balance, path ahead, laid the groundwork, root cause, ongoing dialogue, paradigm shift, diverse perspective, comprehensive overview, potentially lead to, particularly noteworthy, in stark contrast</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/ai-writing-tics-engagement-study-470051">The AI writing tics that hurt engagement: A study</a></em></strong>
</blockquote>


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<h2 id="h-keep-human-expertise-at-the-center" class="wp-block-heading">Keep human expertise at the center</h2>



<p class="wp-block-paragraph">The benchmark makes the tradeoff clear. AI delivers the most value when it accelerates research, organization, and analysis. The strongest-performing content still begins with human expertise and original thinking.</p>



<p class="wp-block-paragraph">Protecting your time shouldn&#8217;t cost your brand its voice. As Google continues rewarding original content, the most successful SEO strategies won&#8217;t come from publishing more pages. They&#8217;ll come from publishing pages that offer something AI can&#8217;t: firsthand experience, original insights, and a distinctive perspective.</p>



<p class="wp-block-paragraph">Use AI to tackle the repetitive work. Save your own time and attention for the thinking, expertise, and creativity that differentiate your content from everything else on the web.</p>

AI has made SEO more efficient than ever. Tasks that once took hours now take minutes, and it’s tempting to let that efficiency extend from research all the way through content production.
But there’s a tradeoff. The faster AI makes it to publish, the easier it becomes to sacrifice the original thinking, firsthand experience, and distinctive perspective that Google increasingly rewards. At some point, efficiency stops helping your SEO and starts hurting it.
The solution isn’t to stop using AI. It’s to use it where it creates leverage while keeping human expertise at the center of your content.
Where AI belongs in your SEO workflow
Yes, you can use AI for SEO strategy and junior-level work, but your level of investment in a prompt or chat directly affects the quality of the output. The more context and data you provide, the more useful the results become. That makes AI highly effective for brainstorming strategy, analyzing data patterns, and organizing project frameworks.
For example, Gemini grouped more than 2,000 declining Page 1 keywords into topical clusters, condensing hours of manual analysis into minutes.
Source: Gemini, Keyword categorization for ecommerce website, keywords pulled from Ahrefs



After importing Google Search Console data, Gemini mapped those keyword themes to the URLs losing visibility, producing a focused list of pages to optimize.
That’s where the human takes over.
The line between helpful efficiency and search problems depends on where you stop using the tool. When AI supports the background strategy, it gives you more time to create a better page. When you let AI write the entire copy, the strategy fails.
AI recommends.
      
See your AI visibility
    
What happens when AI writes the page?
To see the true cost of choosing quantity over quality, you can track performance across three common writing methods using first-party data from Google Analytics 4 (GA4) and Google Search Console (GSC). The following GSC data compares blog articles using each approach.
1. Pure AI content
This content is written entirely by AI from a simple prompt, with no human review before publication.




The result: Published in April 2025, these three pages generated some organic performance after launch. By January 2026, however, they had almost completely disappeared from search results. 
2. AI-generated, human-edited content
This is a common middle ground. AI generates the first draft, and a human editor updates the headings, cleans up the grammar, and adjusts the formatting.
Industry data shows that more than 86% of marketers use this editing workflow to reduce production costs.




The result: These five articles were originally published using this hybrid approach, then rewritten entirely by hand earlier this year. That human revision drove modest year-over-year growth over the last three months, increasing clicks by 12% and impressions by 27%.
3. Human-written content
This content starts with a real human perspective, such as messy internal notes or a documented customer solution. AI is used only for brainstorming or proofreading.




The result: Published with Google’s helpful content guidelines in mind, this article demonstrates the value of original authorship. 
Instead of declining over time, it continued to gain visibility through the winter before peaking with substantial click growth in early April. Investing more time upfront created a long-term asset that was eight times more likely to rank in the No. 1 position.
Dig deeper: How AI-generated content performs in Google Search: A 16-month experiment




	
	
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See terms.
		
How Google changed the rules for mass content production
The benchmark reflects a broader shift in how Google evaluates content. Since AI writing tools became common, Google has continued updating its ranking systems to address scaled content abuse. What started with the helpful content system is now part of Google’s core ranking systems.
The March and May 2026 core updates reinforced that direction, rewarding sites with original research, firsthand experience, and distinctive brand voices while reducing the visibility of large volumes of low-value content.
Google’s systems are designed to surface new information. When your publishing process prioritizes output over originality, your content becomes harder to distinguish from everything already published.
Human readers notice the same things search engines reward
While search engines look for unique information, human readers can spot automated writing almost instantly. When every content team relies on the same tools, the same repetitive vocabulary starts to appear.
Words like “delve,” “surge,” and “paramount” quickly signal that AI played a significant role in writing the copy. Corporate marketing was already full of buzzwords like “synergy,” “paradigm shift,” and “holistic” before AI arrived. AI has made that language more common, forcing readers to work through paragraphs of empty copy before they understand what a company actually does or sells.
AI humanizer tools don’t solve the problem. While they may rewrite automated copy to avoid detection, they often replace one recognizable pattern with another. Readers still notice the awkward phrasing these tools create. Using one machine to hide another’s work doesn’t make the writing more useful.
Dig deeper: AI can write SEO content, but it can’t replace real experience



Common AI terms to exclude from your prompts
If you’re using an LLM to organize your thoughts or build an initial outline, include a negative prompt that bans these specific words and phrases. This prevents the machine’s repetitive habits from bleeding into your foundational ideas:
delve, synthesize, semantic, surge, tapestry, paramount, vital, sustain, velocity, deploy, underscore, pivotal, synergy, realm, harness, illuminate, holistic, facilitate, refine, bolster, differentiate, streamline, revolutionize, innovative, cutting-edge, game-changing, transformative, robust, arguably, typically, at its core, that being said, to put it simply, key takeaway, broader perspective, generally speaking, to some extent, broadly speaking, seamless integration, scalable solution, indelible mark, stark reminder, nuanced understanding, complex interplay, unwavering commitment, pivotal moment, navigate the complex, turning point, transformative power, relentless pursuit, multi-faceted approach, significant milestone, pave the way, step forward, far-reaching implications, comprehensive framework, unique blend, delicate balance, path ahead, laid the groundwork, root cause, ongoing dialogue, paradigm shift, diverse perspective, comprehensive overview, potentially lead to, particularly noteworthy, in stark contrast
Dig deeper: The AI writing tics that hurt engagement: A study




  
customers won’t either.
      
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Keep human expertise at the center
The benchmark makes the tradeoff clear. AI delivers the most value when it accelerates research, organization, and analysis. The strongest-performing content still begins with human expertise and original thinking.
Protecting your time shouldn’t cost your brand its voice. As Google continues rewarding original content, the most successful SEO strategies won’t come from publishing more pages. They’ll come from publishing pages that offer something AI can’t: firsthand experience, original insights, and a distinctive perspective.
Use AI to tackle the repetitive work. Save your own time and attention for the thinking, expertise, and creativity that differentiate your content from everything else on the web.

The biggest gains come from using AI to accelerate strategy, not replace original thinking. See where to draw the line.

AI makes SEO faster, but human expertise still wins

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-your-Google-Merchant-Center-feed-matters-more-in-AI-shopping.png" class="attachment-large size-large wp-post-image" alt="Why your Google Merchant Center feed matters more in AI shopping" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-your-Google-Merchant-Center-feed-matters-more-in-AI-shopping.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-your-Google-Merchant-Center-feed-matters-more-in-AI-shopping-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-your-Google-Merchant-Center-feed-matters-more-in-AI-shopping-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-your-Google-Merchant-Center-feed-matters-more-in-AI-shopping-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI shopping uses structured product data to decide what shoppers see. Here's why your Google Merchant Center feed is key to discovery.

If you ask ChatGPT to recommend a product, it’ll show you a carousel with eight product options.
In a March 2026 study, Tom Wells examined where these products originate. Out of more than 43,000 products, 83% matched Google’s top 40 organic Shopping results. For Bing, only 11% matched, and almost all of those were also found on Google.
The products AI shoppers see don’t come from the open web, your product detail pages (PDPs), or your reviews. Instead, they’re pulled from a single file most brands haven’t checked since setting up paid Shopping: your Google Merchant Center feed.
With AI shopping, your products’ visibility depends on the quality and accuracy of your feed. Your product detail page has taken a back seat. Your feed is now the real star.
The product feed decides which items show up first
ChatGPT builds its product carousel using shopping query fan-outs. These queries are separate from the search queries that generate the answer text.
Wells found that one of these queries often pulls a single page of Google Shopping results to populate an eight-product carousel, and 60% of strong matches come from the top 10 Shopping results. The order of products in the carousel matches their ranking in Google Shopping. Wells isn’t the only one seeing this.
Profound reviewed more than 1 million ChatGPT shopping offers in June, with an even more striking finding: Of the product citations ChatGPT pulled directly from merchant feeds, about 99.9% appeared as the top product offer.
Source: What ChatGPT actually looks for and how to get your products cited higher in AI Shopping, Profound



The share of feed-sourced retrievals also grew from 4.3% to about 20% of all ChatGPT shopping retrievals in just six weeks. The reason is completeness.
In Profound’s data, feed-sourced offers populated brand, product image, and merchant details 100% of the time, compared with 0% for page-scraped offers. They also carried ChatGPT’s “best price” tag 100% of the time, compared with 21% for page-scraped offers.
The feed provides the LLM with clean, structured fields rather than forcing it to infer that information from the page.
Malte Landwehr of Peec AI, whose data supported the Wells study, shared that he added a new shop to Merchant Center and saw it appear in Google Shopping the next day, then in ChatGPT. If you connect your feed, your products can show up.
Skip it, and you might go invisible.
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The real catch and what it means for your PDP
Most advice about optimizing for AI leaves out an important detail.
According to Profound’s analysis, about 88% of ChatGPT product offers still come from web product detail pages rather than feeds. Even for merchants already using feeds, around 76% of offers still came from the page. This means the feed doesn’t replace the PDP.
The feed helps with ranking, but most offers still come from the PDP.
Your catalog serves two main purposes.
The feed determines whether you’re included in the selection and where you rank. The PDP is where you convince shoppers to buy and gather reviews and coverage that influence how AI models talk about your brand.
However, getting coverage alone doesn’t guarantee you’ll be chosen. When Lily Ray studied brands that ranked themselves No. 1 in their own listicles, about 69% were cited but not recommended.
Instead, the top spot often went to a larger competitor on the same list. These “ghost” rankings are where your content is used as the source, but someone else gets picked.
On-page structure alone isn’t enough for AI shopping. 
In June 2026, our team analyzed 11,400 AI shopping answers across ChatGPT, Perplexity, and Gemini. We found that category structure didn’t affect whether AI recommended a brand on any platform.
Optimize one surface using the other’s playbook, and you’ll underperform on both.
Agencies are already reorganizing around this. As Andre de Gaye, sales director at Charle, put it: 




His team is “moving away from treating SEO and feed management as separate silos.”
What agents actually read
An AI shopping agent doesn’t browse your site the way a person does. It reads structured attributes and product details.
OpenAI explains that product results are organic and unsponsored. They’re ranked by relevance using signals such as “availability, price, quality, and whether a merchant is the primary seller.”
These signals come from catalogs and feeds, not on-page copy. The catalogs now include major retailers like Target, Sephora, Nordstrom, Best Buy, The Home Depot, and millions of Shopify merchants.
The non-negotiable core is the data Google Shopping has always rewarded: a valid GTIN, an accurate title, price and availability that match your live site, a clean image, brand, and the correct product category.
Get those wrong, and nothing downstream matters.
Google continues to add to these basics. In July, it started supporting the product category property, which includes both Google’s taxonomy and your product types in merchant listing structured data. Sale duration fields were also added.
The number of signals agents can read about a product keeps increasing, and all of them come from the feed.
At Google Marketing Live 2026, Google added another layer by introducing conversational attributes to the Merchant Center product data specification.
These optional fields are meant for AI features like AI Mode and Gemini. Google lists:
Question and answer: Structured Q&A pairs. This is a great starting point because it lets you answer questions like “Does this work for air travel?” before the shopper asks.
Related product: Defines relationships using types like often_bought_with, required_part, accessory, and substitute. This helps an agent suggest a complete solution instead of a single product.
Document link: Lets you include supporting PDFs, such as manuals, spec sheets, or sizing guides.
Item group title and variant option: Connects variants to a product family and matches queries like “Show me this in black, medium.”
Popularity rank: A score showing how a product performs against the rest of your catalog, so a model can answer questions like “What’s your best-selling running shoe?”
None of these affect whether your product is approved.
They’re all optional enhancements that let you provide information that previously lived only on the page or in a PDF a model couldn’t always read.




Hidden feed problems that reduce your visibility
For most brands, the main challenge isn’t strategy. It’s keeping everything updated and well maintained.
In Google Merchant Center, common problems include missing or incorrect GTINs, image issues, and shipping or price details that don’t match your website. If a product is disapproved, it won’t just rank lower in AI shopping results.
It won’t show up at all.
The most harmful gaps are often the ones that don’t trigger any errors.
Titles that are too generic or template-based often miss details shoppers care about, such as material, use case, compatibility, or size.
Boilerplate descriptions make it harder for a model to extract detailed information.
If variant data is missing, searches for terms like “black, medium” won’t return any results.
If your pricing isn’t clear or can’t be read by AI, it won’t help. Previsible looked at 6.77 million AI-referred sessions and found that “contact us for pricing” gives AI nothing to compare or recommend.
If your availability information is out of date, AI might recommend products that are no longer available.
This problem is easy to measure.
Product detail pages scored only 63.5 for AI citation readability, even for top retailers, according to Adobe’s Q2 AI Traffic report. That’s much lower than their homepages and buying guides, which scored in the low 80s.
Source: Quarterly AI Traffic Report Adobe Digital Insights April 2026



The pages containing the product data AI needs are often the hardest for machines to read. Each missing detail acts as a silent filter.
Your product might be approved and indexed, but it can still be invisible when someone searches in natural language.
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This is where customers decide to buy
If AI shopping were still a minor trend, there’d be no hurry. But things have changed.
The same Adobe report found that traffic from AI sources to U.S. retail sites grew by 393% year over year in the first quarter, and by December, it was up more than 1,150%.
Source: Quarterly AI Traffic Report Adobe Digital Insights April 2026



By March, AI-referred traffic converted 42% better than non-AI traffic, while a year earlier, it converted at only about half that rate.
Salesforce also reported that about 20% of global online holiday sales — roughly $262 billion — were linked to AI and agents.
AI-referred traffic converted at about eight times the rate of social traffic. AI platforms are now bringing shoppers who are ready to buy to retail sites.
The key question is: Are your products being recommended?
How to see if your product feed is ready for AI
You can check your product feed this week. Focus on these four areas.
Eligibility
Check your Merchant Center for disapprovals and diagnostics. Fix GTIN errors and price mismatches first. Any disapproved product can’t be shown by AI agents. This is the first requirement.
Coverage and specificity
Look at your top 50 revenue products and review their titles and descriptions as if you were an AI agent.
Do the titles mention the features buyers care about?
If the description just repeats the title, it doesn’t add value or help shoppers choose your product.
Conversational attributes
Begin by adding question-and-answer sections to your best sellers. Then link related products and include a popularity rank. Focus on the SKUs that already generate revenue. You don’t need to update all 40,000 items right away.
Freshness
Keep prices and availability continuously in sync with your live site. AI shopping surfaces refresh constantly, so a feed that updates just once a day is already behind.
If you follow these four steps, you may find the issue isn’t your strategy. Instead, your feed may have been set up for paid Shopping years ago and hasn’t been updated for discovery since.




The platforms are converging on the feed
Google’s Shopping Graph now holds 60 billion product listings, up from 50 billion earlier in the year.
Google has also worked with Shopify, Etsy, Wayfair, Target, and Walmart to create the Universal Commerce Protocol, which uses your existing Merchant Center feed for agentic checkout.
To opt in, merchants add a new native_commerce attribute to the feed and keep their product, offer, and review schema in sync. Miss it, and products are ineligible for AI-powered checkout.
As Jason Tabeling noted in July, Merchant Center is “no longer simply for Shopping ads. It’s becoming the primary source of product data for AI discovery.”
Meanwhile, presence on AI surfaces is decoupling from classic rankings.
A July study by SE Ranking found that only about 2.32% of advertisers appearing in Google’s AI Mode also ranked organically for the same search. Around 85% didn’t appear in organic results at all.
The old signals and the new ones are pulling apart. Kevin Indig explained the change clearly:
“The last decade rewarded marketing arbitrage. Agentic commerce rewards product truth.”
The way people complete purchases is still evolving. For instance, Walmart tested checkout inside ChatGPT, but it converted at only about one-third the rate of Walmart’s own website.
OpenAI has also stopped offering its hosted checkout. Still, shoppers are already turning to AI for product discovery, and this shift relies on the product feed.
customers won’t either.
      
See your AI visibility
    
AI shopping starts with your product feed
Traditional SEO is still important. But AI doesn’t pick the best-looking product page.
AI chooses the feed that answers shoppers’ questions before they even ask. It looks for information that’s accurate, complete, and written in everyday language.
Most brands haven’t touched that file since they set it up for paid Shopping. The brands that update it now will be ahead when everyone else catches up.
The next time ChatGPT shows a shopper eight products, ask yourself one question:
Is your product one of them?
That all comes down to your feed.

AI shopping uses structured product data to decide what shoppers see. Here's why your Google Merchant Center feed is key to discovery.

AI shopping starts with your product feed, not your product page

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem.png" class="attachment-large size-large wp-post-image" alt="Blended Google Ads campaigns can hide your biggest growth problem" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Strong ROAS can mask where your budget goes. Here's how separating brand campaigns drives revenue growth and new customer acquisition.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem.png" class="attachment-large size-large wp-post-image" alt="Blended Google Ads campaigns can hide your biggest growth problem" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Blended-Google-Ads-campaigns-can-hide-your-biggest-growth-problem-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">When auditing accounts, one of the most common mistakes companies make is allowing brand and non-brand traffic to live together in the same campaigns. This is textbook poor PPC management.</p>



<p class="wp-block-paragraph">Whether it&#8217;s <a href="https://searchengineland.com/google-performance-max-431821">Performance Max</a>, Search, or Standard Shopping, blending brand and non-brand traffic inflates reported ROAS while limiting your ability to scale revenue.</p>



<p class="wp-block-paragraph">If your goal is to maximize short-term ROAS, automation will naturally gravitate toward branded searches. Why? Because they&#8217;re cheaper, convert at higher rates, and are an easy win for the algorithm.&nbsp;</p>



<p class="wp-block-paragraph">Google will take the easiest possible route to achieve the goal you&#8217;re asking it to. Those users were often already looking for you. So why are you paying for them? Why lump brand traffic in with campaigns designed to drive incremental growth?</p>



<p class="wp-block-paragraph">If your objective is to grow the business, acquire new customers, and increase market share, you need to know how much of your budget is creating demand rather than capturing existing demand.</p>



<h2 id="h-the-problem-with-blended-automation" class="wp-block-heading">The problem with blended automation</h2>



<p class="wp-block-paragraph">When brand and non-brand traffic are combined, a few things happen beneath the surface:</p>



<ul class="wp-block-list">
<li>Brand searches often consume the majority of the budget.</li>



<li>Overall ROAS looks stronger than it actually is.</li>



<li>Non-brand products and categories struggle to gain visibility.</li>



<li>Budget shifts toward the easiest conversions instead of the biggest growth opportunities.</li>



<li>Brand campaigns can take credit for other media channels (CTV, programmatic, etc.) without an MMM in place.</li>
</ul>



<p class="wp-block-paragraph">This creates a dangerous feedback loop. Automation sees brand traffic performing well, allocates more budget to it, reports excellent efficiency, and reinforces the same behavior.</p>



<p class="wp-block-paragraph">Meanwhile, opportunities to reach new customers become increasingly limited.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0101ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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<h2 id="h-a-case-study-in-incremental-growth" class="wp-block-heading">A case study in incremental growth</h2>



<p class="wp-block-paragraph">Before working together, one client&#8217;s Google Ads account was heavily weighted toward branded traffic.</p>



<p class="wp-block-paragraph">Before our team took over, brand and non-brand campaigns were blended, product segmentation was minimal, and a significant portion of spend flowed toward branded searches. </p>



<p class="wp-block-paragraph">While overall PPC performance appeared healthy, much of the revenue came from customers who were already familiar with the brand.</p>



<p class="wp-block-paragraph">When they turned to us to improve performance, their goals were clear:</p>



<ul class="wp-block-list">
<li>Increase total business revenue.</li>



<li>Acquire more new customers.</li>



<li>Grow non-brand revenue.</li>



<li>Reduce reliance on branded search.</li>
</ul>



<p class="wp-block-paragraph">Instead of optimizing for the highest reported ROAS, we restructured the account around growth. Yes, that meant our overall ROAS would decline, but that was exactly the goal.</p>



<h2 id="h-how-we-restructured-the-account-for-growth" class="wp-block-heading">How we restructured the account for growth</h2>



<h3 id="h-1-separate-brand-and-non-brand" class="wp-block-heading">1. Separate brand and non-brand</h3>



<p class="wp-block-paragraph">The first step was isolating branded traffic from non-branded campaigns.</p>



<p class="wp-block-paragraph">This immediately gave us clearer visibility into budget allocation and how each traffic source contributed to overall performance.</p>



<p class="wp-block-paragraph">Brand campaigns remained important. We weren&#8217;t trying to eliminate them, but they were no longer allowed to absorb budget that could be used to generate incremental growth.&nbsp;</p>



<p class="wp-block-paragraph">We reduced the branded campaign budget to a small percentage of overall spend, allowing the vast majority of dollars to flow to campaigns better suited to drive incremental growth and acquire new customers.</p>



<h3 id="h-2-build-granular-product-segmentation" class="wp-block-heading">2. Build granular product segmentation</h3>



<p class="wp-block-paragraph">Rather than relying on broad Shopping structures, we rebuilt the non-brand campaign structure using Standard Shopping campaigns with significantly more product segmentation.</p>



<p class="wp-block-paragraph">This allowed us to:</p>



<ul class="wp-block-list">
<li>Allocate budget by product priority.</li>



<li>Bid differently across product groups.</li>



<li>Invest more aggressively in strategic categories.</li>



<li>Scale products that previously received very little visibility.</li>
</ul>



<p class="wp-block-paragraph">Instead of treating every product the same, the account could prioritize the areas with the greatest growth potential.</p>



<p class="wp-block-paragraph">This is another common challenge with automation. Left on its own, Google&#8217;s algorithms naturally allocate more budget toward products that already generate the strongest performance. While that can maximize short-term efficiency, it often leaves lower-volume products, emerging categories, or strategic product lines with little opportunity to gain traction.</p>



<p class="wp-block-paragraph">When an entire catalog is grouped into a single campaign, it&#8217;s nearly impossible to prioritize products based on business objectives. You&#8217;re essentially asking Google to decide what&#8217;s most important, and its answer isn&#8217;t always aligned with yours.</p>



<p class="wp-block-paragraph">By segmenting products around business goals, we regained that control. We could intentionally invest in high-priority categories, newer products, or underperforming product groups with untapped potential, forcing the algorithm to compete in the areas where growth mattered most.</p>



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<h3 id="h-3-use-performance-max-for-what-it-does-best" class="wp-block-heading">3. Use Performance Max for what it does best</h3>



<p class="wp-block-paragraph">Rather than asking Performance Max to do everything, we assigned it a specific role.</p>



<p class="wp-block-paragraph">We leveraged <a href="https://searchengineland.com/how-to-use-performance-max-for-high-value-customer-acquisition-and-retention-458843">Performance Max&#8217;s New Customer Acquisition</a> setting to focus on acquiring incremental customers while Standard Shopping maintained greater control over product-level bidding and budget allocation.</p>



<p class="wp-block-paragraph">This created a balance between automation and manual control instead of relying entirely on one campaign type.</p>



<h2 id="h-the-results-looking-beyond-paid-search-performance" class="wp-block-heading">The results: Looking beyond paid search performance</h2>



<p class="wp-block-paragraph">On paper, one &#8220;important&#8221; metric looked worse.</p>



<p class="wp-block-paragraph">Google PPC revenue declined 25% year over year, representing approximately $2.3 million less in paid search revenue.</p>



<p class="wp-block-paragraph">If you looked only at Google Ads reporting, it would appear performance had declined.</p>



<p class="wp-block-paragraph">But that wasn&#8217;t the full story.</p>



<p class="wp-block-paragraph">Because we intentionally reduced our dependence on branded paid traffic, more branded searches naturally shifted to organic listings.</p>



<p class="wp-block-paragraph">As a result:</p>



<ul class="wp-block-list">
<li>Google organic revenue increased 99% year over year.</li>



<li>Combined Google PPC and organic revenue increased 15% year over year.</li>



<li>New customer acquisition increased 20% year over year.</li>
</ul>



<p class="wp-block-paragraph">Instead of paying for customers who likely would&#8217;ve found the brand anyway, we allowed organic search to capture more branded demand while investing paid media in expanding the customer base.</p>



<p class="wp-block-paragraph">As a result, the business grew even though paid revenue alone decreased.</p>



<h2 id="h-why-brand-spend-still-matters" class="wp-block-heading">Why brand spend still matters</h2>



<p class="wp-block-paragraph">Everything we&#8217;ve discussed up to this point might make it sound like brand spend is something to avoid. But that&#8217;s not the case.</p>



<p class="wp-block-paragraph">For most retailers, brand campaigns are a necessary part of an account strategy. In highly competitive industries, if someone searches for your brand, the last thing you want is a competitor&#8217;s Shopping ad appearing instead of your own products.</p>



<p class="wp-block-paragraph">If your brand name includes a product term (e.g., Mattress Firm or Guitar Center), protecting your brand is even more important because competitors can more easily match branded queries.</p>



<p class="wp-block-paragraph">The challenge with Shopping campaigns is that Standard Shopping doesn&#8217;t provide a way to explicitly target only branded search queries. That means you need to get creative because you still need a way to protect the demand you&#8217;ve worked so hard to build.</p>



<p class="wp-block-paragraph">We&#8217;ve found a workaround: Create brand-focused Standard Shopping campaigns that:</p>



<ul class="wp-block-list">
<li>Include only the relevant products.</li>



<li>Apply a robust negative keyword list (often using our non-brand Search keywords as exact-match negatives).</li>



<li>Set an aggressive tROAS target. </li>
</ul>



<p class="wp-block-paragraph">Google naturally favors high-converting queries, and nothing converts as easily as brand traffic. As a result, the majority of spend shifts to brand queries when a high target is set.</p>



<p class="wp-block-paragraph">In some cases, we also apply a portfolio bid strategy with a maximum CPC limit to the brand campaign. Because non-brand queries generally drive higher CPCs, the cap helps filter out unwanted non-brand traffic. So yes, in many cases, brand spend is necessary. You just need to make sure your ad dollars have boundaries.</p>



<p class="wp-block-paragraph">To do that, define what success looks like. Maybe it&#8217;s maintaining a certain impression share on branded searches or hitting a specific ROAS target while protecting your brand from competitors. </p>



<p class="wp-block-paragraph">Whatever the objective, keep brand and non-brand strategies distinct so you can accurately measure performance and confidently invest in the areas driving incremental growth.</p>


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<h2 id="h-the-metrics-that-matter-aren-t-always-in-google-ads" class="wp-block-heading">The metrics that matter aren&#8217;t always in Google Ads</h2>



<p class="wp-block-paragraph">One of the biggest mistakes you can make is evaluating paid media in a vacuum.</p>



<p class="wp-block-paragraph">If reducing branded paid spend increases branded organic traffic, that&#8217;s not inherently a loss. In many cases, it&#8217;s exactly what should happen.</p>



<p class="wp-block-paragraph">The goal isn&#8217;t to maximize paid revenue at all costs. The goal is to maximize total business growth. Sometimes that means accepting lower paid media metrics in exchange for stronger overall business performance.</p>



<p class="wp-block-paragraph">Automation is incredibly powerful, but it optimizes toward the signals you give it. If brand and non-brand traffic are blended, the algorithm will often chase the easiest conversions rather than the most incremental ones.</p>



<p class="wp-block-paragraph">Separating branded traffic, building a thoughtful product segmentation strategy, and giving automation a clearly defined role gives you much better visibility into performance while creating room for sustainable growth.</p>



<p class="wp-block-paragraph">The strongest Google Ads accounts don&#8217;t rely solely on automation or solely on manual control. They combine both, using automation where it excels while maintaining enough structure to ensure budgets drive real business outcomes rather than simply capturing demand that already exists. That&#8217;s why we consider the 25% &#8220;performance loss&#8221; in the example above a success.</p>

When auditing accounts, one of the most common mistakes companies make is allowing brand and non-brand traffic to live together in the same campaigns. This is textbook poor PPC management.
Whether it’s Performance Max, Search, or Standard Shopping, blending brand and non-brand traffic inflates reported ROAS while limiting your ability to scale revenue.
If your goal is to maximize short-term ROAS, automation will naturally gravitate toward branded searches. Why? Because they’re cheaper, convert at higher rates, and are an easy win for the algorithm. 
Google will take the easiest possible route to achieve the goal you’re asking it to. Those users were often already looking for you. So why are you paying for them? Why lump brand traffic in with campaigns designed to drive incremental growth?
If your objective is to grow the business, acquire new customers, and increase market share, you need to know how much of your budget is creating demand rather than capturing existing demand.
The problem with blended automation
When brand and non-brand traffic are combined, a few things happen beneath the surface:
Brand searches often consume the majority of the budget.
Overall ROAS looks stronger than it actually is.
Non-brand products and categories struggle to gain visibility.
Budget shifts toward the easiest conversions instead of the biggest growth opportunities.
Brand campaigns can take credit for other media channels (CTV, programmatic, etc.) without an MMM in place.
This creates a dangerous feedback loop. Automation sees brand traffic performing well, allocates more budget to it, reports excellent efficiency, and reinforces the same behavior.
Meanwhile, opportunities to reach new customers become increasingly limited.
your competitors win.
      
Analyze your competitors
    
A case study in incremental growth
Before working together, one client’s Google Ads account was heavily weighted toward branded traffic.
Before our team took over, brand and non-brand campaigns were blended, product segmentation was minimal, and a significant portion of spend flowed toward branded searches. 
While overall PPC performance appeared healthy, much of the revenue came from customers who were already familiar with the brand.
When they turned to us to improve performance, their goals were clear:
Increase total business revenue.
Acquire more new customers.
Grow non-brand revenue.
Reduce reliance on branded search.
Instead of optimizing for the highest reported ROAS, we restructured the account around growth. Yes, that meant our overall ROAS would decline, but that was exactly the goal.
How we restructured the account for growth
1. Separate brand and non-brand
The first step was isolating branded traffic from non-branded campaigns.
This immediately gave us clearer visibility into budget allocation and how each traffic source contributed to overall performance.
Brand campaigns remained important. We weren’t trying to eliminate them, but they were no longer allowed to absorb budget that could be used to generate incremental growth. 
We reduced the branded campaign budget to a small percentage of overall spend, allowing the vast majority of dollars to flow to campaigns better suited to drive incremental growth and acquire new customers.
2. Build granular product segmentation
Rather than relying on broad Shopping structures, we rebuilt the non-brand campaign structure using Standard Shopping campaigns with significantly more product segmentation.
This allowed us to:
Allocate budget by product priority.
Bid differently across product groups.
Invest more aggressively in strategic categories.
Scale products that previously received very little visibility.
Instead of treating every product the same, the account could prioritize the areas with the greatest growth potential.
This is another common challenge with automation. Left on its own, Google’s algorithms naturally allocate more budget toward products that already generate the strongest performance. While that can maximize short-term efficiency, it often leaves lower-volume products, emerging categories, or strategic product lines with little opportunity to gain traction.
When an entire catalog is grouped into a single campaign, it’s nearly impossible to prioritize products based on business objectives. You’re essentially asking Google to decide what’s most important, and its answer isn’t always aligned with yours.
By segmenting products around business goals, we regained that control. We could intentionally invest in high-priority categories, newer products, or underperforming product groups with untapped potential, forcing the algorithm to compete in the areas where growth mattered most.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
3. Use Performance Max for what it does best
Rather than asking Performance Max to do everything, we assigned it a specific role.
We leveraged Performance Max’s New Customer Acquisition setting to focus on acquiring incremental customers while Standard Shopping maintained greater control over product-level bidding and budget allocation.
This created a balance between automation and manual control instead of relying entirely on one campaign type.
The results: Looking beyond paid search performance
On paper, one “important” metric looked worse.
Google PPC revenue declined 25% year over year, representing approximately $2.3 million less in paid search revenue.
If you looked only at Google Ads reporting, it would appear performance had declined.
But that wasn’t the full story.
Because we intentionally reduced our dependence on branded paid traffic, more branded searches naturally shifted to organic listings.
As a result:
Google organic revenue increased 99% year over year.
Combined Google PPC and organic revenue increased 15% year over year.
New customer acquisition increased 20% year over year.
Instead of paying for customers who likely would’ve found the brand anyway, we allowed organic search to capture more branded demand while investing paid media in expanding the customer base.
As a result, the business grew even though paid revenue alone decreased.
Why brand spend still matters
Everything we’ve discussed up to this point might make it sound like brand spend is something to avoid. But that’s not the case.
For most retailers, brand campaigns are a necessary part of an account strategy. In highly competitive industries, if someone searches for your brand, the last thing you want is a competitor’s Shopping ad appearing instead of your own products.
If your brand name includes a product term (e.g., Mattress Firm or Guitar Center), protecting your brand is even more important because competitors can more easily match branded queries.
The challenge with Shopping campaigns is that Standard Shopping doesn’t provide a way to explicitly target only branded search queries. That means you need to get creative because you still need a way to protect the demand you’ve worked so hard to build.
We’ve found a workaround: Create brand-focused Standard Shopping campaigns that:
Include only the relevant products.
Apply a robust negative keyword list (often using our non-brand Search keywords as exact-match negatives).
Set an aggressive tROAS target. 
Google naturally favors high-converting queries, and nothing converts as easily as brand traffic. As a result, the majority of spend shifts to brand queries when a high target is set.
In some cases, we also apply a portfolio bid strategy with a maximum CPC limit to the brand campaign. Because non-brand queries generally drive higher CPCs, the cap helps filter out unwanted non-brand traffic. So yes, in many cases, brand spend is necessary. You just need to make sure your ad dollars have boundaries.
To do that, define what success looks like. Maybe it’s maintaining a certain impression share on branded searches or hitting a specific ROAS target while protecting your brand from competitors. 
Whatever the objective, keep brand and non-brand strategies distinct so you can accurately measure performance and confidently invest in the areas driving incremental growth.
a customer you lose.
      
See who’s stealing your traffic
    
The metrics that matter aren’t always in Google Ads
One of the biggest mistakes you can make is evaluating paid media in a vacuum.
If reducing branded paid spend increases branded organic traffic, that’s not inherently a loss. In many cases, it’s exactly what should happen.
The goal isn’t to maximize paid revenue at all costs. The goal is to maximize total business growth. Sometimes that means accepting lower paid media metrics in exchange for stronger overall business performance.
Automation is incredibly powerful, but it optimizes toward the signals you give it. If brand and non-brand traffic are blended, the algorithm will often chase the easiest conversions rather than the most incremental ones.
Separating branded traffic, building a thoughtful product segmentation strategy, and giving automation a clearly defined role gives you much better visibility into performance while creating room for sustainable growth.
The strongest Google Ads accounts don’t rely solely on automation or solely on manual control. They combine both, using automation where it excels while maintaining enough structure to ensure budgets drive real business outcomes rather than simply capturing demand that already exists. That’s why we consider the 25% “performance loss” in the example above a success.

Strong ROAS can mask where your budget goes. Here's how separating brand campaigns drives revenue growth and new customer acquisition.

Why separating brand and non-brand campaigns improves ROAS

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI is generating more of the web, and it's carrying the same accessibility gaps with it. Marketing has the leverage to close them

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-1200x675.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/AudioEye-20260731.jpg" alt="" class="wp-image-483856" srcset="https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/AudioEye-20260731-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p class="wp-block-paragraph">AI is writing your copy, building your landing pages, and increasingly generating the code behind your digital experience. Which raises a question worth sitting with: if AI is building more of what your customers touch, who is making sure it works for everyone?</p>



<p class="wp-block-paragraph">For many brands, that question doesn&#8217;t have a clear answer yet. And the data shows it.</p>



<p class="wp-block-paragraph">As of the <a href="https://webaim.org/projects/million/" target="_blank" rel="noopener sponsored nofollow">2026 WebAIM Million report</a>, 95.9% of the top one million website homepages have detectable accessibility failures. The average page has 56.1 errors. And the number of errors increased 10.1% over the prior year, reversing six consecutive years of gradual improvement.</p>



<p class="wp-block-paragraph">That trend didn&#8217;t reverse by accident. According to that same report, the average homepage now contains 1,437 elements, up 22.5% in a single year and nearly double what it did in 2019. More code is being shipped, faster, with the same accessibility gaps baked in. If your team is using AI to build or update digital experiences, that pattern almost certainly includes you.</p>



<p class="wp-block-paragraph">Accessibility gets treated as an engineering problem, something you hand off and forget. But when AI is generating the experiences your customers touch, the gap becomes everyone&#8217;s problem, and marketing has more leverage to close it than most teams realize.</p>



<h2 id="h-ai-assisted-development-iis-making-the-web-less-accessible" class="wp-block-heading">AI-assisted development Iis making the web less accessible</h2>



<p class="wp-block-paragraph">The AI tools your team uses to write copy, build landing pages, and generate code were trained on the web as it exists. The web is largely inaccessible, and that&#8217;s exactly what these models learned from. The six most common failure types — low-contrast text, missing alt text, unlabeled form fields, empty links, empty buttons, and missing document language — have been the top failures for seven consecutive years.</p>



<p class="wp-block-paragraph">When a model learns from billions of pages of existing web content, it learns those patterns. Most AI tools today lack the training foundation needed to generate consistently accessible code.</p>



<p class="wp-block-paragraph">What makes this challenging for marketing teams is that these failures remain hidden until a user encounters them directly. The code looks fine. The page renders correctly. The gap only surfaces when someone navigates with a screen reader, or tabs through a form with a keyboard. By then, someone has already had a bad experience on your site, or it has already been flagged for legal action.</p>



<p class="wp-block-paragraph">The New York City Bar Association put it plainly in a<a href="https://www.nycbar.org/reports/the-impact-of-the-use-of-ai-on-people-with-disabilities/" target="_blank" rel="noopener sponsored nofollow"> June 2025 report</a>: &#8220;AI cannot solve for accessibility if it was never trained to recognize it.&#8221; Asking an AI tool to generate accessible code is asking it to do something it has no foundation to do well. The failures stay invisible until a real user encounters them, or a plaintiff&#8217;s law firm flags your site.</p>



<h2 id="h-accessibility-lawsuits-have-doubled-since-2020-and-ecommerce-is-the-primary-target" class="wp-block-heading">Accessibility lawsuits have doubled since 2020, and ecommerce is the primary target</h2>



<p class="wp-block-paragraph">Marketing leaders understand the reputational impact of accessibility litigation. The data shows this risk is both real and growing.</p>



<p class="wp-block-paragraph">According to AudioEye&#8217;s <a href="https://www.audioeye.com/guides/2026-web-accessibility-litigation-report/?utm_source=pr&amp;utm_medium=article&amp;utm_campaign=2026-sponsored-article&amp;utm_term=martech" target="_blank" rel="noopener sponsored nofollow">2026 Web Accessibility Litigation Report</a>, U.S. digital accessibility lawsuits have doubled since 2020. 78% target ecommerce. Nearly eight in 10 are now filed in state courts, where damages can compound quickly. In 2025 alone, 26,253 combined federal and state claims were filed.</p>



<p class="wp-block-paragraph">The most instructive data point from that report: 38.5% of companies that faced accessibility lawsuits already had an accessibility tool in place when they were sued. They thought the problem was handled. They had partial coverage and didn&#8217;t know it. Litigation often reveals these gaps publicly, with significant reputational consequences.</p>



<p class="wp-block-paragraph">This isn&#8217;t only a U.S. problem. On June 4, 2026, a French court <a href="https://www.faire-face.fr/2026/06/11/pour-le-tribunal-judiciaire-de-caen-laccessibilite-des-sites-de-e-commerce-doit-etre-totale/" target="_blank" rel="noopener sponsored nofollow">ordered Carrefour</a> to bring carrefour.fr and its app to full accessibility conformance within six months, the first ruling under the European Accessibility Act to reject partial conformance as a legal defense. Carrefour&#8217;s own accessibility statements had claimed 50% to 70% conformance across its platforms. The court&#8217;s reasoning was direct: a site can&#8217;t be somewhat accessible. It works or it doesn&#8217;t.</p>



<p class="wp-block-paragraph">Settlement costs typically run $15,000 to $75,000. Legal fees run $10,000 to $30,000 on top of that. And those are the cases that settle quickly. One widely-cited case against a <a href="https://www.boia.org/blog/fashion-nova-settles-web-accessibility-lawsuit-for-5.15-million" target="_blank" rel="noopener sponsored nofollow">major retailer</a> began as a routine accessibility claim in 2020 and ended five years later with over 200 court filings and a total cost of $5.15 million. Defending against these claims often costs more than early settlement, and significantly more than solving it proactively.</p>



<h2 id="h-inaccessible-digital-experiences-are-turning-away-a-18-trillion-market" class="wp-block-heading">Inaccessible digital experiences are turning away a $18 trillion market</h2>



<p class="wp-block-paragraph">More than 1.3 billion people globally live with a disability. Together with their family and friends, they represent over $18 trillion in spending power, according to the <a href="https://www.rod-group.com/research-insights/annual-report-2024/" target="_blank" rel="noopener sponsored nofollow">Return on Disability Group</a>. <a href="https://www.clickawaypound.com/downloads/cap19final0502.pdf" target="_blank" rel="noopener sponsored nofollow">Seven out of 10 customers</a> with disabilities will leave a website if it&#8217;s inaccessible, and 83% limit their shopping to sites they know work for them.</p>



<p class="wp-block-paragraph">Your marketing spend is already allocated to reach this audience. An inaccessible digital experience turns them away before your message can resonate.</p>



<p class="wp-block-paragraph">Brand trust is built or lost at the experience level. Accessibility is no different. The brands that are known for getting this right build loyalty that doesn&#8217;t appear in any single quarter but compounds across cycles. Those that overlook accessibility experience abandoned sessions, negative word of mouth, and in some cases, public litigation.</p>



<h2 id="h-3-things-marketing-leaders-can-do-right-now" class="wp-block-heading">3 things marketing leaders can do right now</h2>



<p class="wp-block-paragraph">Accessibility doesn&#8217;t have to live entirely in engineering. Marketing leaders have more leverage here than most realize.</p>



<p class="wp-block-paragraph"><strong>1. Scrutinize your tools, the ones you have and the ones you&#8217;re about to buy.</strong> If your team is using AI to build or update digital experiences, find out what that AI can and cannot detect. Automated tools can only detect about two-thirds of accessibility issues, and of those, fix about half. The issues outside that range are often the ones most likely to trigger a lawsuit.</p>



<p class="wp-block-paragraph">That scrutiny matters even more as you revise your stack. As you evaluate new AI-powered platforms, whether it&#8217;s a CMS, a personalization engine, or a code generator, don&#8217;t assume the vendor solved accessibility because they solved speed. Ask what the tool does and doesn&#8217;t detect, whether a human ever reviews the output, and who owns the gap when it&#8217;s wrong. Partnering with a <a href="https://www.audioeye.com/?utm_source=pr&amp;utm_medium=article&amp;utm_campaign=2026-sponsored-article&amp;utm_term=martech" target="_blank" rel="noopener sponsored nofollow">solution like AudioEye</a>, which combines AI-powered automation with expert-written custom fixes, gives you coverage that no automated tool delivers on its own.</p>



<p class="wp-block-paragraph"><strong>2. Connect accessibility to the metrics you already report. </strong>Baseline conversion rate and bounce rate on a high-traffic page, fix it, then measure the same page again. Put the accessibility issue count in the same report as CAC and conversion rate, not a separate compliance dashboard nobody in marketing reads. This is a revenue and reach conversation, not a compliance cost.</p>



<p class="wp-block-paragraph"><strong>3. Make accessibility part of your campaign launch process.</strong> No digital experience, whether a landing page, a product launch, or a seasonal campaign, should go live without an accessibility check. Treat it the way you treat brand safety in media buying: a non-negotiable before anything goes out the door.</p>



<p class="wp-block-paragraph">The AI tools your team relies on are not going away, and they shouldn&#8217;t. But speed without accessibility is not a growth strategy. The brands that close this gap will reach more customers, reduce legal exposure, and build the kind of trust that no ad budget can buy.</p>


AI is writing your copy, building your landing pages, and increasingly generating the code behind your digital experience. Which raises a question worth sitting with: if AI is building more of what your customers touch, who is making sure it works for everyone?
For many brands, that question doesn’t have a clear answer yet. And the data shows it.
As of the 2026 WebAIM Million report, 95.9% of the top one million website homepages have detectable accessibility failures. The average page has 56.1 errors. And the number of errors increased 10.1% over the prior year, reversing six consecutive years of gradual improvement.
That trend didn’t reverse by accident. According to that same report, the average homepage now contains 1,437 elements, up 22.5% in a single year and nearly double what it did in 2019. More code is being shipped, faster, with the same accessibility gaps baked in. If your team is using AI to build or update digital experiences, that pattern almost certainly includes you.
Accessibility gets treated as an engineering problem, something you hand off and forget. But when AI is generating the experiences your customers touch, the gap becomes everyone’s problem, and marketing has more leverage to close it than most teams realize.
AI-assisted development Iis making the web less accessible
The AI tools your team uses to write copy, build landing pages, and generate code were trained on the web as it exists. The web is largely inaccessible, and that’s exactly what these models learned from. The six most common failure types — low-contrast text, missing alt text, unlabeled form fields, empty links, empty buttons, and missing document language — have been the top failures for seven consecutive years.
When a model learns from billions of pages of existing web content, it learns those patterns. Most AI tools today lack the training foundation needed to generate consistently accessible code.
What makes this challenging for marketing teams is that these failures remain hidden until a user encounters them directly. The code looks fine. The page renders correctly. The gap only surfaces when someone navigates with a screen reader, or tabs through a form with a keyboard. By then, someone has already had a bad experience on your site, or it has already been flagged for legal action.
The New York City Bar Association put it plainly in a June 2025 report: “AI cannot solve for accessibility if it was never trained to recognize it.” Asking an AI tool to generate accessible code is asking it to do something it has no foundation to do well. The failures stay invisible until a real user encounters them, or a plaintiff’s law firm flags your site.
Accessibility lawsuits have doubled since 2020, and ecommerce is the primary target
Marketing leaders understand the reputational impact of accessibility litigation. The data shows this risk is both real and growing.
According to AudioEye’s 2026 Web Accessibility Litigation Report, U.S. digital accessibility lawsuits have doubled since 2020. 78% target ecommerce. Nearly eight in 10 are now filed in state courts, where damages can compound quickly. In 2025 alone, 26,253 combined federal and state claims were filed.
The most instructive data point from that report: 38.5% of companies that faced accessibility lawsuits already had an accessibility tool in place when they were sued. They thought the problem was handled. They had partial coverage and didn’t know it. Litigation often reveals these gaps publicly, with significant reputational consequences.
This isn’t only a U.S. problem. On June 4, 2026, a French court ordered Carrefour to bring carrefour.fr and its app to full accessibility conformance within six months, the first ruling under the European Accessibility Act to reject partial conformance as a legal defense. Carrefour’s own accessibility statements had claimed 50% to 70% conformance across its platforms. The court’s reasoning was direct: a site can’t be somewhat accessible. It works or it doesn’t.
Settlement costs typically run $15,000 to $75,000. Legal fees run $10,000 to $30,000 on top of that. And those are the cases that settle quickly. One widely-cited case against a major retailer began as a routine accessibility claim in 2020 and ended five years later with over 200 court filings and a total cost of $5.15 million. Defending against these claims often costs more than early settlement, and significantly more than solving it proactively.
Inaccessible digital experiences are turning away a $18 trillion market
More than 1.3 billion people globally live with a disability. Together with their family and friends, they represent over $18 trillion in spending power, according to the Return on Disability Group. Seven out of 10 customers with disabilities will leave a website if it’s inaccessible, and 83% limit their shopping to sites they know work for them.
Your marketing spend is already allocated to reach this audience. An inaccessible digital experience turns them away before your message can resonate.
Brand trust is built or lost at the experience level. Accessibility is no different. The brands that are known for getting this right build loyalty that doesn’t appear in any single quarter but compounds across cycles. Those that overlook accessibility experience abandoned sessions, negative word of mouth, and in some cases, public litigation.
3 things marketing leaders can do right now
Accessibility doesn’t have to live entirely in engineering. Marketing leaders have more leverage here than most realize.
1. Scrutinize your tools, the ones you have and the ones you’re about to buy. If your team is using AI to build or update digital experiences, find out what that AI can and cannot detect. Automated tools can only detect about two-thirds of accessibility issues, and of those, fix about half. The issues outside that range are often the ones most likely to trigger a lawsuit.
That scrutiny matters even more as you revise your stack. As you evaluate new AI-powered platforms, whether it’s a CMS, a personalization engine, or a code generator, don’t assume the vendor solved accessibility because they solved speed. Ask what the tool does and doesn’t detect, whether a human ever reviews the output, and who owns the gap when it’s wrong. Partnering with a solution like AudioEye, which combines AI-powered automation with expert-written custom fixes, gives you coverage that no automated tool delivers on its own.
2. Connect accessibility to the metrics you already report. Baseline conversion rate and bounce rate on a high-traffic page, fix it, then measure the same page again. Put the accessibility issue count in the same report as CAC and conversion rate, not a separate compliance dashboard nobody in marketing reads. This is a revenue and reach conversation, not a compliance cost.
3. Make accessibility part of your campaign launch process. No digital experience, whether a landing page, a product launch, or a seasonal campaign, should go live without an accessibility check. Treat it the way you treat brand safety in media buying: a non-negotiable before anything goes out the door.
The AI tools your team relies on are not going away, and they shouldn’t. But speed without accessibility is not a growth strategy. The brands that close this gap will reach more customers, reduce legal exposure, and build the kind of trust that no ad budget can buy.

AI is generating more of the web, and it's carrying the same accessibility gaps with it. Marketing has the leverage to close them

AI is building your digital experience. It’s also making it less accessible. by AudioEye

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory.png" class="attachment-large size-large wp-post-image" alt="AI model memory" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The analysis of AI searches found 63% of brand-specific searches involved one of each model's five most familiar brands.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory.png" class="attachment-large size-large wp-post-image" alt="AI model memory" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ai-model-memory-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">AI models searched for brands they already knew 3.2 times more often than unfamiliar brands, according to a new study. Models searched for familiar brands 55.7% of the time, compared with 17.4% for brands outside their top 10, geoSurge found.</p>



<p class="wp-block-paragraph"><strong>Memory strongly decided which companies </strong>were<strong> considered</strong>. Researchers measured model memory and search behavior separately to see whether what a model knew influenced what it searched for. Across nearly 4,000 responses to 66 U.S. buyer questions, models searched for familiar brands 3.2 times more often than unfamiliar ones.</p>



<ul class="wp-block-list">
<li>Most fan-out searches didn&#8217;t name a brand. Only 31% included a company name. </li>



<li>When models searched for a specific brand, 63% of those searches involved one of their five most familiar brands.</li>
</ul>



<p class="wp-block-paragraph" id="h-results-by-industry"><strong>Results by industry. </strong>Models searched for familiar brands 41% to 82% of the time, compared with 9% to 23% for unfamiliar brands. Researchers noted that some industries were represented by as few as six prompts. </p>



<ul class="wp-block-list">
<li>The industries analyzed were travel, automotive, finance, business software, education, food and restaurants, luxury, fitness and wellness, and fashion.</li>
</ul>



<p class="wp-block-paragraph" id="h-results-by-industry"><strong>Memory didn&#8217;t determine every search.</strong> In one example, Gemini searched for Lemon Squeezy while answering a question about online payment providers, even though the brand wasn&#8217;t in its measured memory. This shows live search can still find unfamiliar brands, especially in categories where models rely less on what they already know, researchers said.</p>



<p class="wp-block-paragraph" id="h-results-by-industry"><strong>Why we care.</strong> AI models are more likely to search for brands they already know. This means a brand may have an advantage before the model even starts searching. Strong content can still help newer brands get noticed, but familiar brands appear to have an unfair advantage.</p>



<p class="wp-block-paragraph" id="h-results-by-industry"><strong>About the data. </strong>Researchers tested 66 U.S. buyer prompts 60 times between May 29 and June 9, analyzing 3,960 responses, 13,281 fan-out searches, and 1,416 brand-level observations. The authors said the results show a relationship between memory and search behavior, but don&#8217;t prove that one caused the other.</p>



<p class="wp-block-paragraph" id="h-results-by-industry"><strong>The study.</strong> <a href="https://geosurge.ai/posts/model-memory-predicts-which-brands-get-searched" target="_blank" rel="noopener">Report: AI Searches What It Remembers</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">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
      </div>
      <p id="semrush-one-subhead">
       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
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    </a>



AI models searched for brands they already knew 3.2 times more often than unfamiliar brands, according to a new study. Models searched for familiar brands 55.7% of the time, compared with 17.4% for brands outside their top 10, geoSurge found.
Memory strongly decided which companies were considered. Researchers measured model memory and search behavior separately to see whether what a model knew influenced what it searched for. Across nearly 4,000 responses to 66 U.S. buyer questions, models searched for familiar brands 3.2 times more often than unfamiliar ones.
Most fan-out searches didn’t name a brand. Only 31% included a company name. 
When models searched for a specific brand, 63% of those searches involved one of their five most familiar brands.
Results by industry. Models searched for familiar brands 41% to 82% of the time, compared with 9% to 23% for unfamiliar brands. Researchers noted that some industries were represented by as few as six prompts. 
The industries analyzed were travel, automotive, finance, business software, education, food and restaurants, luxury, fitness and wellness, and fashion.
Memory didn’t determine every search. In one example, Gemini searched for Lemon Squeezy while answering a question about online payment providers, even though the brand wasn’t in its measured memory. This shows live search can still find unfamiliar brands, especially in categories where models rely less on what they already know, researchers said.
Why we care. AI models are more likely to search for brands they already know. This means a brand may have an advantage before the model even starts searching. Strong content can still help newer brands get noticed, but familiar brands appear to have an unfair advantage.
About the data. Researchers tested 66 U.S. buyer prompts 60 times between May 29 and June 9, analyzing 3,960 responses, 13,281 fan-out searches, and 1,416 brand-level observations. The authors said the results show a relationship between memory and search behavior, but don’t prove that one caused the other.
The study. Report: AI Searches What It Remembers



  
customers won’t either.
      
See your AI visibility

The analysis of AI searches found 63% of brand-specific searches involved one of each model's five most familiar brands.

AI models favor familiar brands in search: Study

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/Google-Display.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Google-Display.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Display-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Display-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Display-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Display &#038; Video 360's latest Structured Data Files update adds AI labels, DOOH support and new bulk management features.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/Google-Display.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Google-Display.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Display-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Display-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Display-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google released Structured Data Files (SDF) v10.1 for Display &amp; Video 360, adding support for AI transparency labels, digital out-of-home (DOOH) campaigns and new reporting fields while deprecating versions earlier than v10.</p>



<p class="wp-block-paragraph">The update gives advertisers and developers more control over campaign management through bulk uploads and downloads.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s new</strong>. SDF v10.1 is now generally available for all Display &amp; Video 360 users and includes several notable additions:</p>



<ul class="wp-block-list">
<li>A new option to specify whether a YouTube video asset was created or edited using AI, supporting Google&#8217;s expanding AI transparency initiatives.</li>



<li>Support for digital out-of-home (DOOH) Insertion Order and Line Item resources.</li>



<li>New columns that identify which Display &amp; Video 360 creatives are associated with ads and ad groups.</li>



<li>Updated values for the Inventory Mode column in Line Item files.</li>
</ul>



<p class="wp-block-paragraph">Google has also deprecated all SDF versions prior to v10 and is encouraging developers to migrate using its published migration guide.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Teams that rely on Structured Data Files to manage Display &amp; Video 360 campaigns at scale will need to update their workflows. The AI labelling field aligns SDF with Google&#8217;s broader advertising transparency efforts, while the new DOOH support and creative mapping fields expand what can be managed and audited through bulk operations.</p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. <a href="https://ads-developers.googleblog.com/2026/07/launching-structured-data-files-v101.html">Structured Data Files v10.1 brings AI labelling</a>, DOOH support and new campaign management capabilities to Display &amp; Video 360, while marking the end of support for SDF versions earlier than v10.</p>


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Google released Structured Data Files (SDF) v10.1 for Display & Video 360, adding support for AI transparency labels, digital out-of-home (DOOH) campaigns and new reporting fields while deprecating versions earlier than v10.
The update gives advertisers and developers more control over campaign management through bulk uploads and downloads.
What’s new. SDF v10.1 is now generally available for all Display & Video 360 users and includes several notable additions:
A new option to specify whether a YouTube video asset was created or edited using AI, supporting Google’s expanding AI transparency initiatives.
Support for digital out-of-home (DOOH) Insertion Order and Line Item resources.
New columns that identify which Display & Video 360 creatives are associated with ads and ad groups.
Updated values for the Inventory Mode column in Line Item files.
Google has also deprecated all SDF versions prior to v10 and is encouraging developers to migrate using its published migration guide.
Why we care. Teams that rely on Structured Data Files to manage Display & Video 360 campaigns at scale will need to update their workflows. The AI labelling field aligns SDF with Google’s broader advertising transparency efforts, while the new DOOH support and creative mapping fields expand what can be managed and audited through bulk operations.
Bottom line. Structured Data Files v10.1 brings AI labelling, DOOH support and new campaign management capabilities to Display & Video 360, while marking the end of support for SDF versions earlier than v10.
before your competitors.
      
Start your free trial

Google Display & Video 360's latest Structured Data Files update adds AI labels, DOOH support and new bulk management features.

Google launches Structured Data Files v10.1 for Display & Video 360

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google extends its AI-generated ad description test to Shopping ads, expanding it experiment they confirmed earlier this month.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/AI-geenrated-image-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google appears to be expanding its test of AI-generated descriptions to Shopping and Product ads, potentially changing how sponsored listings are presented on Search.</p>



<p class="wp-block-paragraph">The experiment follows a similar test on standard Search ads earlier this month and could influence how shoppers interact with paid listings.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening.</strong> PPC expert Brodie Clark has spotted AI-generated descriptions appearing alongside Shopping and Product ads, suggesting Google is extending an existing Search ads experiment to additional ad formats.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="980" height="958" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ai-generated-descriptions-shopping-ads-YKbzTvCz.png" alt="" class="wp-image-484017" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ai-generated-descriptions-shopping-ads-YKbzTvCz.png 980w, https://searchengineland.com/wp-content/seloads/2026/07/google-ai-generated-descriptions-shopping-ads-YKbzTvCz-768x751.png 768w" sizes="(max-width: 980px) 100vw, 980px" /></figure>



<p class="wp-block-paragraph">Earlier in July, Google confirmed it was testing <a href="https://searchengineland.com/google-tests-ai-generated-summaries-in-search-ads-481424">AI-generated summaries on sponsored Search results</a>, </p>



<ul class="wp-block-list">
<li>&#8220;This is a small experiment to see if adding AI-generated context to Search ads helps people make more informed decisions.&#8221;</li>
</ul>



<p class="wp-block-paragraph">The latest sightings indicate the experiment is no longer limited to traditional text ads and now includes Shopping placements.</p>



<p class="wp-block-paragraph">Google has not announced whether the feature will expand beyond the current test.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Shopping advertisers invest heavily in optimising product titles, descriptions and images to influence click-through rates and conversions. AI-generated descriptions introduce another layer that advertisers don&#8217;t directly control, raising questions about whether Google&#8217;s summaries accurately represent products and how they could affect user engagement. </p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. Google is testing AI-generated descriptions in Shopping ads, extending an earlier experiment on Search ads. While the feature aims to help shoppers make more informed decisions, advertisers will be watching closely to see how Google&#8217;s AI-generated messaging affects click-through rates and campaign performance.</p>



<p class="wp-block-paragraph"><strong>First spotted.</strong> This possible test was spotted by <a href="https://brodieclark.com/notes/#jul-2026">Brodie Clarke</a> and posted on his SERP Alert page.</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 appears to be expanding its test of AI-generated descriptions to Shopping and Product ads, potentially changing how sponsored listings are presented on Search.
The experiment follows a similar test on standard Search ads earlier this month and could influence how shoppers interact with paid listings.
What’s happening. PPC expert Brodie Clark has spotted AI-generated descriptions appearing alongside Shopping and Product ads, suggesting Google is extending an existing Search ads experiment to additional ad formats.




Earlier in July, Google confirmed it was testing AI-generated summaries on sponsored Search results, 
“This is a small experiment to see if adding AI-generated context to Search ads helps people make more informed decisions.”
The latest sightings indicate the experiment is no longer limited to traditional text ads and now includes Shopping placements.
Google has not announced whether the feature will expand beyond the current test.
Why we care. Shopping advertisers invest heavily in optimising product titles, descriptions and images to influence click-through rates and conversions. AI-generated descriptions introduce another layer that advertisers don’t directly control, raising questions about whether Google’s summaries accurately represent products and how they could affect user engagement. 
Bottom line. Google is testing AI-generated descriptions in Shopping ads, extending an earlier experiment on Search ads. While the feature aims to help shoppers make more informed decisions, advertisers will be watching closely to see how Google’s AI-generated messaging affects click-through rates and campaign performance.
First spotted. This possible test was spotted by Brodie Clarke and posted on his SERP Alert page.
customers won’t either.
      
See your AI visibility

Google extends its AI-generated ad description test to Shopping ads, expanding it experiment they confirmed earlier this month.

Google tests AI-generated descriptions in Shopping ads

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image.png" class="attachment-large size-large wp-post-image" alt="Your sales team needs more than a pitch deck - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>When sales is rewarded for closing deals and delivery is judged on results, unrealistic SEO and AI promises become almost inevitable.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image.png" class="attachment-large size-large wp-post-image" alt="Your sales team needs more than a pitch deck - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Your-sales-team-needs-more-than-a-pitch-deck-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Roughly 10 years ago, I worked in the digital marketing division of a company generating more than $5 billion in annual revenue. Our team included approximately 50 SEO professionals, developers, and content writers responsible for delivering the services its sales team sold.</p>



<p class="wp-block-paragraph">It was my first experience working alongside a true sales organization and my first exposure to the disconnect between what salespeople were incentivized to promise and what the delivery team could realistically execute.</p>



<p class="wp-block-paragraph">The sales reps <em>(mostly) </em>weren’t dishonest or incompetent. Most were doing exactly what the company rewarded them for doing: <strong>closing deals.</strong>



<p class="wp-block-paragraph">The problem was that once the contract was signed, the salesperson collected the commission while someone else inherited the promise.</p>



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



<h2 id="h-salespeople-aren-t-the-problem-their-incentives-are" class="wp-block-heading">Salespeople aren’t the problem. Their incentives are.</h2>



<p class="wp-block-paragraph">Salespeople are generally rewarded for:</p>



<ul class="wp-block-list">
<li>Signing new customers.</li>



<li>Increasing contract value.</li>



<li>Renewing existing accounts.</li>



<li>Shortening the sales cycle.</li>
</ul>



<p class="wp-block-paragraph">They are rarely compensated based on whether the SEO strategy worked, whether the AI implementation delivered meaningful value, or whether the team executing could deliver against his/her promise.</p>



<p class="wp-block-paragraph">That creates an obvious conflict. The easiest way to close a hesitant prospect is to reduce uncertainty, but SEO and AI should be an asterisk in any dictionary definition of <em>“uncertainty.”</em> If the rep makes the work sound faster, easier, or more predictable than it actually is, they improve their chances of making the sale while transferring the risk to someone else.</p>



<h2 id="h-the-good-strong-salespeople-create-opportunities" class="wp-block-heading">The good: Strong salespeople create opportunities</h2>



<p class="wp-block-paragraph">It’s easy for delivery teams to complain about sales. <em>Trust me, I’ve complained more than my fair share.</em> But selling services is difficult. Prospects rarely arrive with a clearly defined problem, a realistic budget, and an executive team ready to act.</p>



<p class="wp-block-paragraph"><strong>Strong salespeople create opportunities by:</strong>



<ul class="wp-block-list">
<li>Uncovering what the customer actually needs</li>



<li>Translating the executing team’s capabilities into business value</li>



<li>Building trust before specialists even enter the conversation</li>



<li>Handling prospecting, qualification, and follow-ups</li>



<li>Helping the company grow beyond referrals and reputation</li>
</ul>



<p class="wp-block-paragraph">Search professionals often underestimate these skills. Let’s be honest: our industry isn’t exactly overflowing with people skills or a <a href="https://www.seoforlunch.com/p/compromise" target="_blank" rel="noopener">willingness to compromise</a>. Being capable of delivering great work doesn’t mean you can convince someone to buy it.</p>



<p class="wp-block-paragraph">The goal shouldn’t be to restrain sales or turn every salesperson into a technical SEO. It should be to give them enough knowledge, structure, and access to specialists to recognize the right opportunity, and sell an engagement the team can actually fulfill.</p>



<p class="wp-block-paragraph">Unfortunately, creating the opportunity is only half the equation. What the salesperson promises to close it matters just as much.</p>



<h2 id="h-the-bad-the-delivery-team-inherits-the-promise" class="wp-block-heading">The bad: The delivery team inherits the promise</h2>



<p class="wp-block-paragraph">The problems begin when the customer buys an outcome the executing team never agreed was possible.</p>



<p class="wp-block-paragraph">The problems begin when the customer buys an outcome the delivery team never agreed was possible. This might mean:</p>



<ul class="wp-block-list">
<li>Guaranteeing rankings.</li>



<li>Committing to timelines before reviewing the website.</li>



<li>Downplaying the client’s role in implementation.</li>



<li>Selling the wrong combination of services.</li>



<li>Promising results the product was never capable of delivering.</li>
</ul>



<p class="wp-block-paragraph">Sometimes the salesperson exaggerated. Other times, they simply didn’t understand what they were selling. That distinction didn’t matter much to the client — or to me when my phone rang unexpectedly in the middle of the day.</p>



<p class="wp-block-paragraph">I regularly received calls and emails from frustrated clients telling me their boss was on their ass because they weren’t getting what the salesperson had promised. By then, the client had repeated those promises internally and put their own credibility on the line.</p>



<p class="wp-block-paragraph">My job was to determine whether the salesperson had lied, misunderstood the product, or sold the wrong solution — and then explain the gap without throwing a coworker under the bus or making the client feel like an idiot for believing them.</p>



<p class="wp-block-paragraph">The salesperson received credit for closing the deal. The delivery team inherited an angry customer, an impossible expectation, and a relationship damaged before the real work had even begun.</p>



<p class="wp-block-paragraph">And if setting expectations for traditional SEO was already difficult, AI has made it considerably worse.</p>



<h2 id="h-ai-makes-the-expectation-problem-worse" class="wp-block-heading">AI makes the expectation problem worse</h2>



<p class="wp-block-paragraph">AI has taken an existing problem in SEO sales and <a href="https://www.seoforlunch.com/p/collaboration" target="_blank" rel="noopener">multiplied it by 100</a>.</p>



<p class="wp-block-paragraph">Organic visibility has never been something anyone could guarantee. Results depend on the client’s business, website, competition, implementation, brand, and link authority, search demand, and the constant changes made by Google. Now companies are selling AI visibility alongside SEO despite having even less control over when (or why) a brand appears in ChatGPT, Google AI Overviews, or other AI experiences.</p>



<p class="wp-block-paragraph">That uncertainty should result in more careful claims. Instead, AI FOMO has created an opportunity to make bigger sales and bigger promises with less proof.</p>



<p class="wp-block-paragraph"><strong>A salesperson can now point to:</strong>



<ul class="wp-block-list">
<li>A handful of prompts where a client appears.</li>



<li>An AI visibility score from a third-party platform.</li>



<li>A polished dashboard tracking mentions and citations.</li>



<li>A competitor appearing in answers where the prospect does not.</li>
</ul>



<p class="wp-block-paragraph">Unfortunately, “We’ll help you understand and improve your AI visibility” is harder to sell than “We’ll get your brand into AI answers.”</p>



<p class="wp-block-paragraph">AI hasn’t made organic visibility more predictable <em>(or easier)</em> to track. It has simply made FOMO easier to monetize. When sales is incentivized by ink on a contract while delivery is judged by results, the two teams aren’t working toward the same goal.</p>



<h2 id="h-what-sales-reps-actually-need" class="wp-block-heading">What sales reps actually need</h2>



<p class="wp-block-paragraph">Sales reps don’t need to become technical SEOs or pretend to be “the expert” during sales meetings. But they do need more than a pitch deck, a product sheet, and a few impressive case studies.</p>



<p class="wp-block-paragraph">They need:</p>



<ul class="wp-block-list">
<li><strong>Clear boundaries:</strong> What can never be guaranteed?</li>



<li><strong>Qualification criteria:</strong> Which prospects can realistically succeed?</li>



<li><strong>Approved claims:</strong> What can be said about outcomes and timelines?</li>



<li><strong>Customer responsibilities:</strong> What must the client provide or implement?</li>



<li><strong>Realistic case studies:</strong> What conditions and products made those results possible?</li>



<li><strong>A feedback loop:</strong> Which promises repeatedly cause problems after handoff?</li>



<li><strong>Shared accountability:</strong> How will sales be measured after the contract is signed?</li>



<li><strong>Access to delivery:</strong> When should an expert join the sales process?</li>
</ul>



<p class="wp-block-paragraph">That final point might be the most important.</p>



<p class="wp-block-paragraph">Some of the best sales reps I had worked with would call or email me before pitching a solution. They would explain what the law firm wanted to accomplish and ask which of our products <em>(or combination of products)</em> gave the client the best chance of getting there.</p>



<p class="wp-block-paragraph">Because I understood the available levers and the capabilities of our products, I could connect the two. Sometimes that resulted in a larger sale because the client genuinely needed more support. Other times, it prevented the rep from selling a cheaper solution that didn’t have a chance in hell of producing the results being discussed.</p>



<p class="wp-block-paragraph">Involving delivery doesn’t prevent upselling. Most of the time, <a href="https://www.seoforlunch.com/p/what-if-you-were-held-accountable" target="_blank" rel="noopener">it makes the upsell defensible</a>.</p>



<p class="wp-block-paragraph">The execution team shouldn’t first learn about the client’s goals during the kickoff call. They should be involved early enough to validate the opportunity, shape the solution, and flag expectations that cannot be met.</p>



<p class="wp-block-paragraph">Businesses should consider tying a portion of sales compensation to account quality or early retention. If an engagement collapses within 90 days because it was never viable, that wasn’t a successful sale. It was delayed churn.</p>



<p class="wp-block-paragraph">The tension between sales and delivery will never disappear completely. But if your salespeople win when the contract is signed, and everyone else loses immediately afterward, you don’t have a sales problem. You have an incentive problem.</p>



<p class="wp-block-paragraph">If the signature is the only thing you reward, don’t act surprised when the promise is impossible to deliver.</p>



<p class="wp-block-paragraph"><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>

Roughly 10 years ago, I worked in the digital marketing division of a company generating more than $5 billion in annual revenue. Our team included approximately 50 SEO professionals, developers, and content writers responsible for delivering the services its sales team sold.
It was my first experience working alongside a true sales organization and my first exposure to the disconnect between what salespeople were incentivized to promise and what the delivery team could realistically execute.
The sales reps (mostly) weren’t dishonest or incompetent. Most were doing exactly what the company rewarded them for doing: closing deals.



The problem was that once the contract was signed, the salesperson collected the commission while someone else inherited the promise.
View embedded content



Salespeople aren’t the problem. Their incentives are.
Salespeople are generally rewarded for:
Signing new customers.
Increasing contract value.
Renewing existing accounts.
Shortening the sales cycle.
They are rarely compensated based on whether the SEO strategy worked, whether the AI implementation delivered meaningful value, or whether the team executing could deliver against his/her promise.
That creates an obvious conflict. The easiest way to close a hesitant prospect is to reduce uncertainty, but SEO and AI should be an asterisk in any dictionary definition of “uncertainty.” If the rep makes the work sound faster, easier, or more predictable than it actually is, they improve their chances of making the sale while transferring the risk to someone else.
The good: Strong salespeople create opportunities
It’s easy for delivery teams to complain about sales. Trust me, I’ve complained more than my fair share. But selling services is difficult. Prospects rarely arrive with a clearly defined problem, a realistic budget, and an executive team ready to act.
Strong salespeople create opportunities by:




Uncovering what the customer actually needs
Translating the executing team’s capabilities into business value
Building trust before specialists even enter the conversation
Handling prospecting, qualification, and follow-ups
Helping the company grow beyond referrals and reputation
Search professionals often underestimate these skills. Let’s be honest: our industry isn’t exactly overflowing with people skills or a willingness to compromise. Being capable of delivering great work doesn’t mean you can convince someone to buy it.
The goal shouldn’t be to restrain sales or turn every salesperson into a technical SEO. It should be to give them enough knowledge, structure, and access to specialists to recognize the right opportunity, and sell an engagement the team can actually fulfill.
Unfortunately, creating the opportunity is only half the equation. What the salesperson promises to close it matters just as much.
The bad: The delivery team inherits the promise
The problems begin when the customer buys an outcome the executing team never agreed was possible.
The problems begin when the customer buys an outcome the delivery team never agreed was possible. This might mean:
Guaranteeing rankings.
Committing to timelines before reviewing the website.
Downplaying the client’s role in implementation.
Selling the wrong combination of services.
Promising results the product was never capable of delivering.
Sometimes the salesperson exaggerated. Other times, they simply didn’t understand what they were selling. That distinction didn’t matter much to the client — or to me when my phone rang unexpectedly in the middle of the day.
I regularly received calls and emails from frustrated clients telling me their boss was on their ass because they weren’t getting what the salesperson had promised. By then, the client had repeated those promises internally and put their own credibility on the line.
My job was to determine whether the salesperson had lied, misunderstood the product, or sold the wrong solution — and then explain the gap without throwing a coworker under the bus or making the client feel like an idiot for believing them.
The salesperson received credit for closing the deal. The delivery team inherited an angry customer, an impossible expectation, and a relationship damaged before the real work had even begun.
And if setting expectations for traditional SEO was already difficult, AI has made it considerably worse.
AI makes the expectation problem worse
AI has taken an existing problem in SEO sales and multiplied it by 100.
Organic visibility has never been something anyone could guarantee. Results depend on the client’s business, website, competition, implementation, brand, and link authority, search demand, and the constant changes made by Google. Now companies are selling AI visibility alongside SEO despite having even less control over when (or why) a brand appears in ChatGPT, Google AI Overviews, or other AI experiences.
That uncertainty should result in more careful claims. Instead, AI FOMO has created an opportunity to make bigger sales and bigger promises with less proof.
A salesperson can now point to:




A handful of prompts where a client appears.
An AI visibility score from a third-party platform.
A polished dashboard tracking mentions and citations.
A competitor appearing in answers where the prospect does not.
Unfortunately, “We’ll help you understand and improve your AI visibility” is harder to sell than “We’ll get your brand into AI answers.”
AI hasn’t made organic visibility more predictable (or easier) to track. It has simply made FOMO easier to monetize. When sales is incentivized by ink on a contract while delivery is judged by results, the two teams aren’t working toward the same goal.
What sales reps actually need
Sales reps don’t need to become technical SEOs or pretend to be “the expert” during sales meetings. But they do need more than a pitch deck, a product sheet, and a few impressive case studies.
They need:
Clear boundaries: What can never be guaranteed?
Qualification criteria: Which prospects can realistically succeed?
Approved claims: What can be said about outcomes and timelines?
Customer responsibilities: What must the client provide or implement?
Realistic case studies: What conditions and products made those results possible?
A feedback loop: Which promises repeatedly cause problems after handoff?
Shared accountability: How will sales be measured after the contract is signed?
Access to delivery: When should an expert join the sales process?
That final point might be the most important.
Some of the best sales reps I had worked with would call or email me before pitching a solution. They would explain what the law firm wanted to accomplish and ask which of our products (or combination of products) gave the client the best chance of getting there.
Because I understood the available levers and the capabilities of our products, I could connect the two. Sometimes that resulted in a larger sale because the client genuinely needed more support. Other times, it prevented the rep from selling a cheaper solution that didn’t have a chance in hell of producing the results being discussed.
Involving delivery doesn’t prevent upselling. Most of the time, it makes the upsell defensible.
The execution team shouldn’t first learn about the client’s goals during the kickoff call. They should be involved early enough to validate the opportunity, shape the solution, and flag expectations that cannot be met.
Businesses should consider tying a portion of sales compensation to account quality or early retention. If an engagement collapses within 90 days because it was never viable, that wasn’t a successful sale. It was delayed churn.
The tension between sales and delivery will never disappear completely. But if your salespeople win when the contract is signed, and everyone else loses immediately afterward, you don’t have a sales problem. You have an incentive problem.
If the signature is the only thing you reward, don’t act surprised when the promise is impossible to deliver.
This post first appeared on the author’s website and is republished here with permission.

When sales is rewarded for closing deals and delivery is judged on results, unrealistic SEO and AI promises become almost inevitable.

Selling SEO or AI services? Your sales team needs more than a pitch deck

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python.png" class="attachment-large size-large wp-post-image" alt="How to build a keyword clustering tool with Python" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Automate the process of grouping thousands of search queries into coherent topics for faster, more scalable content planning.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python.png" class="attachment-large size-large wp-post-image" alt="How to build a keyword clustering tool with Python" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-a-keyword-clustering-tool-with-Python-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Keyword grouping is one of those <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> tasks that sounds simple until you’re staring at a spreadsheet with 12,000 rows and a &#8220;group by intent&#8221; column you’re filling in by hand.</p>



<p class="wp-block-paragraph">Manual clustering doesn’t scale, and rule-based grouping misses the semantic overlap between phrases that don’t share a single word but clearly belong together.</p>



<p class="wp-block-paragraph">I’ve refactored a keyword clustering script that I developed a few years ago. The framework uses TF-IDF vectorization to generate feature vectors, which are then clustered using HDBSCAN, a density-based clustering algorithm. You can find the script <a href="https://github.com/simodepth96/Keyword-Clustering-HDBSCAN" target="_blank" rel="noopener">here</a>.</p>



<h2 id="h-the-problem-with-keyword-clustering" class="wp-block-heading">The problem with keyword clustering</h2>



<p class="wp-block-paragraph">Keyword clustering is the catalyst for topic generation. Rather than briefing content writers with hundreds of individual keywords, clustering allows you to group semantically related queries into coherent topics, making it much easier to produce content that satisfies a wider range of search intent.</p>



<p class="wp-block-paragraph">The result is a <a href="https://searchengineland.com/guide/topic-clusters">set of topics</a> that can strengthen semantic relationships, topical authority, internal linking, and visibility across related queries.</p>



<p class="wp-block-paragraph">There are two key challenges here: preprocessing the data and clustering the topics.</p>



<h3 id="h-preprocessing-the-data" class="wp-block-heading">Preprocessing the data</h3>



<p class="wp-block-paragraph">SEO keyword exports are often noisy, especially if they come from your own databases. Removing stopwords and non-ASCII characters can’t be done manually, hence the need to automate data cleaning at scale.</p>



<p class="wp-block-paragraph">Python is a straightforward solution because it provides the flexibility to build a pipeline that can clean, normalize, and transform large keyword datasets with minimal manual intervention.</p>



<h3 id="h-clustering-the-topics-nbsp" class="wp-block-heading">Clustering the topics&nbsp;</h3>



<p class="wp-block-paragraph">When analyzing keyword data, you rarely know how many topical groups exist until you’ve explored the dataset. This makes algorithms such as k-means a poor fit, as they require you to specify the number of clusters in advance.</p>



<p class="wp-block-paragraph">A combination of TF-IDF and HDBSCAN proves particularly effective. TF-IDF transforms each keyword into a numerical vector by assigning greater weight to terms that are distinctive within the dataset while down-weighting those that appear frequently across many keywords.</p>



<p class="wp-block-paragraph">These vectors are then fed into HDBSCAN, a density-based clustering algorithm that identifies natural groupings without requiring the number of clusters to be defined in advance.</p>



<p class="wp-block-paragraph">One of HDBSCAN’s key advantages is its ability to identify noise. Rather than forcing every keyword into a cluster, it assigns outliers to a -1 label, which means it excludes keywords that don’t belong to any topic.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1129" height="605" src="https://searchengineland.com/wp-content/seloads/2026/07/From-keywords-to-clusters.png" alt="From keywords to clusters" class="wp-image-483984" srcset="https://searchengineland.com/wp-content/seloads/2026/07/From-keywords-to-clusters.png 1129w, https://searchengineland.com/wp-content/seloads/2026/07/From-keywords-to-clusters-768x412.png 768w" sizes="(max-width: 1129px) 100vw, 1129px" /></figure>
</div>


<p class="wp-block-paragraph">This is especially valuable for SEO datasets, where keyword exports often contain highly specific long-tail queries that don’t naturally fit into broader thematic groups.&nbsp;</p>



<p class="wp-block-paragraph">Instead of degrading cluster quality by assigning these terms arbitrarily, HDBSCAN isolates them, producing cleaner and more coherent topical clusters.</p>


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<h2 id="h-sourcing-the-keyword-list-from-bigquery-nbsp" class="wp-block-heading">Sourcing the keyword list from BigQuery&nbsp;</h2>



<p class="wp-block-paragraph">Before any clustering happens, you need a keyword list — and if your Search Console property is already exporting to BigQuery, that’s a better source than the UI export, since it isn’t capped at 1,000 rows and isn’t sampled.</p>



<p class="wp-block-paragraph">A simple pull against the standard GSC BigQuery export schema looks like this:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="424" height="200" src="https://searchengineland.com/wp-content/seloads/2026/07/Sourcing-the-keyword-list-from-BigQuery-.png" alt="Sourcing the keyword list from BigQuery " class="wp-image-483986"/></figure>
</div>


<p class="wp-block-paragraph">Export the result as a CSV, strip it down to a single query column, save it as a .txt file with one keyword per line, and that’s your clustering input.</p>



<p class="wp-block-paragraph">If you don’t have BigQuery export set up, you can still work with Search Console data, but you’ll have a more limited dataset to work with.</p>



<p class="wp-block-paragraph">In any case, you can export queries from the Search Console interface, as the notebook only cares that you feed it a list of keywords.</p>



<h2 id="h-prompting-the-ai" class="wp-block-heading">Prompting the AI</h2>



<p class="wp-block-paragraph">A few years ago, I would get along with a less polished version of this script.</p>



<p class="wp-block-paragraph">AI made the following things easier when rebooting and fine-tuning the script for enhanced output.</p>



<h3 id="h-asking-for-tunable-parameters-not-hardcoded-ones" class="wp-block-heading">Asking for tunable parameters, not hardcoded ones</h3>



<p class="wp-block-paragraph">Cluster sensitivity and minimum cluster size behave very differently depending on whether you’re clustering 50 keywords or 50,000. Requesting these as adjustable variables up top meant I could retune without touching the logic.</p>



<h3 id="h-specifying-the-environment" class="wp-block-heading">Specifying the environment</h3>



<p class="wp-block-paragraph">The first version was a plain Python script meant to run from a terminal. Since the actual workflow is &#8220;pull keywords, run notebook, hand a client a file,&#8221; I asked for a Google Colab-specific version, which changes the shape of the code (try/except blocks around Colab-only imports, no argparse) more than a small tweak would suggest. It also enabled better visualization by leveraging Plotly to the fullest.</p>



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<h2 id="h-running-the-code" class="wp-block-heading">Running the code</h2>



<p class="wp-block-paragraph">The gist of the script was and remains simple. It takes in a flat list of keywords and automatically groups them into topic clusters.</p>



<p class="wp-block-paragraph">All you have to do is upload a .txt file with one keyword per line, and it cleans the text (stripping special characters, stopwords, and non-English entries).</p>



<p class="wp-block-paragraph">Using tunable parameters is the key to the entire framework. Play around with the sensitivity and <code>min_cluster_size</code> parameters. Adjust them based on the size of your keyword list.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="856" height="371" src="https://searchengineland.com/wp-content/seloads/2026/07/Running-the-code-parameters.png" alt="Running the code - parameters" class="wp-image-483987" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Running-the-code-parameters.png 856w, https://searchengineland.com/wp-content/seloads/2026/07/Running-the-code-parameters-768x333.png 768w" sizes="(max-width: 856px) 100vw, 856px" /></figure>
</div>


<p class="wp-block-paragraph">Once you’re happy with the number of clusters, TF-IDF is used to score how important each word is within the set. HDBSCAN then finishes the process.</p>



<p class="wp-block-paragraph">Each cluster gets an auto-generated label based on its most distinctive terms, and the results are exported to an Excel file with both a grouped cluster view and a full keyword-by-keyword breakdown.&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="823" height="215" src="https://searchengineland.com/wp-content/seloads/2026/07/Running-the-code-parameters-and-cluster-scores.png" alt="Running the code - parameters and cluster scores" class="wp-image-483988" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Running-the-code-parameters-and-cluster-scores.png 823w, https://searchengineland.com/wp-content/seloads/2026/07/Running-the-code-parameters-and-cluster-scores-768x201.png 768w" sizes="(max-width: 823px) 100vw, 823px" /></figure>
</div>


<h2 id="h-where-ai-adds-value-nbsp" class="wp-block-heading">Where AI adds value&nbsp;</h2>



<p class="wp-block-paragraph">The decisions that actually matter, from choosing TF-IDF over word embeddings to selecting HDBSCAN because the number of topics is unknown, still require an understanding of how keyword clustering works.</p>



<p class="wp-block-paragraph">What AI removed was the repetitive work of rebuilding boilerplate code, wiring libraries together, and refining the notebook into something reusable.</p>



<p class="wp-block-paragraph">The final result is a lightweight clustering tool that can process thousands of keywords in minutes, producing a sensible first draft of your topical taxonomy. It won’t replace editorial judgment, but it will eliminate hours of manual grouping and give your content team a far more structured starting point.</p>



<p class="wp-block-paragraph">If you already export Search Console data into BigQuery, this becomes a natural step in your workflow: extract your queries, run the notebook, review the clusters, and start planning content based on topics rather than isolated keywords.</p>

Keyword grouping is one of those SEO tasks that sounds simple until you’re staring at a spreadsheet with 12,000 rows and a “group by intent” column you’re filling in by hand.
Manual clustering doesn’t scale, and rule-based grouping misses the semantic overlap between phrases that don’t share a single word but clearly belong together.
I’ve refactored a keyword clustering script that I developed a few years ago. The framework uses TF-IDF vectorization to generate feature vectors, which are then clustered using HDBSCAN, a density-based clustering algorithm. You can find the script here.
The problem with keyword clustering
Keyword clustering is the catalyst for topic generation. Rather than briefing content writers with hundreds of individual keywords, clustering allows you to group semantically related queries into coherent topics, making it much easier to produce content that satisfies a wider range of search intent.
The result is a set of topics that can strengthen semantic relationships, topical authority, internal linking, and visibility across related queries.
There are two key challenges here: preprocessing the data and clustering the topics.
Preprocessing the data
SEO keyword exports are often noisy, especially if they come from your own databases. Removing stopwords and non-ASCII characters can’t be done manually, hence the need to automate data cleaning at scale.
Python is a straightforward solution because it provides the flexibility to build a pipeline that can clean, normalize, and transform large keyword datasets with minimal manual intervention.
Clustering the topics 
When analyzing keyword data, you rarely know how many topical groups exist until you’ve explored the dataset. This makes algorithms such as k-means a poor fit, as they require you to specify the number of clusters in advance.
A combination of TF-IDF and HDBSCAN proves particularly effective. TF-IDF transforms each keyword into a numerical vector by assigning greater weight to terms that are distinctive within the dataset while down-weighting those that appear frequently across many keywords.
These vectors are then fed into HDBSCAN, a density-based clustering algorithm that identifies natural groupings without requiring the number of clusters to be defined in advance.
One of HDBSCAN’s key advantages is its ability to identify noise. Rather than forcing every keyword into a cluster, it assigns outliers to a -1 label, which means it excludes keywords that don’t belong to any topic.




This is especially valuable for SEO datasets, where keyword exports often contain highly specific long-tail queries that don’t naturally fit into broader thematic groups. 
Instead of degrading cluster quality by assigning these terms arbitrarily, HDBSCAN isolates them, producing cleaner and more coherent topical clusters.
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Sourcing the keyword list from BigQuery 
Before any clustering happens, you need a keyword list — and if your Search Console property is already exporting to BigQuery, that’s a better source than the UI export, since it isn’t capped at 1,000 rows and isn’t sampled.
A simple pull against the standard GSC BigQuery export schema looks like this:




Export the result as a CSV, strip it down to a single query column, save it as a .txt file with one keyword per line, and that’s your clustering input.
If you don’t have BigQuery export set up, you can still work with Search Console data, but you’ll have a more limited dataset to work with.
In any case, you can export queries from the Search Console interface, as the notebook only cares that you feed it a list of keywords.
Prompting the AI
A few years ago, I would get along with a less polished version of this script.
AI made the following things easier when rebooting and fine-tuning the script for enhanced output.
Asking for tunable parameters, not hardcoded ones
Cluster sensitivity and minimum cluster size behave very differently depending on whether you’re clustering 50 keywords or 50,000. Requesting these as adjustable variables up top meant I could retune without touching the logic.
Specifying the environment
The first version was a plain Python script meant to run from a terminal. Since the actual workflow is “pull keywords, run notebook, hand a client a file,” I asked for a Google Colab-specific version, which changes the shape of the code (try/except blocks around Colab-only imports, no argparse) more than a small tweak would suggest. It also enabled better visualization by leveraging Plotly to the fullest.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Running the code
The gist of the script was and remains simple. It takes in a flat list of keywords and automatically groups them into topic clusters.
All you have to do is upload a .txt file with one keyword per line, and it cleans the text (stripping special characters, stopwords, and non-English entries).
Using tunable parameters is the key to the entire framework. Play around with the sensitivity and min_cluster_size parameters. Adjust them based on the size of your keyword list.




Once you’re happy with the number of clusters, TF-IDF is used to score how important each word is within the set. HDBSCAN then finishes the process.
Each cluster gets an auto-generated label based on its most distinctive terms, and the results are exported to an Excel file with both a grouped cluster view and a full keyword-by-keyword breakdown. 




Where AI adds value 
The decisions that actually matter, from choosing TF-IDF over word embeddings to selecting HDBSCAN because the number of topics is unknown, still require an understanding of how keyword clustering works.
What AI removed was the repetitive work of rebuilding boilerplate code, wiring libraries together, and refining the notebook into something reusable.
The final result is a lightweight clustering tool that can process thousands of keywords in minutes, producing a sensible first draft of your topical taxonomy. It won’t replace editorial judgment, but it will eliminate hours of manual grouping and give your content team a far more structured starting point.
If you already export Search Console data into BigQuery, this becomes a natural step in your workflow: extract your queries, run the notebook, review the clusters, and start planning content based on topics rather than isolated keywords.

Automate the process of grouping thousands of search queries into coherent topics for faster, more scalable content planning.

How to build a keyword clustering tool with Python

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Multi-location-SEO-How-to-structure-geographic-pages-at-scale.png" class="attachment-large size-large wp-post-image" alt="Multi-location SEO- How to structure geographic pages at scale" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Multi-location-SEO-How-to-structure-geographic-pages-at-scale.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Multi-location-SEO-How-to-structure-geographic-pages-at-scale-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Multi-location-SEO-How-to-structure-geographic-pages-at-scale-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Multi-location-SEO-How-to-structure-geographic-pages-at-scale-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Build your local presence around real locations, markets, and service areas instead of turning every geographic keyword into a new URL.

When regional brands expand, their websites tend to expand right along with them. Over time, that can leave them with hundreds of location-related pages, many targeting the same intent or serving no clear purpose.
More geographic pages don’t necessarily create stronger local visibility. They can lead to internal competition, fragmented authority, conflicting local information, and a growing maintenance burden.
The better approach to multi-location SEO is to create the smallest number of pages necessary to accurately represent the business, then make those pages as useful, connected, and authoritative as possible. Scale the clarity and authority of the location ecosystem, not simply the number of geographic URLs.
How geographic page bloat happens
Most bloated geographic footprints don’t stem from a single terrible strategic decision. They build over time, one seemingly reasonable page at a time.
One agency creates a page for every city with measurable search volume. A local team launches neighborhood pages around an existing office. A franchisee builds its own service area section. Years later, another vendor introduces a new URL structure without addressing the pages already in place.
Each decision may have made sense to someone at the time. Collectively, they create an architecture nobody fully owns.
The problem usually starts with a few assumptions:
Every geographic keyword needs its own page.
A physical location and a service area can be treated the same way.
Swapping the city name creates enough differentiation.
More indexable pages automatically create more visibility.
The issue isn’t that pages share content. Reusable service descriptions, brand messaging, and booking instructions are often necessary. You don’t need to rewrite accurate information just to reach an arbitrary uniqueness percentage.
The real problem is that many of these pages can’t explain why they exist.
They target similar searches, satisfy the same intent, and push users toward the same location or conversion path without adding meaningful information about the market.
At the extreme end, this can resemble what Google defines as doorway abuse: pages created to rank for similar queries that funnel visitors to another part of the website.
That doesn’t mean every city page is a doorway page, but “the keyword has volume” isn’t enough of a reason to publish one.
Even when a site is nowhere near a spam policy issue, excessive geographic pages still create internal competition, fragmented authority, conflicting local information, and a maintenance burden that worsens as the brand grows.
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Start with the real-world business structure
Before deciding which URLs should exist, map how the business actually operates.
This is where local SEO teams need to lead the conversation. Site architecture should reflect the business first. Build around the locations, services, entities, and markets the brand can genuinely support, then use keyword research to refine the structure.
Map the corporate brand, operating regions, physical facilities, local teams, services available at each location, and the surrounding communities those locations genuinely serve.
That forces teams to separate four concepts that are often treated as interchangeable: physical locations, regional markets, service areas, and cities the brand simply wants to rank in.
A physical location is a real, customer-facing facility with its own address, hours, staff, and local experience. It generally deserves an authoritative location page.
A regional market may represent a state, metro area, or operating territory containing multiple facilities. A regional page can be useful when customers need help understanding the brand’s presence or choosing between nearby locations.
A service area is a market served by a physical location or field-based team. It doesn’t automatically represent a separate entity or deserve its own URL.
Google’s service area and hybrid business guidance distinguishes businesses that serve customers at their location from those that travel or deliver to them. Even so, Google Business Profile settings shouldn’t dictate the website architecture.
Adding a city to a Business Profile service area doesn’t mean you need a landing page for it. Publishing a page also doesn’t establish a physical presence there.
Finally, a city the brand wants to rank in is a marketing goal, not a page type.
There’s nothing wrong with wanting to attract customers from nearby markets. The mistake is assuming every target city requires a dedicated URL.
Give every page a job
Most multi-location brands benefit from some form of hub-and-spoke architecture, but there’s no universal folder structure.
A larger organization may need:
/locations/
/locations/pennsylvania/
/locations/pennsylvania/philadelphia/
A smaller regional brand may be better served by:
/locations/
/locations/philadelphia-pa/
Neither is inherently more optimized. The right structure mirrors the business without adding layers for their own sake.
The main locations hub should help users understand the footprint and find the right facility. A location finder can support that experience, but the directory should still include crawlable links to important regional and location pages.
Regional hubs should exist only when they provide meaningful context or help users choose among several facilities.
Individual location pages should serve as the authoritative digital representation of real locations. They should answer practical questions about the address, hours, services, staff, directions, accessibility, and what customers should expect.
These pages play a larger role than simply attracting organic traffic or functioning as a landing page. They help customers understand the location, take action, and confirm that the business has a credible local presence.
Service pages explain what the business offers. Location pages explain where and how that service is delivered. They should support each other rather than repeat one another.




Service area pages should be approved exceptions, not the default response to every city keyword. They can make sense when a market has a dedicated team, distinct logistics, local regulations, or substantial project experience that can’t be handled effectively on an existing page.
A service area page needs to do more than confirm the company is willing to travel there.
Ask whether the page deserves to exist
Before approving another city page, ask a harder question: Does the website actually need it? Search volume can point to an opportunity, but it doesn’t automatically justify creating a new page. 
A geographic page should have a clear customer purpose, a defined place in the architecture, legitimate business relevance, and enough useful information to maintain over time.
A few questions usually expose whether the page has enough substance.
Is there a physical location in the market? A legitimate facility generally supports a dedicated page. Without a physical presence, the case needs to be stronger.
Is the service delivered differently there? Different staff, products, regulations, contractual requirements, pricing, or processes can justify separate treatment.
Can the business prove its relationship to the market? That proof may be a dedicated team, defined service territory, completed projects, local partnerships, or a nearby facility responsible for the area.

Would the page help someone if search engines didn’t exist?
Would a customer intentionally navigate to it?
Would a support representative send it to someone?
Would it answer a question another page can’t answer as well?
Can an existing page satisfy the intent? Sometimes the better move is to strengthen an existing regional or location page with service area details, directions from nearby communities, local FAQs, or clearer location guidance.
If the page only makes sense inside a keyword tool, it probably isn’t ready to publish.





	
	
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Reusable content isn’t the enemy
Templates are necessary when a brand manages dozens or hundreds of locations.
Repeating accurate information and using shared design elements is fine for multi-location sites. The real vulnerability is letting the template become the entire page without adding genuine local differentiation.
Fixed content may include brand positioning, service explanations, booking processes, and compliance language.
Variable content should reflect the location itself:
Address, hours, and local contact information.
Services genuinely available there.
Staff and team member profiles.
Directions, parking, and accessibility.
Original photography and facility details.
Local testimonials.
Region-specific pricing, regulatory, or service information.
FAQs based on real customer questions.




There’s no useful universal percentage of content that must be unique. If the booking process is the same across every facility, repeating it is fine. If the team, parking, hours, and service availability differ, those elements need to be handled locally.
The goal is useful differentiation. Generic city histories, nearby attractions, and paragraphs describing every community as “vibrant” don’t establish local relevance. 
Local content should reduce uncertainty, demonstrate real-world knowledge, or help the customer make a decision.
Use internal links to explain the ecosystem
Internal links do more than pass authority from one page to another. In a multi-location site, they also help define relationships.
They show that a facility belongs to a region. They connect services with the locations where those services are available. They connect team members with the locations where they work.
A strong linking model may include the locations directory linking to regional or individual pages, regional hubs linking to facilities, location pages linking to available services, service pages linking back to relevant facilities, and team member pages connecting to the locations where those individuals work.
These relationships should be intentional. They shouldn’t exist simply to force more city-and-service anchor text into the site.
Google’s guidance on making links crawlable recommends standard HTML links with descriptive anchor text.
This is especially relevant for location finders. A JavaScript-powered finder can be helpful, but it should complement a browsable directory rather than replace it.
Important location pages shouldn’t depend exclusively on XML sitemaps, footer links, internal search forms, or JavaScript interactions to be discovered.
Keep URLs and entity signals aligned
URL governance isn’t the most exciting part of local SEO, but ignoring it is how sites end up with several pages representing the same location.
For example:
/locations/philadelphia/
/service-centers/philadelphia-pa/
/auto-service-philadelphia/
All three URLs may represent the same facility while dividing internal links, backlinks, engagement, and reporting between competing destinations.




Choose one authoritative URL for each location and use it consistently across navigation, service pages, team member pages, internal directories, Google Business Profiles, structured data, and controlled listings.
Create rules for how locations are named, when regional folders are used, how duplicate city names are handled, and what happens when locations move, close, or merge.
The location page, Business Profile, and authoritative third-party sources should agree on the core facts: business name, address, phone number, hours, available services, and operational status.
Google’s guidelines emphasize that a company’s online presence should reflect how it operates in the real world. Structured data can reinforce that consistency, but it can’t turn an unsupported city page into a legitimate location. When markup contradicts Google Business Profile signals or on-page content, it can create conflicting entity signals.
The same principle applies to AI-driven discovery. Give each page a clear purpose, keep the facts accurate, and make relationships between business entities easy to understand. More geographic URLs won’t make an unclear architecture easier for search engines or LLMs to interpret.
Dig deeper: How structured data supports local visibility across Google and AI



Consolidate before you expand again
If the website already has hundreds of weak geographic pages, don’t solve the problem by publishing a better template on top of the old one.
Start by figuring out what’s already there.
Inventory every location, regional, city, neighborhood, and service area URL.
Assign a defined page type and purpose to each.
Review traffic, rankings, conversions, backlinks, internal links, and indexing.
Group pages targeting the same market, service, and intent.
Confirm which pages represent real facilities or legitimate service relationships.
Select the strongest destination for each intent cluster.
Merge useful local content, media, and links into the preferred page.
Redirect retired URLs to the most relevant surviving destination.
Update internal links, navigation, canonicals, structured data, and XML sitemaps.
Monitor rankings, conversions, indexing, and local visibility after consolidation.




Google’s guidance for site moves with URL changes recommends permanent server-side redirects and warns against sending large numbers of retired URLs to an irrelevant destination, such as the home page.
Canonical tags aren’t a substitute for making a decision. They may identify a preferred version, but they don’t remove unnecessary pages or eliminate the maintenance burden.
This isn’t an exercise in deleting pages simply to make the website smaller. Some pages that look repetitive in a crawl may represent genuinely different locations, teams, or customer needs.
The point is to remove the pages that can’t explain why they exist and strengthen the ones that can.
more local customers.
      
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Governance is what keeps the site from bloating again
Geographic page bloat is often a governance problem that becomes an SEO problem.
Franchisees, local teams, agencies, and corporate departments may all launch pages without anyone protecting the architecture as a whole.
A scalable governance model should define approved page types, creation criteria, required local content, URL conventions, ownership, and processes for openings, relocations, closures, and mergers.
It should also make a few questions unavoidable:
Who owns the page?
What real-world entity or market does it represent?
How will users reach it?
What makes it necessary?
Who updates it when the business changes?
Those answers should exist before the URL does.
Strong regional brands create authoritative pages for legitimate locations, add regional layers where they help customers, and reserve service area pages for markets with enough real business relevance to support them.
Every geographic page creates an ongoing obligation. It needs a purpose, accurate information, and something the rest of the website doesn’t already provide more effectively.
A strong local presence is measured by how clearly and accurately the website reflects the real-world business, not by the number of geographic URLs it publishes.

Build your local presence around real locations, markets, and service areas instead of turning every geographic keyword into a new URL.

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