<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search.png" class="attachment-large size-large wp-post-image" alt="The AI visibility index- Which brands are vanishing from AI search?" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Some brands dominate Google yet barely surface in AI answers. Here’s where traditional authority and AI recall diverge.

Some of the brands with the strongest SEO footprints barely appear in AI answers. Others with much smaller search footprints show up again and again.
Fractl analyzed how consistently AI models recommend brands, which brands disappear despite strong SEO footprints, and which signals separate the brands that overperform from those that under-index. (Disclosure: I’m the co-founder of Fractl.)



The short answer: organic authority still matters, but it’s not the whole map.
More than 9 in 10 brands in the dataset behaved the way most SEOs would expect: stronger traditional search authority generally tracked with stronger AI visibility. But the outliers are where the findings get interesting.
The gap between traditional search authority and AI recall is where the visibility challenge gets more complicated.
AI answers have default brands
Some brands with high domain ratings, large keyword portfolios, and millions of monthly organic visits barely surfaced in their own categories. Others with smaller traditional search footprints appeared far more often than their SEO metrics would predict.
Every industry we reviewed had a short list of brands that surfaced again and again, regardless of how the prompt was phrased.
Those default answers weren’t always the biggest companies in the category. They weren’t always the brands with the most traffic. And they weren’t always the companies a traditional SEO leaderboard would put first.




We noticed a few patterns emerge immediately:
Travel was the most concentrated category: Booking.com (285 mentions), Airbnb (227), and Expedia (215) accounted for roughly 20% of the sector’s total mention volume. Three brands accounted for one out of every five recommendations.
HealthTech had a runaway leader: Teladoc (275) led Amwell (220), the runner-up, by roughly 25%. The models have settled.
Wellness had the deepest bench: Peloton, Headspace, Calm, Whoop, and Oura all cleared 168 mentions. No single brand dominates, which means the category-leader slot is still up for grabs.
Insurance broke the way industry watchers should have expected: Lemonade (213) ranked above State Farm (172). Root Insurance (165) ranked above Progressive (114). The models cited the digital-native carriers first and treated the legacy ones as the alternatives, rather than the default.
Lifestyle was the loudest signal of the methodology working: Patagonia, Allbirds, Eileen Fisher, and Everlane (sustainability-coded direct-to-consumer brands) outranked Sephora, Samsung, and Whirlpool. Whether that’s a function of how product roundup articles get written or a deeper bias in training data, the result is the same: The models prefer the brand story that gets told in third-party content.
The pattern held across every sector. A handful of categories do have winners the models clearly recognize, but plenty of the time, those picks don’t line up with what you’d expect. Stripe wins in FinTech, Notion in SaaS, Coursera in Education, and so on. Most of the time, though, the default names aren’t the ones you’d predict.
That’s the first strategic shift for SEO leaders.
The competitive set inside an AI answer is much smaller than the competitive set on a search results page. If your brand can’t crack the top five to 10 names the model recalls for your category, ranking well in Google may not be enough to get you into the AI-generated consideration set.
Dig deeper: Hidden gem publishers outperform major media on audience affinity: Study



  
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Traditional authority doesn’t guarantee AI recall
More than 9 in 10 brands in our dataset were broadly aligned: strong traditional search authority usually tracked with stronger AI visibility. That’s the expected outcome. It’s also why marketers shouldn’t throw out every SEO principle they’ve spent the last decade building around.
But the edges of the dataset show where the strategy changes. About 5% (471 brands) were underexposed in LLMs. These were companies with high domain ratings, very high organic traffic, and a deep keyword portfolio that still drew almost no references from the models. 
On SEO infrastructure, they look like market leaders. On LLM references, they look like companies nobody has ever written about.




About 4% (377 brands) were AI overperformers. The models referenced them far more often than their modest traditional signals would suggest, much more aggressively in their verticals than Ahrefs data says they should be.
Roughly 9% lined up with how often a brand appeared in third-party content that it did not create. Brands that showed up disproportionately in roundups, expert lists, and comparison reviews tended to be far more visible to the models because that kind of coverage gets ingested into the training data. Brands with plenty of search authority but little third-party coverage tended to land in the underrepresented group, regardless of vertical.
That’s the strategic shift: What a brand says about itself matters less for AI recall than what the rest of the web has repeatedly said about it. Owned content still matters. Technical SEO still matters. But for AI visibility, the corroboration layer is becoming harder to ignore.




Some brands are miscategorized
The brands underrepresented in AI are, in many cases, the blue chips of their categories by most traditional measures. These brands have domain ratings above 80, millions of monthly organic visits, and hundreds of thousands of keywords ranked. But the models don’t even mention them in their own sectors.
The issue is categorization.




A few of the patterns we spotted:
Microsoft and Spotify topped the list in FinTech: They’re the giants of traditional visibility, but the models didn’t categorize them as fintechs when prompted with fintech queries. That’s not a bug. Microsoft is heavily cited when you ask about productivity software or cloud platforms. It’s a categorization gap. The models have decided what counts as a fintech, and Microsoft wasn’t on the list.
Legacy insurance carriers got displaced: Aetna, Cigna, Humana, Liberty Mutual, and Trustpilot all sat at DR 80+ with millions of monthly visits. The models barely cited them. Lemonade and Root sat higher in the same prompts despite a fraction of the traditional footprint.
Legacy retail brands got displaced: Sephora, Samsung, Whirlpool, and GE Appliances. The models preferred Patagonia, Allbirds, and Eileen Fisher when the prompt asked for lifestyle recommendations. This is the most visible signal that the training data heavily weights direct-to-consumer brand narratives over legacy retail.
Healthcare giants were under-indexed. Medtronic, GoodRx, Humana, and 23andMe. The models cited telehealth-native brands like Teladoc, Amwell, and Doxy.me instead.
Low recall doesn’t always mean the model doesn’t know your brand. It may mean the model has filed your brand under a different category than the one your buyers are searching.
More content alone won’t fix that.
You need stronger category signals in the places models are likely to encounter and reinforce those associations: analyst content, industry publications, comparison pages, partner pages, customer stories, review sites, and category-specific media coverage. Before you chase more AI mentions, make sure the models understand which category you’re supposed to win.
Dig deeper: What 1 million keywords reveal about AI’s impact on search




	
	
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The AI overperformers show the playbook
The other side may be even more helpful to marketers, since it shows how brands punching above their weight actually pull it off. Smaller Ahrefs profiles. Larger model recall. And all 377 of these brands show up repeatedly in the third-party material the models were trained on.




This list tells the story:
Monday.com was the biggest overperformer in the dataset, with an AI Visibility Score of 0.71. Monday.com already has a lot of traditional traffic, close to 1 million organic visitors, and more than 50,000 ranked keywords. Its overperformer status tracks directly with how much more often the models referenced it than its SaaS peers.
Root Insurance owned the insurance category outright, drawing more AI references than any of the legacy carriers despite being a fraction of their size.
Six of the top 15 overperformers were in education: Stanford, MIT, Khan Academy, LinkedIn Learning, IBM Data Science, and Google Career Certificates. The models cited these for their credibility, not because they run high-Domain-Rating commercial platforms.
Smaller players followed the same pattern. Doxy.me, Nike Training Club, and Google Flights all got pulled into product roundups, showed up heavily on expert lists, and drew an outsized number of LLM references as a result.
The common thread isn’t just “good SEO.” It’s category-level presence in other people’s content. That pushes AI visibility work closer to digital PR, content distribution, and category authority building than many teams want to admit.
If you want to know why a competitor with less than half your organic traffic shows up in the model’s answer while you don’t, the question to ask isn’t, “What’s their SEO strategy?” It’s, “What’s their press coverage, and how did they earn it?”
You’re not just optimizing your own site. You’re shaping the set of sources AI systems use to understand who belongs in the category.
Most brands exist in only one model’s world
Only 900 brands in our dataset, or 11% of the total, were referenced by all three models. Another 12% appeared in two. The overwhelming majority, 77%, were referenced by only one model.




That’s a major measurement problem. A brand can win in ChatGPT and lose in Gemini. It can show up in Claude and disappear from ChatGPT. It can own one model’s answer set while barely registering in another.
Each model also had a different fingerprint.
Claude leaned more heavily toward SaaS and insurance brands, with Notion, Linear, and Lemonade appearing more often there than in the other two models.
Gemini leaned toward travel and healthcare, with Booking.com, Teladoc, and Livongo appearing at a much higher rate.
ChatGPT was the most consensus-driven of the three, with the most overlap between its top brands and the other models.
The heatmap shows how unevenly distributed even consensus brands are:




The gaps weren’t always subtle. Notion was cited 17 times more often by Claude than by Gemini. Whoop appeared almost four times more often in Claude than Gemini. State Farm drew almost half its mentions from ChatGPT and only about a quarter from Gemini.
This is why aggregate AI visibility scores can mislead senior teams. If a dashboard says your “AI visibility” improved, your first question should be: Where?
A single blended score can hide the actual problem. You may not have an AI visibility problem broadly. You may have a Gemini problem. Or a Claude problem. Or a category prompt problem. Or a source problem.
Measure each model separately. Track prompts by category and intent. Look at which sources appear repeatedly. Then decide where the gap is worth closing.
Some sectors have already been re-sorted
The disconnect between traditional authority and AI recall wasn’t evenly distributed across industries. Some sectors still look a lot like Google, while others have been almost completely re-ranked by the models.




The pattern breaks down differently by sector:
Insurance was the least represented, at 8%. Legacy carriers like Aetna, Cigna, Humana, and Liberty Mutual were replaced by digitally native carriers. If you’re an established carrier, you have the largest gap between your Google authority and your AI recall.
Education had the highest overperformer rate, at 7%, with Stanford, MIT, Khan Academy, IBM Data Science, Google Career Certificates, and LinkedIn Learning dominating. Professional education and institutions do better here than the commercial platforms because they generate credibility content.
HealthTech was neutral (underrepresented by 5%, overperforming by 6%), with a shift underway from pharma and medical device companies to consumer-facing digital health.
Travel had the smallest disconnect (4% under, 4% over) because LLM recall and traditional awareness lined up closely. In travel, Booking.com, Expedia, and Airbnb are the best-known names to both Google and the models. Travel’s top performers are stable.
SaaS and FinTech sat between 3% to 4% over and 6% under. That’s because in these categories, the story is one incumbent replacing another (Stripe vs. PayPal, Notion vs. Microsoft) rather than new entrants displacing the old guard entirely.
The pattern across sectors mirrors the broader finding. Where the legacy authority of a category was built primarily on SEO and traffic, the models have re-sorted. Where the category was built around brand stories that circulated in third-party media, the alignment is tighter.
What this means for brand strategy
These are my five main lessons from the study, roughly ordered by how quickly you can act on them.
Measure traditional search authority and AI recall separately 
Domain rating, organic traffic, and keyword rankings still matter. They just don’t tell the whole story. 
A brand can rank well on Google and still fail to show up in AI answers. Another brand can have a smaller traditional SEO footprint and still become a default recommendation. Track both. 
Measure where you rank in Google and where you appear in AI responses. Then look for mismatches. The mismatches are where the strategy lives.
Build third-party validation, not just owned content
The brands that outperform in AI responses tend to appear repeatedly in other people’s content. That includes:
Roundups.
Best-of lists.
Comparison articles.
Expert reviews.
Analyst pages.
Podcasts. 
YouTube transcripts.
Community discussions.
Category-specific publisher coverage. 
Owned content can help clarify your positioning, but it won’t replace external corroboration. If AI systems are building answers from the broader web’s understanding of your brand, digital PR becomes one of the most practical levers for AI visibility.
Track each model separately 
Only 11% of brands were referenced by all three models. That should kill the idea that “AI visibility” is one clean metric. Measure ChatGPT, Gemini, and Claude separately. Break prompts out by product category, buyer intent, and comparison set. 
Track whether your brand appears, how it’s described, which competitors appear with it, and which sources seem to support the answer. A model-specific miss gives you a much clearer action plan than a blended visibility score.
Fix categorization before you chase volume
If your brand has strong overall awareness but weak recall in your target category, the issue may be categorization. The model may know who you are. It just may not think you belong in the answer set for the queries that matter. That requires a different strategy. 
You need more category-specific proof: media coverage, comparison content, partner references, customer stories, awards, reviews, and third-party pages that repeatedly connect your brand to the category you want to own.
Move before the default-answer slot hardens 
Some categories are still wide open. Wellness, lifestyle, and parts of HealthTech still have room for new default answers. 
Others are already consolidating. Travel, major SaaS categories, and parts of FinTech are much harder to break into because the models already return a tight set of familiar names. 
The earlier you build category association, the easier it is to influence recall. Once a model repeatedly sees the same brands attached to the same category, dislodging them gets harder.
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What the web says about your brand matters
Your next brand visibility audit shouldn’t stop at your site. It should map the sources that teach models who belongs in your category: roundups, comparisons, expert lists, publisher coverage, reviews, partner pages, and community discussions.
That’s where the overperformers in this study separated themselves, not by being the biggest brands in organic search, but by being repeatedly reinforced in the places AI systems use to understand, compare, and recommend companies.
Methodology



Fractl analyzed AI visibility across eight industries using GPT-4o, Gemini 2.5 Flash, and Claude Sonnet 4.6. Each model received the same 96 industry-specific prompts, with each prompt run 15 times per model. The analysis produced 4,320 responses and more than 8,500 unique brand references.
Fractl then mapped the brands identified in those responses to Ahrefs data, including domain rating, organic traffic, referring domains, and keyword volume. This made it possible to compare each brand’s traditional search authority with how frequently it appeared in AI-generated responses.
The analysis grouped brands based on how their AI visibility compared with their traditional search performance. Brands that appeared less often than their SEO metrics would suggest were classified as underrepresented, while brands that appeared more often were classified as overperformers.

Some brands dominate Google yet barely surface in AI answers. Here’s where traditional authority and AI recall diverge.

The AI visibility index: Which brands are vanishing from AI search?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png" class="attachment-large size-large wp-post-image" alt="The $113K paid search test- How much traffic did organic really replace" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Paid search looked valuable in the channel report. A 13-week test revealed how much of that value the business actually needed to buy.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png" class="attachment-large size-large wp-post-image" alt="The $113K paid search test- How much traffic did organic really replace" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">I turned off $113,000 a month in <a href="https://searchengineland.com/library/ppc">paid search</a>. </p>



<p class="wp-block-paragraph">Thirteen weeks later, 65% of the paid-attributed revenue had been recaptured through organic and direct, and the analysis showed that nearly nine out of 10 dollars in the branded campaign I studied had been defending clicks organic was already positioned to win.</p>



<p class="wp-block-paragraph">Every performance marketer has the same recurring nightmare.</p>



<p class="wp-block-paragraph">You turn off paid search. Traffic goes rogue. Revenue goes full &#8220;Thelma &amp; Louise&#8221; and drives off a cliff. Then, during Monday’s leadership meeting, someone asks (very calmly, which is somehow worse) what you thought would happen.</p>



<p class="wp-block-paragraph">So paid stays on. Forever.&nbsp;</p>



<p class="wp-block-paragraph">It becomes the unwanted houseguest eating everything in your fridge with no indication of finding an apartment. I wanted to know if it deserved to be there.</p>



<h2 id="h-experiment-were-they-spending-113k-buying-customers-or-buying-our-own-clicks" class="wp-block-heading">Experiment: Were they spending $113K buying customers or buying our own clicks?</h2>



<p class="wp-block-paragraph">I was asked to answer this question:&nbsp;</p>



<p class="wp-block-paragraph">How much of our paid search budget was bringing in customers they wouldn’t have reached otherwise, and how much was paying Google for clicks our organic listings were already winning?</p>



<p class="wp-block-paragraph">This was what I wanted to test by turning it off.</p>



<p class="wp-block-paragraph">A company paused branded paid search across four of its largest markets: the United States, United Kingdom, Australia, and Canada.</p>



<p class="wp-block-paragraph">By the end of the month, most non-brand paid search had been paused, too. Branded search never came back.</p>



<p class="wp-block-paragraph">Before the pause, the company was spending $106,000 to $119,000 per month across branded search, non-brand search, Shopping, and <a href="https://searchengineland.com/google-performance-max-431821">Performance Max</a>, so I’ll say $113,000 per month on average.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/paid-search-clicks-double-organic-clicks-fall-study-469519">Paid search click share doubles as organic clicks fall: Study</a></em></strong>


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<h2 id="h-the-questions-that-make-paid-search-hard-to-turn-off" class="wp-block-heading">The questions that make paid search hard to turn off</h2>



<p class="wp-block-paragraph">There’s always a “<strong>but what if</strong>.”</p>



<ul class="wp-block-list">
<li>What if the ad at the top of the page makes someone trust the brand a little more? </li>



<li>What if seeing paid and organic together gives us more real estate on the SERP? </li>



<li>What if competitors start bidding on our name? </li>



<li>What if turning paid off doesn’t move the click downstairs to organic but makes the click disappear entirely?</li>
</ul>



<p class="wp-block-paragraph">This is the paid search halo.</p>



<p class="wp-block-paragraph">And unlike Bigfoot, there’s some evidence it exists.</p>



<p class="wp-block-paragraph">Google studied more than 400 advertisers that paused search campaigns and found that, on average,<a href="https://research.google/pubs/incremental-clicks-impact-of-search-advertising/"> 89% of ad clicks were incremental</a>. Organic search didn’t magically pick up most of the traffic when the ads went away.<a href="https://research.google/pubs/impact-of-ranking-of-organic-search-results-on-the-incrementality-of-search-ads/"> Google’s follow-up research</a> also found that incrementality changes depending on whether an organic result appears and where it ranks.</p>



<p class="wp-block-paragraph">Which sounds like a fairly compelling reason to leave the credit card where it is.</p>



<p class="wp-block-paragraph">Except there’s another famous experiment.</p>



<p class="wp-block-paragraph"><a href="https://cepr.org/voxeu/columns/experiment-estimate-effectiveness-branded-search-ads">eBay turned off branded paid search</a> and found that almost all of the missing paid clicks and attributed sales were immediately<a href="https://www.theguardian.com/technology/2013/mar/13/google-keyword-advertising-wastes-money-ebay"> captured by organic search</a>. For a household-name brand with strong organic visibility, the ads were intercepting customers who already knew where they were going.</p>



<p class="wp-block-paragraph">So which one is right? Annoyingly: both.</p>



<p class="wp-block-paragraph">Paid search can create incremental traffic. It can also charge you admission for customers who were already standing in line.</p>



<p class="wp-block-paragraph">The only way to know which one you’re buying is to measure it. So I did.</p>



<p class="wp-block-paragraph">I wanted to know what our halo cost and whether there was anything underneath it.</p>



<h2 id="h-putting-paid-and-organic-in-one-spreadsheet-nbsp" class="wp-block-heading">Putting paid and organic in one spreadsheet&nbsp;</h2>



<p class="wp-block-paragraph">I brought every Google Ads search term and its associated spend together with every matching Search Console query and its organic clicks.</p>



<p class="wp-block-paragraph">Then I matched them keyword by keyword across the same period to understand what the company was paying for.</p>



<p class="wp-block-paragraph">I sorted each query into one of three categories:</p>



<ul class="wp-block-list">
<li><strong>Cannibalized:</strong> The company paid for a click organic was already positioned to win.</li>



<li><strong>Dependent:</strong> Organic performance benefited from paid support and declined when the ads disappeared.</li>



<li>Incremental: Paid search reached a customer that organic search couldn’t have captured.</li>
</ul>



<p class="wp-block-paragraph">Then I ran one of the company’s branded campaigns through the model.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/prevent-ppc-cannibalizing-seo-efforts-451920">How to prevent PPC from cannibalizing your SEO efforts</a></em></strong>



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<h2 id="h-the-impact-89-defense-11-offense" class="wp-block-heading">The impact: 89% defense, 11% offense</h2>



<p class="wp-block-paragraph">On queries where<a href="https://searchengineland.com/get-ppc-seo-working-together-386083"> paid and organic overlapped</a>, organic was already winning 71% of clicks, even with the ads running.</p>



<p class="wp-block-paragraph">Of the $36,129 spent in the branded campaign I analyzed, only $3,945, or 10.9%, appeared to buy clicks organic couldn’t have captured.</p>



<p class="wp-block-paragraph">The other $32,184?</p>



<p class="wp-block-paragraph">Brand defense.</p>



<p class="wp-block-paragraph">Nearly nine out of every 10 dollars were going toward traffic the company was already positioned to receive.</p>



<p class="wp-block-paragraph">Turns out, a lot of our “paid acquisition” was organic traffic with a cover charge.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Metric</strong></td><td><strong>Result</strong></td></tr><tr><td>Branded spend</td><td>$36,129</td></tr><tr><td>Organic share of overlapping clicks</td><td>71%</td></tr><tr><td>Incremental spend</td><td>$3,945</td></tr><tr><td>Incremental share</td><td>10.9%</td></tr><tr><td>Brand-defense spend</td><td>$32,184</td></tr><tr><td>Brand-defense share</td><td>89.1%</td></tr><tr><td>Annualized incremental spend</td><td>~$15,000</td></tr><tr><td>Annualized cannibalized spend</td><td>~$128,000</td></tr></tbody></table></figure>



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<h2 id="h-then-the-company-turned-off-paid-and-organic-moved-in" class="wp-block-heading">Then, the company turned off paid — and organic moved in</h2>



<p class="wp-block-paragraph">Six weeks after the pause, organic search began doing what I hoped it would do: reaching over and collecting some of the clicks paid search used to purchase.</p>



<p class="wp-block-paragraph">And once the full revenue data became available, the story became much easier to quantify.</p>



<p class="wp-block-paragraph">Organic revenue increased $18,000 to $28,000 per month over the pre-pause baseline.</p>



<p class="wp-block-paragraph">Direct revenue initially increased by about $14,000 per month. By week 13 and beyond, the direct revenue lift had climbed to roughly $54,000 per month.</p>



<p class="wp-block-paragraph">Combined organic and direct revenue recaptured 30% of the paid-attributed revenue within six weeks. By week 13, recapture had reached 65%.</p>



<p class="wp-block-paragraph">All while spending nothing to acquire those visits through paid search.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Time after pause</strong></td><td><strong>Organic revenue change/month</strong></td><td><strong>Direct revenue change/month</strong></td><td><strong>Recapture</strong></td></tr><tr><td>Weeks 1-6</td><td>+$17,800</td><td>+$14,500</td><td>30%</td></tr><tr><td>Weeks 7-12</td><td>+$28,100</td><td>+$14,100</td><td>39%</td></tr><tr><td>Weeks 13+</td><td>+$15,800</td><td>+$54,000</td><td>65%</td></tr></tbody></table></figure>



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<p class="wp-block-paragraph">Comparing the paid-on period with the later paid-off period, organic clicks increased across several of the company’s most commercially valuable product terms.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Query type</strong></td><td><strong>Paid on</strong></td><td><strong>Paid off</strong></td><td><strong>Change</strong></td></tr><tr><td>Core product term</td><td>5,144</td><td>5,858</td><td><strong>+14%</strong></td></tr><tr><td>Premium product term</td><td>638</td><td>990</td><td><strong>+55%</strong></td></tr><tr><td>Product variant</td><td>228</td><td>402</td><td><strong>+76%</strong></td></tr><tr><td>Category plural</td><td>315</td><td>444</td><td><strong>+41%</strong></td></tr><tr><td>Product variant</td><td>206</td><td>289</td><td><strong>+40%</strong></td></tr><tr><td>Brand + category</td><td>265</td><td>352</td><td><strong>+33%</strong></td></tr><tr><td>Entry-level product</td><td>433</td><td>490</td><td><strong>+13%</strong></td></tr></tbody></table></figure>



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<p class="wp-block-paragraph">Across every commercial query shown here, organic clicks went up after paid went away.</p>



<h2 id="h-clicks-came-back-faster-than-the-money" class="wp-block-heading">Clicks came back faster than the money</h2>



<p class="wp-block-paragraph">Paid and organic are two doors leading into the same room. Shut one, and customers don’t necessarily go home. Plenty of them use the other door.</p>



<p class="wp-block-paragraph">The catch is that they don’t all buy at the same rate. Paid traffic converted at about 2.9%. Organic converted closer to 2.3%.</p>



<p class="wp-block-paragraph">So the clicks came back faster than the revenue did.&nbsp;</p>



<p class="wp-block-paragraph">Did organic replace paid?</p>



<p class="wp-block-paragraph">Not perfectly. But far more of it than the original attribution ever suggested.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-ppc-trust-across-search-case-study-484383">How SEO and PPC build trust across search: Case study</a></em></strong>


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<h2 id="h-what-i-learned-revenue-isn-t-profit" class="wp-block-heading">What I learned: Revenue isn’t profit</h2>



<p class="wp-block-paragraph">This is usually where case studies start polishing the numbers until everyone involved looks brilliant. I won’t.</p>



<p class="wp-block-paragraph">Top-line revenue fell roughly $30,000 to $40,000 per month from the pre-pause baseline.</p>



<p class="wp-block-paragraph">That happened. But after accounting for media spend that was no longer going to Google, the working estimate put net P&amp;L roughly $5,000 to $20,000 per month higher.</p>



<p class="wp-block-paragraph">That may sound contradictory. It isn’t.</p>



<p class="wp-block-paragraph">Did the company sell slightly less? Yes.</p>



<p class="wp-block-paragraph">Did the business potentially make more money? Also yes.</p>



<p class="wp-block-paragraph">And that may be the most important thing this experiment uncovered.</p>



<p class="wp-block-paragraph">Channel dashboards are good at telling you what revenue an ad touched.</p>



<p class="wp-block-paragraph">They’re less good at telling you whether you needed to buy the touch in the first place.</p>



<p class="wp-block-paragraph">The company had been spending six figures a month on paid search. In the branded campaign I analyzed, 89% of the spend was defense rather than incremental acquisition.</p>



<p class="wp-block-paragraph">Six weeks after paid went dark, organic and direct had recaptured 30% of the lost paid-attributed revenue.</p>



<p class="wp-block-paragraph">By week 13, they’d recaptured 65%. That doesn’t mean branded paid search is bad.</p>



<p class="wp-block-paragraph">Competitors exist. SERPs change. Organic can’t capture every click. And in this experiment, paid traffic converted better.</p>



<p class="wp-block-paragraph">It means you should know what you’re paying for. “We’ve always run branded search” isn’t a strategy.</p>



<p class="wp-block-paragraph">Measure the halo. Price the defense. Give organic enough time to show you what it can carry.</p>



<p class="wp-block-paragraph">And above all, don’t confuse less attributed revenue with a worse business.</p>



<p class="wp-block-paragraph">This company turned off the money. The customers kept coming.</p>



<p class="wp-block-paragraph">They just stopped paying Google to introduce them to people who were already looking.</p>

I turned off $113,000 a month in paid search. 
Thirteen weeks later, 65% of the paid-attributed revenue had been recaptured through organic and direct, and the analysis showed that nearly nine out of 10 dollars in the branded campaign I studied had been defending clicks organic was already positioned to win.
Every performance marketer has the same recurring nightmare.
You turn off paid search. Traffic goes rogue. Revenue goes full “Thelma & Louise” and drives off a cliff. Then, during Monday’s leadership meeting, someone asks (very calmly, which is somehow worse) what you thought would happen.
So paid stays on. Forever. 
It becomes the unwanted houseguest eating everything in your fridge with no indication of finding an apartment. I wanted to know if it deserved to be there.
Experiment: Were they spending $113K buying customers or buying our own clicks?
I was asked to answer this question: 
How much of our paid search budget was bringing in customers they wouldn’t have reached otherwise, and how much was paying Google for clicks our organic listings were already winning?
This was what I wanted to test by turning it off.
A company paused branded paid search across four of its largest markets: the United States, United Kingdom, Australia, and Canada.
By the end of the month, most non-brand paid search had been paused, too. Branded search never came back.
Before the pause, the company was spending $106,000 to $119,000 per month across branded search, non-brand search, Shopping, and Performance Max, so I’ll say $113,000 per month on average.
Dig deeper: Paid search click share doubles as organic clicks fall: Study



  
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The questions that make paid search hard to turn off
There’s always a “but what if.”
What if the ad at the top of the page makes someone trust the brand a little more? 
What if seeing paid and organic together gives us more real estate on the SERP? 
What if competitors start bidding on our name? 
What if turning paid off doesn’t move the click downstairs to organic but makes the click disappear entirely?
This is the paid search halo.
And unlike Bigfoot, there’s some evidence it exists.
Google studied more than 400 advertisers that paused search campaigns and found that, on average, 89% of ad clicks were incremental. Organic search didn’t magically pick up most of the traffic when the ads went away. Google’s follow-up research also found that incrementality changes depending on whether an organic result appears and where it ranks.
Which sounds like a fairly compelling reason to leave the credit card where it is.
Except there’s another famous experiment.
eBay turned off branded paid search and found that almost all of the missing paid clicks and attributed sales were immediately captured by organic search. For a household-name brand with strong organic visibility, the ads were intercepting customers who already knew where they were going.
So which one is right? Annoyingly: both.
Paid search can create incremental traffic. It can also charge you admission for customers who were already standing in line.
The only way to know which one you’re buying is to measure it. So I did.
I wanted to know what our halo cost and whether there was anything underneath it.
Putting paid and organic in one spreadsheet 
I brought every Google Ads search term and its associated spend together with every matching Search Console query and its organic clicks.
Then I matched them keyword by keyword across the same period to understand what the company was paying for.
I sorted each query into one of three categories:
Cannibalized: The company paid for a click organic was already positioned to win.
Dependent: Organic performance benefited from paid support and declined when the ads disappeared.
Incremental: Paid search reached a customer that organic search couldn’t have captured.
Then I ran one of the company’s branded campaigns through the model.
Dig deeper: How to prevent PPC from cannibalizing your SEO efforts




	
	
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The impact: 89% defense, 11% offense
On queries where paid and organic overlapped, organic was already winning 71% of clicks, even with the ads running.
Of the $36,129 spent in the branded campaign I analyzed, only $3,945, or 10.9%, appeared to buy clicks organic couldn’t have captured.
The other $32,184?
Brand defense.
Nearly nine out of every 10 dollars were going toward traffic the company was already positioned to receive.
Turns out, a lot of our “paid acquisition” was organic traffic with a cover charge.

MetricResult
Branded spend$36,129
Organic share of overlapping clicks71%
Incremental spend$3,945
Incremental share10.9%
Brand-defense spend$32,184
Brand-defense share89.1%
Annualized incremental spend~$15,000
Annualized cannibalized spend~$128,000






Then, the company turned off paid — and organic moved in
Six weeks after the pause, organic search began doing what I hoped it would do: reaching over and collecting some of the clicks paid search used to purchase.
And once the full revenue data became available, the story became much easier to quantify.
Organic revenue increased $18,000 to $28,000 per month over the pre-pause baseline.
Direct revenue initially increased by about $14,000 per month. By week 13 and beyond, the direct revenue lift had climbed to roughly $54,000 per month.
Combined organic and direct revenue recaptured 30% of the paid-attributed revenue within six weeks. By week 13, recapture had reached 65%.
All while spending nothing to acquire those visits through paid search.

Time after pauseOrganic revenue change/monthDirect revenue change/monthRecapture
Weeks 1-6+$17,800+$14,50030%
Weeks 7-12+$28,100+$14,10039%
Weeks 13++$15,800+$54,00065%






Comparing the paid-on period with the later paid-off period, organic clicks increased across several of the company’s most commercially valuable product terms.

Query typePaid onPaid offChange
Core product term5,1445,858+14%
Premium product term638990+55%
Product variant228402+76%
Category plural315444+41%
Product variant206289+40%
Brand + category265352+33%
Entry-level product433490+13%






Across every commercial query shown here, organic clicks went up after paid went away.
Clicks came back faster than the money
Paid and organic are two doors leading into the same room. Shut one, and customers don’t necessarily go home. Plenty of them use the other door.
The catch is that they don’t all buy at the same rate. Paid traffic converted at about 2.9%. Organic converted closer to 2.3%.
So the clicks came back faster than the revenue did. 
Did organic replace paid?
Not perfectly. But far more of it than the original attribution ever suggested.
Dig deeper: How SEO and PPC build trust across search: Case study



  
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What I learned: Revenue isn’t profit
This is usually where case studies start polishing the numbers until everyone involved looks brilliant. I won’t.
Top-line revenue fell roughly $30,000 to $40,000 per month from the pre-pause baseline.
That happened. But after accounting for media spend that was no longer going to Google, the working estimate put net P&L roughly $5,000 to $20,000 per month higher.
That may sound contradictory. It isn’t.
Did the company sell slightly less? Yes.
Did the business potentially make more money? Also yes.
And that may be the most important thing this experiment uncovered.
Channel dashboards are good at telling you what revenue an ad touched.
They’re less good at telling you whether you needed to buy the touch in the first place.
The company had been spending six figures a month on paid search. In the branded campaign I analyzed, 89% of the spend was defense rather than incremental acquisition.
Six weeks after paid went dark, organic and direct had recaptured 30% of the lost paid-attributed revenue.
By week 13, they’d recaptured 65%. That doesn’t mean branded paid search is bad.
Competitors exist. SERPs change. Organic can’t capture every click. And in this experiment, paid traffic converted better.
It means you should know what you’re paying for. “We’ve always run branded search” isn’t a strategy.
Measure the halo. Price the defense. Give organic enough time to show you what it can carry.
And above all, don’t confuse less attributed revenue with a worse business.
This company turned off the money. The customers kept coming.
They just stopped paying Google to introduce them to people who were already looking.

Paid search looked valuable in the channel report. A 13-week test revealed how much of that value the business actually needed to buy.

The $113,000 paid search test: How much traffic did organic replace?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads.png" class="attachment-large size-large wp-post-image" alt="Inside ChatGPT&#039;s retrieval stack: The index, the cache, and the pages it actually reads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>New research exposes the mechanics behind ChatGPT citations, including what gets retrieved, what gets read, and what ultimately gets cited.


When ChatGPT cites a web page, where did that page come from?
In July, our team at RESONEO captured and dissected 1,200 ChatGPT answers, 88,000 search results, and 26,900 distinct pages to find out. (Disclosure: I’m the co-founder of RESONEO.)



We found three layers in how ChatGPT grounds its answers: a discovery index that finds pages, a reading cache that keeps full copies of pages it has fetched, and a small set of pages it opens live.
Each layer has its own rules, its own staleness, and its own blind spots. Understanding those layers makes much of ChatGPT’s citation behavior easier to explain — including why some pages are retrieved, some are read, and some ultimately get cited.
How we uncovered ChatGPT’s retrieval system
Our Chrome extension records the raw data stream ChatGPT sends to the browser, including fields the interface never displays. 
Until July 21, that stream contained a field called result_source that named the internal pipeline behind every single search result. 
Four values kept coming back: labrador, bright, oxylabs, and serp. None of them is documented by OpenAI, which only speaks publicly of “third-party search providers.”
Then the field vanished from the stream. It happened overnight, on every account we monitor. We now reidentify each pipeline by its formatting signatures (i.e., snippet length, title shape), with a classifier that gets it right about 98% of the time. V5 of our extension ships with that classifier built in. 
We also ran a fleet of accounts across tiers and countries, replayed the same prompts through OpenAI’s API, and published canary pages on our own domain with full server logs, so we could see exactly what OpenAI’s robots fetch rather than guess.
Dig deeper: Inside ChatGPT Search: how web.run and fan-out queries shape AI visibility



  
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The cast of pipelines
What we had established before this round, and what still holds:
labrador is OpenAI’s own retrieval hub. Besides web search, it returns news, academia (arXiv), Reddit, and YouTube results, so it acts as an orchestrator of in-house indexes, open repositories like Wikipedia, and news partnerships.
bright and oxylabs are scraped Google results, bought live.
P1/P2/P3 serve shopping from OpenAI’s own merchant feeds, with Google kept around as an oracle for prices and reviews.
B1/B3, Yelp, and TripAdvisor serve local results, with a twist: The links displayed to users point to Google Maps.
Shopping and local never touch web search. If you sell products or run a restaurant, your battle is fought in merchant feeds and business listings, on top of the classic citations. 
This article focuses on the web search side because that’s where the surprises were.
August update: The Think button changes which web ChatGPT sees
Six weeks after the July study, we replayed the same prompts across every ChatGPT configuration: free and paid, Instant, Think, and Thinking, at different effort levels. Until this rollout, Thinking was a paid-only product mode. Free users now effectively have two retrieval regimes: Instant and Think. The first surprise is that source volume and source origin are now two separate questions.
On a free account, clicking Think more than doubles the retrieval pool, from 15.1 to 35.3 URLs per conversation and from 9.8 to 16.3 distinct domains. That puts free Think at almost exactly the same source volume as paid Thinking at medium effort.
But the two modes don’t search the same web. Free Think gets 74.7% of its results from labrador, OpenAI’s own index, just 3.1% from classic scraped Google web results and 22.2% from oxylabs, which feeds its news channel. Paid Thinking is almost the mirror image: 75.3% scraped Google and 24.7% labrador.
That distinction matters for AI search visibility. Two people can ask the same question, retrieve roughly the same number of sources, and still get answers built from very different corpora. Ranking well in Google remains important for paid Thinking, but it doesn’t guarantee that a page will enter the free Think retrieval set, where OpenAI’s own index is the main gatekeeper.
Paid Thinking also became much narrower between July and August. On the same medium-effort prompts, fan-outs fell from 3.56 to 1.90 per conversation, URLs from 47.2 to 33.9, and distinct domains from 21.9 to 15.5. At advanced effort, the contraction is stronger still: 67 URLs became 40.5, and 29.5 domains became 14.7. Our captures date the break between July 26 and July 31, before OpenAI’s August 6 model refresh.
Free Instant didn’t show a detectable change in retrieval volume, but the product behavior around it did change sharply: reasoning appeared in 96.7% of our August answers, versus 0.6% in July, and the pipeline mix was redistributed.
The queries themselves also became more navigational. The site: operator appears in 3.5% of free Instant queries, 10.8% of free Think queries, 41.9% of medium-effort paid Thinking queries and 58.1% at high effort. 
At high effort, site: usage rose from 40.8% in late July to 58.1% in mid-August, even as the total query count fell. ChatGPT is searching less broadly and more often going straight to domains, brands and official sources it already expects to be useful.
One final caveat for anyone benchmarking through the API: The API doesn’t reproduce the product. We replayed identical prompts across four ChatGPT product regimes and four API models, then measured the overlap between the brands each answer mentioned using Jaccard similarity. 
API variants overlap with one another at 0.32–0.39, ChatGPT regimes at 0.25–0.37, but product-to-API similarity falls to just 0.23–0.27. The API is therefore useful for probing what the underlying model family knows about a brand, but not for predicting a ChatGPT answer. 
Even two ChatGPT regimes share only about a third of the brands they mention because the retrieval corpus, tools, system instructions, and compute differ. The closest pairs do line up with OpenAI’s announced mapping: free Think is closest to Luna and paid ChatGPT is closest to Sol, but those model names aren’t exposed in the ChatGPT UI.
OpenAI’s web index works differently from Bing
OpenAI has its own web index, and Bing doesn’t supply it. We checked three ways. Only 1.5% of labrador URLs appear in Bing’s top 20 for the same fan-outs. 
No labrador snippet matches a Bing snippet. Bing hard-caps titles at 75 characters, while 24% of labrador titles sail past that limit. The longest title we captured runs 289 characters.
That index feeds essentially all search citations in instant mode, the fast mode that free users get. The exception is questions whose answer requires near-live data (“who won last night’s game”), where it splits roughly 50/50 with scraped Google results.
What does a result from that index actually contain? Three things: 
The URL. 
The full page title, never truncated, even when it’s absurdly long.
A snippet of about 200 characters.
The snippet construction deserves a close look because it’s the text from your page that reaches the model in instant mode. It’s anchored on your H1, rendered in caps, plus whatever visible text sits immediately around it. That can include things you wouldn’t necessarily choose: 
The category label above the title.
The alt text of the image below it.
The byline.
The publication date.
A table of contents. 
We measured one case where the snippet was 100% table of contents and 0% content. Whatever occupies those 200 characters is what gets picked up, and nothing else. The meta description is ignored entirely.
One more thing about that snippet: It doesn’t depend on the query. The same URL returns the same frozen snippet whether the user asked about pricing, history, or side effects. It’s cut at indexing time and served as is until the next crawl.
By modern search standards, this is pretty crude. Query-independent snippets are the kind of thing web search left behind about two decades ago, and here they are in 2026, powering one of the world’s most-used AI assistants. 
We assume this is a placeholder and that it’ll improve fast. For now, it’s what your content gets judged on.
For comparison, bright results behave like Google results: titles are cut around 60 characters with an ellipsis, snippets are around 160 characters, and the meta description is used about one time in three. Your meta description does nothing for labrador and still works for the Google-fed pipelines.
Dig deeper: Bing, not Google, shapes which brands ChatGPT recommends



The routing is economic, not technical
Why does OpenAI maintain both? Cost is the answer.
In instant mode, ChatGPT must answer in a few seconds, and the user typically pays nothing. So it queries only what OpenAI already owns: the labrador hub. No page gets opened (zero pages opened in 93% of instant answers we captured). The response is grounded in titles and 200-character snippets, full stop.
In thinking mode, the user pays a subscription and accepts the wait. Now the expensive tools are invoked: scraped Google rankings through bright (75% of thinking search results) and real page opens performed by the ChatGPT-User robot. A thinking conversation pulls around 100 results across some 28 domains, compared with around 11 results in instant.
Everything that costs OpenAI money (paying scraping providers) or time (opening URLs, calling live APIs) is reserved for the mode where users pay and wait. Here’s why this matters commercially: More than 90% of ChatGPT users are on the free tier. 
Free means instant, and instant means labrador. The pipeline nobody had heard of six months ago is the one deciding visibility in the vast majority of ChatGPT conversations happening right now.
The funnel numbers make the stakes concrete. Across our corpus, 61,332 URLs were surfaced into the sources sidebar. 5,032 became the lead source behind a citation. 759 pages were actually opened, all in thinking mode. An opened page ends up cited 74% of the time. A page merely retrieved but never opened, 7%.
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A reading cache shared by everyone
This is the part Jérôme Salomon of Oncrawl dug into with us, and it changed how we think about “ChatGPT visited my page.”
Alongside the index, ChatGPT maintains a cache of every page it has ever fetched. Not snippets: full pages, converted from HTML to Markdown, and stored that way. The cache is keyed by URL and shared across all users and tiers. If a free user in Berlin asks about a page that a paying user in Ohio triggered a fetch on last week, Berlin gets Ohio’s copy.
The refresh logic is what engineers call stale-while-revalidate. A copy is considered fresh for about 30 minutes. Within that window, everyone gets the stored version, and your server sees nothing. 
Past 30 minutes, the user still gets the stale copy immediately, and a background fetch refreshes it for the next person. This means the recrawl schedule of your pages is set by one signal only: how often ChatGPT users ask about them. Popular pages stay fresh. Unpopular pages age indefinitely. 
Salomon documented copies served more than 90 days after the fetch, with no eviction cap in sight. Your Cache-Control: no-store header? Ignored. Your noindex? Ignored too.
The Markdown conversion has its own personality. 
Scripts, iframes, and JSON-LD are stripped (yes, your structured data never reaches the model through this path). 
Image alt text survives. 
Text hidden by CSS is still extracted, so the robot reads content human visitors never see. 
We’ll leave the implications of that as an exercise for the reader and note that cloaking detection does not yet appear to be OpenAI’s strong suit.
Two hard limits frame every fetch. The robot does not execute JavaScript, so client-side content is invisible to it. And pages are capped at exactly 4 MB. Past that, the page is not truncated. It is rejected outright with an HTTP 400, and the model reads nothing at all. A page that is too heavy is not partially read. It does not exist.
One surprise hides in all this: an API parameter returns the crawl date of the stored copy of a URL. Fetch your own pages through it, and you learn when a ChatGPT user last caused your page to be read. That is a real, nearly free exposure metric, and nobody is watching it.
The analytics blind spot
You’ve probably seen utm_source=chatgpt.com in your analytics reports. That parameter is appended to the clickable links displayed to users. It tracks outbound clicks. 
But not every citation carries that tracking parameter. We observed that the pages ChatGPT opens on its own in thinking mode carry no utm_source in the citations displayed to users.
So the pages the model actually read in full, the ones with a 74% citation rate, leave zero UTM trace. If you gauge your ChatGPT exposure by filtering on that parameter, you’re counting the clicks and missing the reads. Also, watch for the ChatGPT-User user agent in your server logs.
The mystery: Citations that come from nowhere
One finding resists explanation, and we want the community’s help on it.
A share of the results reaching the model carry no snippet at all, just a title and a URL. We see it for arXiv or Reddit results, which come from databases OpenAI has direct access to, so that part is no surprise. But there are many others. 
In instant mode, snippet-less URLs (excluding Reddit, YouTube, and arXiv) get cited more than URLs with snippets (14.9% versus 8.2%). The model prefers citing pages it has read the least about.
Stranger still, some cited links match no search result at all. Our hypothesis is that the model writes them from its parametric memory, the knowledge baked into its weights during training. These are almost always bare domain roots of well-known sites, dutifully suffixed with utm_source=chatgpt.com as if they came from a search. 
A meaningful share of what ChatGPT ends up citing may simply be what it remembers, not what it retrieved. We can observe the phenomenon, but we can’t yet size it precisely, and we would genuinely welcome challengers on this one.
A related oddity: arXiv was pulled more than 2,600 times in our corpus and cited 10 times. Reddit, same pattern. ChatGPT reads preprints and forums to think, then shows the reader ordinary web pages. Being retrieved and being cited are two different markets.
Dig deeper: ChatGPT topic ownership is rare, and SEO alone doesn’t explain it



So, should you write 289-character titles?
The full title reaches the model untruncated, so it’s part of your grounding budget. The 200 characters after your H1 are the only body text the model sees in instant mode.
As such, moving down the category labels, dates, and widgets that sit between your H1 and your first useful sentence directly improves what ChatGPT knows about your page. Both of those are real, measured, and actionable today.
Act on them with a light touch. 
Write the title as a self-contained sentence rather than a truncation-optimized label. 
Make the first 200 characters after the H1 carry your core message. 
Keep an informative alt text near the top. 
Keep your meta description anyway because the Google-fed pipelines still use it. 
Stay under 4 MB and readable without JavaScript.
But don’t rebuild your site around a quirk. Everything in this article describes a system that evolves very fast: the result_source field disappeared overnight, shopping providers were anonymized in the same week, and Google Shopping tokens went from readable to encrypted in four days. Any specific mechanism we documented can be gone by the time you deploy.
Two structural facts, though, are safe to build on. 
LLMs have knowledge with a cutoff date, past which they know nothing, so they’re condemned to lean on search engines to bridge the gap between their training data and today. Retrieval will remain a permanent dependency of every AI assistant. 
Every engine they lean on will be pushed toward Google-grade quality because rudimentary indexes produce rudimentary answers, and users notice. The 200-character snippet probably won’t last long. The need to be the best answer will. That’s why the classic fundamentals, crawlable pages, clear titles, and content that actually answers the question keep paying off no matter how the plumbing changes.
The durable battle: Owning the answers to real questions
Optimizing snippet mechanics is a tactic. The strategy is upstream: knowing what people actually ask AI assistants and being the source that answers it.
Nobody asks ChatGPT for “a stroller with a 49 cm frame width.” They ask for “a stroller that fits through metro ticket gates.” 
Nobody searches for the foam firmness of headphone ear pads. They ask which headphones stay comfortable when you wear glasses. They ask which sofa survives a cat that scratches. 
That gap between spec-sheet language and human language is exactly where AI visibility is decided because assistants answer conversational questions with whatever content maps to them best.
You already own half the corpus that reveals those questions: 
Customer reviews.
Support conversations.
The emails you receive every day.
Surveys nobody reopened. 
The other half lives outside your walls, in what people say about your products on forums, on social platforms, in videos, in the press, and on blogs. 
Read both sides and the key benefits and pain points of your products emerge in your customers’ own words, which are also the words AI assistants hear. 
Mine that language, answer it on pages the robots can read, and you’re optimizing for every retrieval system at once, including the ones OpenAI hasn’t built yet.
What remains open
We don’t know which robot populates the labrador index. Our canary pages caught OAI-SearchBot nearly silent while the index kept serving results. 
Reddit is the easy case, since OpenAI has enjoyed real-time access to Reddit’s data API under its 2024 partnership. 
YouTube is another story, with no public agreement and no visible fingerprint explaining how those results get in. And the over-citation of snippet-less URLs still rests on a hypothesis.
OpenAI’s stack keeps getting harder to observe. Fields vanish from the stream, providers get anonymized, routing shifts week to week, and the whole thing grows more complex over time. 
We published the full study, What ChatGPT pulls, what it shows, what it cites, including the methodology and data behind every number in this article, so others can challenge these findings or push further.

New research exposes the mechanics behind ChatGPT citations, including what gets retrieved, what gets read, and what ultimately gets cited.

Inside ChatGPT’s retrieval stack: The index, cache, and pages it actually reads

<div><img width="1920" height="946" src="https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode-768x378.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode-1536x757.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>This is first rolling out  AI Mode for Google AI Pro &#038; Ultra subscribers in English.

<div><img width="1920" height="946" src="https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode-768x378.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode-1536x757.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Google is bringing Gemini 3.7 Flash, its latest AI model, to Google Search. It is rolling out today globally in AI Mode for Google AI Pro &amp; Ultra susbcribers in English.</p>



<p class="wp-block-paragraph"><strong>More details. </strong>Gemini 3.7 Flash was announced <a href="https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/">yesterday</a> as Google&#8217;s &#8220;most intelligent workhorse model yet for coding and agents.&#8221; But there was no mention of it being used in Google Search. So I asked Google and today, Robby Stein of Google <a href="https://x.com/rmstein/status/2088325481599009146">announced</a> on X:</p>



<ul class="wp-block-list">
<li>&#8220;We’re bringing Gemini 3.7 Flash to Search!&#8221;</li>
</ul>



<p class="wp-block-paragraph">He added:</p>



<ul class="wp-block-list">
<li>&#8220;Rolling out today globally in AI Mode for Google AI Pro &amp; Ultra subs in English.&#8221;</li>
</ul>



<p class="wp-block-paragraph"><strong>What does it do? </strong>Gemini 3.7 makes AI Mode better at &#8220;following instructions&#8221; and &#8220;understanding your intent.&#8221;  Robby Stein said this helps &#8220;you get even more helpful responses,&#8221; from AI Mode.</p>



<p class="wp-block-paragraph"><strong>How do you access it. </strong>To access Gemini 3.7, if you are a paid subscriber, Click the ‘+’ icon to select the model within AI Mode:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1920" height="946" src="https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode.jpg" alt="" class="wp-image-485066" srcset="https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode-768x378.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/gemini-37-flash-aimode-1536x757.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>



<p class="wp-block-paragraph"><strong>Why we care. </strong>Google will likely roll this mode out to all searchers, even free plans, in the coming weeks or months.  The models continue to improve and get better, which should make Google Search and AI Mode better over time.</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">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
      </div>
      <p id="semrush-one-subhead">
       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>



Google is bringing Gemini 3.7 Flash, its latest AI model, to Google Search. It is rolling out today globally in AI Mode for Google AI Pro & Ultra susbcribers in English.
More details. Gemini 3.7 Flash was announced yesterday as Google’s “most intelligent workhorse model yet for coding and agents.” But there was no mention of it being used in Google Search. So I asked Google and today, Robby Stein of Google announced on X:
“We’re bringing Gemini 3.7 Flash to Search!”
He added:
“Rolling out today globally in AI Mode for Google AI Pro & Ultra subs in English.”
What does it do? Gemini 3.7 makes AI Mode better at “following instructions” and “understanding your intent.”  Robby Stein said this helps “you get even more helpful responses,” from AI Mode.
How do you access it. To access Gemini 3.7, if you are a paid subscriber, Click the ‘+’ icon to select the model within AI Mode:




Why we care. Google will likely roll this mode out to all searchers, even free plans, in the coming weeks or months.  The models continue to improve and get better, which should make Google Search and AI Mode better over time.
customers won’t either.
      
See your AI visibility

This is first rolling out  AI Mode for Google AI Pro & Ultra subscribers in English.

Gemini 3.7 Flash rolling out in Google Search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="The 4-step roadmap to AI agents for Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Know when to use off-the-shelf AI, when to build custom systems, and what foundations you need before agents can deliver real value.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="The 4-step roadmap to AI agents for Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-4-step-roadmap-to-AI-agents-for-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Every week, there&#8217;s another announcement about <a href="https://searchengineland.com/ai-agents-ppc-what-to-know-build-457318">AI agents</a>. Google is building them, software vendors are selling them, and LinkedIn would have you believe every marketing team will soon have an autonomous employee managing campaigns around the clock. </p>



<p class="wp-block-paragraph">It&#8217;s easy to conclude that the next competitive advantage is building an AI agent as quickly as possible.</p>



<p class="wp-block-paragraph">While that idea may hold some truth, I think it’s the wrong perspective.</p>



<p class="wp-block-paragraph">After spending the last year building agentic systems for Google Ads, it’s become clear that not every organization is ready for an AI agent. The businesses that see genuine commercial value all follow roughly the same journey, while the ones that struggle usually skip straight to the expensive part.</p>



<h2 id="h-1-build-a-foundation-before-involving-ai" class="wp-block-heading">1. Build a foundation before involving AI</h2>



<p class="wp-block-paragraph">Before experimenting with AI, invest in two foundations: your knowledge base and your data. This is the least exciting stage, which is probably why it&#8217;s the one I often see businesses skip.</p>



<p class="wp-block-paragraph">One of the biggest misconceptions surrounding AI is that it compensates for poor processes. In reality, it simply automates those processes faster.</p>



<p class="wp-block-paragraph">The quality of any AI system is less about the model you use than the context you give it. Even the most capable large language model (LLM) can&#8217;t make sensible decisions if your business knowledge is inaccessible and your data is fragmented across multiple platforms.</p>



<p class="wp-block-paragraph">Your knowledge base should document the following elements in a format AI can understand:</p>



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



<li>Services.</li>



<li>Business rules.</li>



<li>Tone of voice.</li>



<li>Campaign structure.</li>



<li>Internal processes.</li>
</ul>



<p class="wp-block-paragraph">At the same time, make sure your marketing data is accurate, connected, and accessible. Whether you use BigQuery or another centralized warehouse matters less than eliminating the silos that stop AI from seeing the full picture.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/agentic-ai-and-vibe-coding-the-next-evolution-of-ppc-management-467805">Agentic AI and vibe coding: The next evolution of PPC management</a></em></strong>


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




<h2 id="h-2-exhaust-off-the-shelf-ai-before-you-build" class="wp-block-heading">2. Exhaust off-the-shelf AI before you build</h2>



<p class="wp-block-paragraph">You don&#8217;t need developers to start benefiting from AI. I&#8217;d argue most Google Ads teams haven&#8217;t yet exhausted what today&#8217;s off-the-shelf tools can already do. Many advertisers underestimate what&#8217;s already possible.</p>



<p class="wp-block-paragraph">Start by exporting campaign data into ChatGPT or Claude. Ask it to audit account structure, identify wasted spend, surface search term opportunities, or review your shopping feed. The latest models are remarkably capable at analyzing large datasets, often uncovering patterns that would previously have taken hours in spreadsheets.</p>



<p class="wp-block-paragraph">Next, link these tools with Google Ads, Google Analytics, or Google Merchant Center through pre-built <a href="https://searchengineland.com/mcp-data-tools-484650">Model Context Protocol</a> (MCP) connectors. Instead of exporting spreadsheets every week, this approach lets you query live account data while retaining persistent business context through projects or custom GPTs.</p>



<p class="wp-block-paragraph">For many organizations, this combination will deliver the majority of the value they&#8217;ll ever need. Build only when you&#8217;ve genuinely reached the limits of this setup.</p>



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



<h2 id="h-3-build-custom-systems-when-you-need-the-complexity" class="wp-block-heading">3. Build custom systems when you need the complexity</h2>



<p class="wp-block-paragraph">Eventually, you might outgrow off-the-shelf tools. This happens when your requirements become more specific.</p>



<p class="wp-block-paragraph">For instance, you might need to combine advertising performance with stock availability, pricing, margin, and customer relationship management (CRM) data. Or, you might want AI to continuously monitor accounts instead of waiting for prompts or automate approval workflows while retaining appropriate controls.</p>



<p class="wp-block-paragraph">This is when custom development becomes worthwhile.</p>



<p class="wp-block-paragraph">Developers make AI systems more reliable. Together, custom MCPs, guardrails, orchestration, scheduling, and cost optimization transform an interesting demo into a system that&#8217;s dependable enough to use every day.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/why-ppc-ai-agents-fail-without-business-data-476961">Why PPC AI agents fail without business data</a></em></strong>



<h2 id="h-4-identify-and-encourage-early-ai-adopters" class="wp-block-heading">4. Identify and encourage early AI adopters</h2>



<p class="wp-block-paragraph">Ironically, the biggest obstacle to successful AI adoption rarely has anything to do with technology. Instead, it&#8217;s people.</p>



<p class="wp-block-paragraph">The organizations that progress the fastest don’t necessarily expect every employee to become an AI expert overnight. They identify enthusiastic early adopters and give them space to experiment. Then, they encourage early adopters to share what works and gradually embed those workflows across the wider team.</p>



<p class="wp-block-paragraph">Rather than replacing marketers, AI changes how and where marketers create value.</p>



<p class="wp-block-paragraph">We&#8217;ve already spent the last decade handing more execution to algorithms via Smart Bidding, broad match, and Performance Max. Agentic AI is simply the next stage of that evolution. The marketer&#8217;s role continues to shift away from manual execution toward strategy and judgment.</p>


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




<h2 id="h-the-goal-isn-t-autonomous-marketing" class="wp-block-heading">The goal isn&#8217;t autonomous marketing</h2>



<p class="wp-block-paragraph">The biggest mistake I see is businesses trying to automate every aspect of Google Ads. That isn’t where the value lies.</p>



<p class="wp-block-paragraph">Agentic AI is exceptionally good at repetitive, data-heavy work like auditing accounts, monitoring performance, analyzing trends, and surfacing optimization opportunities. When you take those tasks off experienced marketers’ to-do lists, they can spend more time on strategy, creative problem-solving, and business objectives.</p>



<p class="wp-block-paragraph">The teams that outperform over the next few years won&#8217;t necessarily have the most sophisticated agentic AI. They&#8217;ll be the ones who understand where AI creates leverage, where human judgment still matters, and how to build the foundations that allow the two to work together.</p>



<p class="wp-block-paragraph">That&#8217;s why the first step is building an organization that’s ready to benefit from AI agents.</p>

Every week, there’s another announcement about AI agents. Google is building them, software vendors are selling them, and LinkedIn would have you believe every marketing team will soon have an autonomous employee managing campaigns around the clock. 
It’s easy to conclude that the next competitive advantage is building an AI agent as quickly as possible.
While that idea may hold some truth, I think it’s the wrong perspective.
After spending the last year building agentic systems for Google Ads, it’s become clear that not every organization is ready for an AI agent. The businesses that see genuine commercial value all follow roughly the same journey, while the ones that struggle usually skip straight to the expensive part.
1. Build a foundation before involving AI
Before experimenting with AI, invest in two foundations: your knowledge base and your data. This is the least exciting stage, which is probably why it’s the one I often see businesses skip.
One of the biggest misconceptions surrounding AI is that it compensates for poor processes. In reality, it simply automates those processes faster.
The quality of any AI system is less about the model you use than the context you give it. Even the most capable large language model (LLM) can’t make sensible decisions if your business knowledge is inaccessible and your data is fragmented across multiple platforms.
Your knowledge base should document the following elements in a format AI can understand:
Products.
Services.
Business rules.
Tone of voice.
Campaign structure.
Internal processes.
At the same time, make sure your marketing data is accurate, connected, and accessible. Whether you use BigQuery or another centralized warehouse matters less than eliminating the silos that stop AI from seeing the full picture.
Dig deeper: Agentic AI and vibe coding: The next evolution of PPC management



  
your competitors win.
      
Analyze your competitors
    
2. Exhaust off-the-shelf AI before you build
You don’t need developers to start benefiting from AI. I’d argue most Google Ads teams haven’t yet exhausted what today’s off-the-shelf tools can already do. Many advertisers underestimate what’s already possible.
Start by exporting campaign data into ChatGPT or Claude. Ask it to audit account structure, identify wasted spend, surface search term opportunities, or review your shopping feed. The latest models are remarkably capable at analyzing large datasets, often uncovering patterns that would previously have taken hours in spreadsheets.
Next, link these tools with Google Ads, Google Analytics, or Google Merchant Center through pre-built Model Context Protocol (MCP) connectors. Instead of exporting spreadsheets every week, this approach lets you query live account data while retaining persistent business context through projects or custom GPTs.
For many organizations, this combination will deliver the majority of the value they’ll ever need. Build only when you’ve genuinely reached the limits of this setup.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
3. Build custom systems when you need the complexity
Eventually, you might outgrow off-the-shelf tools. This happens when your requirements become more specific.
For instance, you might need to combine advertising performance with stock availability, pricing, margin, and customer relationship management (CRM) data. Or, you might want AI to continuously monitor accounts instead of waiting for prompts or automate approval workflows while retaining appropriate controls.
This is when custom development becomes worthwhile.
Developers make AI systems more reliable. Together, custom MCPs, guardrails, orchestration, scheduling, and cost optimization transform an interesting demo into a system that’s dependable enough to use every day.
Dig deeper: Why PPC AI agents fail without business data



4. Identify and encourage early AI adopters
Ironically, the biggest obstacle to successful AI adoption rarely has anything to do with technology. Instead, it’s people.
The organizations that progress the fastest don’t necessarily expect every employee to become an AI expert overnight. They identify enthusiastic early adopters and give them space to experiment. Then, they encourage early adopters to share what works and gradually embed those workflows across the wider team.
Rather than replacing marketers, AI changes how and where marketers create value.
We’ve already spent the last decade handing more execution to algorithms via Smart Bidding, broad match, and Performance Max. Agentic AI is simply the next stage of that evolution. The marketer’s role continues to shift away from manual execution toward strategy and judgment.
a customer you lose.
      
See who’s stealing your traffic
    
The goal isn’t autonomous marketing
The biggest mistake I see is businesses trying to automate every aspect of Google Ads. That isn’t where the value lies.
Agentic AI is exceptionally good at repetitive, data-heavy work like auditing accounts, monitoring performance, analyzing trends, and surfacing optimization opportunities. When you take those tasks off experienced marketers’ to-do lists, they can spend more time on strategy, creative problem-solving, and business objectives.
The teams that outperform over the next few years won’t necessarily have the most sophisticated agentic AI. They’ll be the ones who understand where AI creates leverage, where human judgment still matters, and how to build the foundations that allow the two to work together.
That’s why the first step is building an organization that’s ready to benefit from AI agents.

Know when to use off-the-shelf AI, when to build custom systems, and what foundations you need before agents can deliver real value.

The 4-step roadmap to AI agents for Google Ads

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google has clarified that Merchant Center for Agencies accounts can now link up to 1,000 client Merchant Center accounts

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-1000-limit-for-Merchant-Centre-for-Agencies-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google has updated its Merchant Center documentation to clarify that Merchant Center for Agencies can now link up to 1,000 client Merchant Center accounts under a single agency account.</p>



<p class="wp-block-paragraph">The newly documented limit gives agencies a clearer understanding of how many client accounts they can manage from one place.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening:</strong> Google added a new detail to its Merchant Center help documentation stating that Agency Admins can link up to 1,000 client Merchant Center accounts.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="958" height="397" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Merchant-Center-For-Agencies-1.png" alt="" class="wp-image-485020" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Merchant-Center-For-Agencies-1.png 958w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Merchant-Center-For-Agencies-1-768x318.png 768w" sizes="(max-width: 958px) 100vw, 958px" /></figure>



<p class="wp-block-paragraph">The limit wasn&#8217;t previously documented, leaving uncertainty around the maximum number of accounts supported.</p>



<p class="wp-block-paragraph"><strong>Why we care:</strong> While <a href="https://support.google.com/merchants/answer/17101837?hl=en#Manage_client_account_connections">the change</a> doesn&#8217;t introduce new functionality, it provides important guidance for agencies managing large merchant portfolios. Knowing the account limit can help agencies plan account structures, onboarding processes and long-term scalability.</p>



<p class="wp-block-paragraph"><strong>Dig deeper. </strong>



<ul class="wp-block-list">
<li><a href="https://searchengineland.com/google-launches-dedicated-merchant-center-hub-for-agencies-471352">Google&nbsp;launches dedicated&nbsp;Merchant Center&nbsp;hub for&nbsp;agencies</a></li>



<li><a href="https://searchengineland.com/google-rolls-out-merchant-center-for-agencies-globally-477055">Google&nbsp;rolls out&nbsp;Merchant Center for Agencies&nbsp;globally</a></li>
</ul>


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



Google has updated its Merchant Center documentation to clarify that Merchant Center for Agencies can now link up to 1,000 client Merchant Center accounts under a single agency account.
The newly documented limit gives agencies a clearer understanding of how many client accounts they can manage from one place.
What’s happening: Google added a new detail to its Merchant Center help documentation stating that Agency Admins can link up to 1,000 client Merchant Center accounts.




The limit wasn’t previously documented, leaving uncertainty around the maximum number of accounts supported.
Why we care: While the change doesn’t introduce new functionality, it provides important guidance for agencies managing large merchant portfolios. Knowing the account limit can help agencies plan account structures, onboarding processes and long-term scalability.
Dig deeper. 




Google launches dedicated Merchant Center hub for agencies
Google rolls out Merchant Center for Agencies globally
a customer you lose.
      
See who’s stealing your traffic

Google has clarified that Merchant Center for Agencies accounts can now link up to 1,000 client Merchant Center accounts

Google Merchant Center for Agencies now supports up to 1,000 linked accounts

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Analytics now supports custom conversion windows for click-through and engaged-view attribution.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Analytics-adds-custom-conversion-attribution-windows-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google Analytics now gives advertisers more flexibility over how conversions are attributed, allowing them to set custom lookback windows that better reflect their sales cycles.</p>



<p class="wp-block-paragraph">The update removes several preset limitations for both click-through and engaged-view conversions.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening</strong>. Advertisers can now configure custom integer lookback windows for two key conversion types:</p>



<ul class="wp-block-list">
<li><strong>Engaged-view conversions (EVC):</strong> Any value from 1 to 30 days, replacing the previous fixed 3-day window.</li>



<li><strong>Click-through conversions (CTC):</strong> Any value from 1 to 90 days, replacing the previous preset options of 1, 7, 14, 30, 60 or 90 days.</li>
</ul>



<p class="wp-block-paragraph">The new settings are available in Google Analytics under Advertising &gt; Conversion management &gt; Settings, as well as in the linked Google Ads conversion management interface.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Attribution windows play a key role in how conversions are credited to advertising campaigns. By allowing advertisers to choose any integer value instead of a handful of preset options, Google is making it easier to align attribution with longer or shorter buying cycles, potentially leading to more accurate performance measurement.</p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. <a href="https://support.google.com/analytics/answer/9164320?hl=en#08112026">Google&#8217;s latest update</a> gives advertisers finer control over conversion attribution by allowing custom click-through and engaged-view lookback windows in both Google Analytics and Google Ads.</p>


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



Google Analytics now gives advertisers more flexibility over how conversions are attributed, allowing them to set custom lookback windows that better reflect their sales cycles.
The update removes several preset limitations for both click-through and engaged-view conversions.
What’s happening. Advertisers can now configure custom integer lookback windows for two key conversion types:
Engaged-view conversions (EVC): Any value from 1 to 30 days, replacing the previous fixed 3-day window.
Click-through conversions (CTC): Any value from 1 to 90 days, replacing the previous preset options of 1, 7, 14, 30, 60 or 90 days.
The new settings are available in Google Analytics under Advertising > Conversion management > Settings, as well as in the linked Google Ads conversion management interface.
Why we care. Attribution windows play a key role in how conversions are credited to advertising campaigns. By allowing advertisers to choose any integer value instead of a handful of preset options, Google is making it easier to align attribution with longer or shorter buying cycles, potentially leading to more accurate performance measurement.
Bottom line. Google’s latest update gives advertisers finer control over conversion attribution by allowing custom click-through and engaged-view lookback windows in both Google Analytics and Google Ads.
a customer you lose.
      
See who’s stealing your traffic

Google Analytics now supports custom conversion windows for click-through and engaged-view attribution.

Google Analytics adds custom conversion attribution windows

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google will begin automatically migrating legacy Search campaign features to AI Max in September, with DSAs following next year.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-sets-AI-Max-migration-timeline-for-Search-campaigns-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google outlined when some legacy campaign features will be automatically migrated to AI Max, giving advertisers and developers a clearer timeline for retiring older campaign structures.</p>



<p class="wp-block-paragraph">The transition begins in September and continues into early 2027, with <a href="https://searchengineland.com/google-to-auto-upgrade-some-search-campaigns-to-ai-max-484428">Campaign-level Broad Match, Automatically Created Assets (ACA)</a> and <a href="https://searchengineland.com/google-delays-dynamic-search-ads-migration-to-ai-max-480049">Dynamic Search Ads (DSAs)</a> all moving to AI Max.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening</strong>. Beginning September 1, Google will automatically upgrade Search campaigns using Campaign-level Broad Match or standalone Automatically Created Assets to AI Max. The migration will roll out gradually throughout the month.</p>



<p class="wp-block-paragraph">Google says campaigns will be migrated in place using equivalent AI Max settings to minimize disruption. Existing brand inclusions and exclusions will also carry over automatically.</p>



<p class="wp-block-paragraph"><strong>Legacy features reach end of life.</strong> Advertisers can no longer create new Campaign-level Broad Match configurations or legacy ACA campaigns.</p>



<p class="wp-block-paragraph">Google blocked the creation of these legacy structures across the Google Ads interface, Ads Editor and API on August 3, signaling the company&#8217;s continued push toward AI Max as the default Search campaign experience.</p>



<p class="wp-block-paragraph"><strong>Developers face API changes</strong>. The migration also affects developers using the Google Ads API.</p>



<p class="wp-block-paragraph">Future API versions released after September 1 will remove support for legacy Campaign-level Broad Match and ACA entities altogether. Older API versions will continue supporting them until their normal sunset period—expected around September 2027—giving developers additional time to update integrations.</p>



<p class="wp-block-paragraph">Google recommends auditing scripts and tools now to ensure they&#8217;re using AI Max settings rather than deprecated campaign structures.</p>



<p class="wp-block-paragraph"><strong>Dynamic Search Ads get more time</strong>. Unlike Broad Match and ACA, Dynamic Search Ads won&#8217;t migrate until February 2027.</p>



<p class="wp-block-paragraph">Google plans to begin warning advertisers about the upcoming transition in September through in-account notifications, followed by additional reminders in January before automatic upgrades begin next February.</p>



<p class="wp-block-paragraph"><strong>Key dates</strong>.</p>



<p class="wp-block-paragraph"><strong>August 3, 2026</strong>: Google stops allowing the creation of new Campaign-level Broad Match and legacy Automatically Created Assets across the Google Ads UI, Ads Editor and API.<br><strong>September 1–30, 2026</strong>: Automatic migration of Campaign-level Broad Match and ACA campaigns to AI Max takes place.<br><strong>September 2026</strong>: Google begins displaying in-account notices encouraging advertisers to voluntarily upgrade Dynamic Search Ads to AI Max.<br><strong>January 15, 2027</strong>: Google sends reminder notifications to advertisers with legacy Dynamic Search Ads.<br><strong>February 1–28, 2027</strong>: Automatic migration of Dynamic Search Ads begins, and the ability to create new DSA ad groups is permanently removed.<br><strong>September 2027</strong> (approximately): Older Google Ads API versions supporting legacy Broad Match and ACA entities are expected to reach their scheduled sunset, permanently removing support.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. This is another major step in Google&#8217;s shift toward AI-driven Search campaigns. Advertisers still using Campaign-level Broad Match, Automatically Created Assets or Dynamic Search Ads should review their accounts before automatic migrations begin, while developers will need to update API integrations to avoid compatibility issues as legacy features are phased out.</p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. <a href="https://ads-developers.googleblog.com/2026/08/migrate-campaign-level-broad-match-and.html">Google&#8217;s AI Max migration is accelerating</a>, with automatic upgrades beginning in September and legacy Search campaign features steadily disappearing over the next year.</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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      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
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       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
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      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
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Google outlined when some legacy campaign features will be automatically migrated to AI Max, giving advertisers and developers a clearer timeline for retiring older campaign structures.
The transition begins in September and continues into early 2027, with Campaign-level Broad Match, Automatically Created Assets (ACA) and Dynamic Search Ads (DSAs) all moving to AI Max.
What’s happening. Beginning September 1, Google will automatically upgrade Search campaigns using Campaign-level Broad Match or standalone Automatically Created Assets to AI Max. The migration will roll out gradually throughout the month.
Google says campaigns will be migrated in place using equivalent AI Max settings to minimize disruption. Existing brand inclusions and exclusions will also carry over automatically.
Legacy features reach end of life. Advertisers can no longer create new Campaign-level Broad Match configurations or legacy ACA campaigns.
Google blocked the creation of these legacy structures across the Google Ads interface, Ads Editor and API on August 3, signaling the company’s continued push toward AI Max as the default Search campaign experience.
Developers face API changes. The migration also affects developers using the Google Ads API.
Future API versions released after September 1 will remove support for legacy Campaign-level Broad Match and ACA entities altogether. Older API versions will continue supporting them until their normal sunset period—expected around September 2027—giving developers additional time to update integrations.
Google recommends auditing scripts and tools now to ensure they’re using AI Max settings rather than deprecated campaign structures.
Dynamic Search Ads get more time. Unlike Broad Match and ACA, Dynamic Search Ads won’t migrate until February 2027.
Google plans to begin warning advertisers about the upcoming transition in September through in-account notifications, followed by additional reminders in January before automatic upgrades begin next February.
Key dates.
August 3, 2026: Google stops allowing the creation of new Campaign-level Broad Match and legacy Automatically Created Assets across the Google Ads UI, Ads Editor and API.
September 1–30, 2026: Automatic migration of Campaign-level Broad Match and ACA campaigns to AI Max takes place.
September 2026: Google begins displaying in-account notices encouraging advertisers to voluntarily upgrade Dynamic Search Ads to AI Max.
January 15, 2027: Google sends reminder notifications to advertisers with legacy Dynamic Search Ads.
February 1–28, 2027: Automatic migration of Dynamic Search Ads begins, and the ability to create new DSA ad groups is permanently removed.
September 2027 (approximately): Older Google Ads API versions supporting legacy Broad Match and ACA entities are expected to reach their scheduled sunset, permanently removing support.
Why we care. This is another major step in Google’s shift toward AI-driven Search campaigns. Advertisers still using Campaign-level Broad Match, Automatically Created Assets or Dynamic Search Ads should review their accounts before automatic migrations begin, while developers will need to update API integrations to avoid compatibility issues as legacy features are phased out.
Bottom line. Google’s AI Max migration is accelerating, with automatic upgrades beginning in September and legacy Search campaign features steadily disappearing over the next year.
customers won’t either.
      
See your AI visibility

Google will begin automatically migrating legacy Search campaign features to AI Max in September, with DSAs following next year.

Google sets AI Max migration timeline for Search campaigns

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it.png" class="attachment-large size-large wp-post-image" alt="LLM traffic converts differently — here’s what to do about it" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Paid search captures demand at the query. AI shapes the decision before the visitor reaches your site. Here’s how to convert that traffic.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it.png" class="attachment-large size-large wp-post-image" alt="LLM traffic converts differently — here’s what to do about it" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/LLM-traffic-converts-differently-—-heres-what-to-do-about-it-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph"><a href="https://searchengineland.com/guide/what-is-paid-search">Paid search</a> has long been the workhorse of performance marketing because you can connect a click to a conversion. But AI-driven search is creating a different kind of referral traffic — one that arrives after an AI system has already helped shape the user&#8217;s decision.</p>



<p class="wp-block-paragraph">Our data shows LLM referral traffic converts at 20%, making it the highest-converting tactic in our dataset and 61% higher than paid search. The challenge is figuring out how to convert that traffic when the journey that led to the click looks nothing like a traditional search.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="262" height="483" src="https://searchengineland.com/wp-content/seloads/2026/08/LLM-referral-traffic-internal-data.png" alt="LLM referral traffic" class="wp-image-484968"/></figure>
</div>


<h2 id="h-ai-search-is-changing-the-journey-before-the-click" class="wp-block-heading">AI search is changing the journey before the click</h2>



<p class="wp-block-paragraph">The growth of LLMs broadly and Google AI Mode is already changing consumer behavior. </p>



<p class="wp-block-paragraph">Google has reported that the average search query in AI Mode is <a href="https://blog.google/products-and-platforms/products/search/ai-mode-us-insights/" target="_blank" rel="noopener">three times longer than a traditional search query</a>. One in six AI Mode searches is also non-textual, using voice or image-based inputs.</p>



<p class="wp-block-paragraph">That additional detail and context change the game for brands. </p>



<p class="wp-block-paragraph">How do you treat a user who comes from an LLM-based search after entering only an image? Will your content even be surfaced in that scenario?</p>



<p class="wp-block-paragraph">As a result, a new kind of traffic is emerging: LLM referral traffic. </p>



<p class="wp-block-paragraph">But how does this traffic behave? Is converting a user who clicked a citation in an AI response different from converting someone who clicked a traditional Google Ads search result?</p>



<p class="wp-block-paragraph">Yes. The journey that leads to the click is fundamentally different, which means the traditional marketing funnel needs to change.</p>


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




<h2 id="h-why-llm-traffic-behaves-differently-from-ppc" class="wp-block-heading">Why LLM traffic behaves differently from PPC</h2>



<p class="wp-block-paragraph">In PPC, you target <strong>intent</strong> based on an isolated search query. A search for &#8220;best CRM for small business&#8221; triggers a specific ad group. The user is presented with several options and is expected to click through to evaluate them.</p>



<p class="wp-block-paragraph">LLM users operate on <strong>context</strong>. They might prompt an AI with: &#8220;I run a 50-person consulting firm using Google Workspace. What CRM integrates best with it, costs less than $50 a user, and has strong email automation?&#8221;</p>



<p class="wp-block-paragraph">By the time the LLM user clicks a link, the AI has already synthesized the options. The user isn&#8217;t looking for a generic landing page comparing 10 CRMs. They want validation of the AI&#8217;s specific recommendation. The LLM has done the top-of-funnel heavy lifting.</p>



<p class="wp-block-paragraph">PPC relies on the click. If a user doesn&#8217;t visit your landing page, you can&#8217;t convert them.</p>



<p class="wp-block-paragraph">LLMs, by design, provide the answer directly within the chat interface. This exacerbates the zero-click search phenomenon. When an LLM provides a clickable citation, it is usually because the user requires deeper evidence, a primary source, or is ready to take a transactional step that the AI cannot complete.</p>



<p class="wp-block-paragraph">That means an LLM referral can represent a much higher-intent action than a top-of-funnel PPC click, although the volume is significantly lower.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/new-search-journey-seo-ppc-484630">The new search journey changes how SEO and PPC should work</a></em></strong>



<h2 id="h-llm-referrals-require-a-different-landing-page-experience" class="wp-block-heading">LLM referrals require a different landing page experience</h2>



<p class="wp-block-paragraph">There’s also a difference in how users perceive the two experiences. When a user clicks a PPC ad, they know it&#8217;s a paid placement, which creates a built-in level of skepticism.</p>



<p class="wp-block-paragraph">When an AI model cites your website as a source, the user may perceive it as an objective recommendation.</p>



<p class="wp-block-paragraph">The AI has evaluated information across the web and selected your content as a source for its answer. That can give LLM traffic a higher baseline of trust, provided your content aligns with what the AI told the user.</p>



<p class="wp-block-paragraph">If you treat an LLM-driven visitor like a PPC visitor, you risk losing them.</p>



<p class="wp-block-paragraph"><strong>The PPC landing page</strong>



<ul class="wp-block-list">
<li><strong>Focus:</strong> Immediate action, such as a form fill, purchase or call.</li>



<li><strong>Design:</strong> Stripped down, minimal navigation and an aggressive call to action.</li>



<li><strong>Content:</strong> Benefit-driven copy, bullet points and messaging highly tailored to the specific search keyword.</li>
</ul>



<p class="wp-block-paragraph"><strong>The ideal LLM landing page</strong>



<ul class="wp-block-list">
<li><strong>Focus:</strong> Depth, verification and the transition from an AI summary to human expertise.</li>



<li><strong>Design:</strong> Navigable, rich in resources, with clear pathways to deeper information.</li>



<li><strong>Content:</strong> Comprehensive and authoritative, showing why the AI cited you.</li>
</ul>



<p class="wp-block-paragraph">When an LLM user lands on your site, they may be fact-checking the AI or looking for the next step. If they&#8217;re greeted by a high-pressure, gated form without the nuanced information the AI promised, they may bounce immediately.</p>



<p class="wp-block-paragraph">Adapting to this shift requires a move from traditional SEO and PPC toward GEO and conversion strategies built specifically for LLM-driven traffic.</p>



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



<h2 id="h-what-it-takes-to-convert-llm-referral-traffic" class="wp-block-heading">What it takes to convert LLM referral traffic</h2>



<h3 id="h-1-optimize-for-information-gain-to-secure-the-citation" class="wp-block-heading">1. Optimize for information gain to secure the citation</h3>



<p class="wp-block-paragraph">You can&#8217;t convert LLM traffic if you don&#8217;t get the citation in the first place. AI models prioritize content that offers <a href="https://searchengineland.com/what-information-gain-seo-means-440326">information gain</a> — unique data, proprietary research, primary sources, and expert opinions that can&#8217;t be found elsewhere.</p>



<p class="wp-block-paragraph"><strong>Action items</strong>



<ul class="wp-block-list">
<li>Audit your content. Are you just regurgitating what&#8217;s already on Page 1 of Google? If so, an LLM has no reason to cite you. </li>



<li>Inject original data, quotes from subject matter experts (SMEs), and unique frameworks into your core pages.</li>
</ul>



<h3 id="h-2-review-citations-for-results-and-wrap-your-brand-around-those-sites" class="wp-block-heading">2. Review citations for results and wrap your brand around those sites</h3>



<p class="wp-block-paragraph">If you can&#8217;t influence the actual results of the LLM prompt, you can review which websites are cited and where traffic is directed.&nbsp;</p>



<p class="wp-block-paragraph">At these sites, you can buy display ads through a contextual strategy or pre-roll on YouTube. YouTube is one of the most commonly cited websites. YouTube shows up in 16% of results, according to a report by Bluefish.</p>



<p class="wp-block-paragraph"><strong>Action items</strong>&nbsp;</p>



<ul class="wp-block-list">
<li>Review all citations and determine which common sites or blog posts show up in the results.&nbsp;</li>



<li>Leverage other media buying channels to influence those results with a contextual ad campaign or other digital media buy.</li>
</ul>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/ai-visibility-starts-before-search-ends-with-citations-476308">Why AI visibility starts before search and ends with citations</a></em></strong>



<h3 id="h-3-capture-the-long-tail-conversational-lead" class="wp-block-heading">3. Capture the long-tail conversational lead</h3>



<p class="wp-block-paragraph">LLM users are incredibly specific. They have unique edge cases.</p>



<p class="wp-block-paragraph"><strong>Action items</strong>



<ul class="wp-block-list">
<li>Build dynamic conversion paths. Instead of a static lead form asking for name, email, and company, consider interactive elements.&nbsp;</li>



<li>Use self-serve qualification tools, calculators, or even your own on-site AI chatbot that can pick up the conversation where the external LLM left off.</li>
</ul>



<h3 id="h-4-rethink-attribution-and-measurement" class="wp-block-heading">4. Rethink attribution and measurement</h3>



<p class="wp-block-paragraph">This is the hardest pill to swallow: tracking LLM traffic is a mess. Much of ChatGPT traffic shows up in analytics as &#8220;Direct&#8221; or &#8220;Referral&#8221; without granular query data.</p>



<p class="wp-block-paragraph"><strong>Action items</strong>



<ul class="wp-block-list">
<li>Move away from relying solely on last-click attribution.&nbsp;</li>



<li>Implement &#8220;How did you hear about us?&#8221; fields on your highest-value forms, specifically including options like &#8220;ChatGPT,&#8221; &#8220;AI Search,&#8221; or &#8220;Perplexity.&#8221;&nbsp;</li>



<li>Monitor overall brand lift and direct traffic correlations alongside major AI feature rollouts.</li>
</ul>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/ai-search-performance-measurment-framework-484546">A 5-layer framework for measuring AI search performance</a></em></strong>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0102ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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        Every click they win is <span>a customer you lose</span>.
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        See where competitors are investing, which keywords drive their results, and how to capture more of the market.
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<h2 id="h-llm-traffic-requires-a-different-conversion-strategy" class="wp-block-heading">LLM traffic requires a different conversion strategy</h2>



<p class="wp-block-paragraph">PPC is a game of capturing existing, standardized demand through targeted spending and aggressive conversion funnels. Converting LLM traffic is a game of earning authority, providing profound context, and facilitating a seamless transition from artificial synthesis to human expertise.</p>



<p class="wp-block-paragraph">Traffic volumes from LLMs may not rival traditional Google Search or PPC campaigns today, but the intent and trust level of an AI-referred user can be unusually high.&nbsp;</p>



<p class="wp-block-paragraph">By shifting your focus from aggressive, keyword-matched landing pages to authoritative, context-rich experiences, you can turn this emerging channel into your most powerful engine for high-quality conversions.</p>

Paid search has long been the workhorse of performance marketing because you can connect a click to a conversion. But AI-driven search is creating a different kind of referral traffic — one that arrives after an AI system has already helped shape the user’s decision.
Our data shows LLM referral traffic converts at 20%, making it the highest-converting tactic in our dataset and 61% higher than paid search. The challenge is figuring out how to convert that traffic when the journey that led to the click looks nothing like a traditional search.




AI search is changing the journey before the click
The growth of LLMs broadly and Google AI Mode is already changing consumer behavior. 
Google has reported that the average search query in AI Mode is three times longer than a traditional search query. One in six AI Mode searches is also non-textual, using voice or image-based inputs.
That additional detail and context change the game for brands. 
How do you treat a user who comes from an LLM-based search after entering only an image? Will your content even be surfaced in that scenario?
As a result, a new kind of traffic is emerging: LLM referral traffic. 
But how does this traffic behave? Is converting a user who clicked a citation in an AI response different from converting someone who clicked a traditional Google Ads search result?
Yes. The journey that leads to the click is fundamentally different, which means the traditional marketing funnel needs to change.
your competitors win.
      
Analyze your competitors
    
Why LLM traffic behaves differently from PPC
In PPC, you target intent based on an isolated search query. A search for “best CRM for small business” triggers a specific ad group. The user is presented with several options and is expected to click through to evaluate them.
LLM users operate on context. They might prompt an AI with: “I run a 50-person consulting firm using Google Workspace. What CRM integrates best with it, costs less than $50 a user, and has strong email automation?”
By the time the LLM user clicks a link, the AI has already synthesized the options. The user isn’t looking for a generic landing page comparing 10 CRMs. They want validation of the AI’s specific recommendation. The LLM has done the top-of-funnel heavy lifting.
PPC relies on the click. If a user doesn’t visit your landing page, you can’t convert them.
LLMs, by design, provide the answer directly within the chat interface. This exacerbates the zero-click search phenomenon. When an LLM provides a clickable citation, it is usually because the user requires deeper evidence, a primary source, or is ready to take a transactional step that the AI cannot complete.
That means an LLM referral can represent a much higher-intent action than a top-of-funnel PPC click, although the volume is significantly lower.
Dig deeper: The new search journey changes how SEO and PPC should work



LLM referrals require a different landing page experience
There’s also a difference in how users perceive the two experiences. When a user clicks a PPC ad, they know it’s a paid placement, which creates a built-in level of skepticism.
When an AI model cites your website as a source, the user may perceive it as an objective recommendation.
The AI has evaluated information across the web and selected your content as a source for its answer. That can give LLM traffic a higher baseline of trust, provided your content aligns with what the AI told the user.
If you treat an LLM-driven visitor like a PPC visitor, you risk losing them.
The PPC landing page




Focus: Immediate action, such as a form fill, purchase or call.
Design: Stripped down, minimal navigation and an aggressive call to action.
Content: Benefit-driven copy, bullet points and messaging highly tailored to the specific search keyword.
The ideal LLM landing page




Focus: Depth, verification and the transition from an AI summary to human expertise.
Design: Navigable, rich in resources, with clear pathways to deeper information.
Content: Comprehensive and authoritative, showing why the AI cited you.
When an LLM user lands on your site, they may be fact-checking the AI or looking for the next step. If they’re greeted by a high-pressure, gated form without the nuanced information the AI promised, they may bounce immediately.
Adapting to this shift requires a move from traditional SEO and PPC toward GEO and conversion strategies built specifically for LLM-driven traffic.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
What it takes to convert LLM referral traffic
1. Optimize for information gain to secure the citation
You can’t convert LLM traffic if you don’t get the citation in the first place. AI models prioritize content that offers information gain — unique data, proprietary research, primary sources, and expert opinions that can’t be found elsewhere.
Action items




Audit your content. Are you just regurgitating what’s already on Page 1 of Google? If so, an LLM has no reason to cite you. 
Inject original data, quotes from subject matter experts (SMEs), and unique frameworks into your core pages.
2. Review citations for results and wrap your brand around those sites
If you can’t influence the actual results of the LLM prompt, you can review which websites are cited and where traffic is directed. 
At these sites, you can buy display ads through a contextual strategy or pre-roll on YouTube. YouTube is one of the most commonly cited websites. YouTube shows up in 16% of results, according to a report by Bluefish.
Action items 
Review all citations and determine which common sites or blog posts show up in the results. 
Leverage other media buying channels to influence those results with a contextual ad campaign or other digital media buy.
Dig deeper: Why AI visibility starts before search and ends with citations



3. Capture the long-tail conversational lead
LLM users are incredibly specific. They have unique edge cases.
Action items




Build dynamic conversion paths. Instead of a static lead form asking for name, email, and company, consider interactive elements. 
Use self-serve qualification tools, calculators, or even your own on-site AI chatbot that can pick up the conversation where the external LLM left off.
4. Rethink attribution and measurement
This is the hardest pill to swallow: tracking LLM traffic is a mess. Much of ChatGPT traffic shows up in analytics as “Direct” or “Referral” without granular query data.
Action items




Move away from relying solely on last-click attribution. 
Implement “How did you hear about us?” fields on your highest-value forms, specifically including options like “ChatGPT,” “AI Search,” or “Perplexity.” 
Monitor overall brand lift and direct traffic correlations alongside major AI feature rollouts.
Dig deeper: A 5-layer framework for measuring AI search performance



  
a customer you lose.
      
See who’s stealing your traffic
    
LLM traffic requires a different conversion strategy
PPC is a game of capturing existing, standardized demand through targeted spending and aggressive conversion funnels. Converting LLM traffic is a game of earning authority, providing profound context, and facilitating a seamless transition from artificial synthesis to human expertise.
Traffic volumes from LLMs may not rival traditional Google Search or PPC campaigns today, but the intent and trust level of an AI-referred user can be unusually high. 
By shifting your focus from aggressive, keyword-matched landing pages to authoritative, context-rich experiences, you can turn this emerging channel into your most powerful engine for high-quality conversions.

Paid search captures demand at the query. AI shapes the decision before the visitor reaches your site. Here’s how to convert that traffic.

LLM traffic converts differently — here’s what to do about it

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Search marketing jobs" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-550x310.jpg 550w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

Looking to take the next step in your search marketing career? 
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
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Sr. Global SEO/GEO Strategist
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Colgate is hiring: SEO/GEO Specialist in Overland Park
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Senior SEO & AI Search Strategist (GEO/AEO)
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Company: VirayoSalary: 120,000-140,000 USDLocation: Remote USDate posted: August 11, 2026We are seeking a senior-level AI Search Strategist (Generative/Answer Engine Optimization) to lead the development and execution of strategies to achieve visibility within AI Overviews (AIOs) and Large Language Models (LLMs), such as Gemini, ChatGPT, and Claude. This role is both strategic and client-facing. You will serve as the primary point […]

SEO & GEO Director
August 11, 2026 
Company: Search Marketing ProsSalary: $120,000 per year USDLocation: Remote, United StatesDate posted: August 11, 2026The Role We are hiring an SEO & GEO Director to own strategy, client outcomes, and team leadership across our full SEO book of business. This is not a delivery role – this is the person who sets the direction, holds the team accountable, and owns […]

SEO/GEO Campaign Strategist
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Company: Signal Hill StrategiesSalary: $85,000-$90,000 per year USDLocation: Remote, United StatesDate posted: August 11, 2026About the role At Signal Hill Strategies, our Campaign Strategists are the architects behind every client campaign. You will own the strategic direction of multiple accounts, building campaigns rooted in a deep understanding of each client’s industry, audience, and competitive edge, and translating that understanding into […]

Junior SEO Specialist
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Company: Honest DigitalSalary: $50,000 to $60,000 (USD)Location: Remote USDate posted: August 10, 2026If you’re looking for a place where you can sharpen your SEO skills, push your expertise to the next level, and work alongside a team that genuinely loves testing, learning, and winning, we’d love to meet you. Honest Digital is one of the fastest-growing digital marketing agencies in […]

Senior SEO Strategist
August 8, 2026 
Company: Proven Marketing NowLocation: Remote WorldwideDate posted: August 8, 2026About PMNow PMNow (Proven Marketing Now) is a 20-person digital marketing agency. Since 2020, we’ve done one thing: get the phone ringing for home services businesses (plumbers, HVAC contractors, and electricians) across the United States. Our main services include Local SEO, website development, Google Ads, and social media management. We’re not […]

Senior Manager, Global SEO & AEO
August 8, 2026 
Company: Stanley Black & Decker, Inc.Salary: $95,600.00 – $153,900.00 per year USDLocation: Remote, United StatesDate posted: August 8, 2026Senior Manager, Global SEO & AEO- REMOTE, US Come build your career. It takes great people to achieve greatness. People with a sense of purpose and integrity. People with a relentless pursuit of excellence. People who care about making things better For […]

Newest PPC and paid media jobs
(Provided to Search Engine Land by PPCjobs.com) 







Paid Search Manager
August 16, 2026 
Company: SKAI BrasilSalary: $85,000-$95,000Location: Brooklyn, NY, United StatesDate posted: August 16, 2026 Description Ready to take your career to the next level? Skai is looking for the best and the brightest to join our family. We're proud of our industry-leading digital marketing software but we're even prouder of the people behind it. That's where you come in! The Expert Services […]

Growth Marketing Manager
August 16, 2026 
Company: BridgeSalary: $100,000-$125,000Location: United States (residency required)Date posted: August 16, 2026 The Growth Marketing Manager owns how Bridge turns demand into pipeline. Reporting to the VP Marketing, this role owns our growth and demand generation strategy and its execution: multi-channel and ABM campaigns, lead generation, the marketing automation and CRM engine behind them, and the reporting that ties activity to […]

Head of Growth Marketing
August 16, 2026 
Company: Our clientSalary: $150K-$225KLocation: United States (residency required)Date posted: August 16, 2026Description Our client is a category-defining Series B agentic AI company automating supply chain spend management for some of the world’s largest enterprises. Its autonomous agents execute procurement, invoicing, and payment workflows rather than simply recommending actions. The company has raised $75M, reached $15M ARR last year, and is […]

Growth Marketing Engineer
August 15, 2026 
Company: Raspberry AISalary: $140,000 – $180,000Location: United States (residency required)Date posted: August 15, 2026 Growth Marketing Engineer Remote (NY Preferred) · Full-Time · Marketing About Raspberry AI We are a Series-A (backed by A16Z), AI platform built for fashion brands and retailers. We are the connective tissue between design, product development, merchandising, marketing, ecommerce and wholesale teams. We help them […]

Manager Lifecycle Digital Growth Marketing
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