{"id":11156,"date":"2026-07-07T19:05:34","date_gmt":"2026-07-07T11:05:34","guid":{"rendered":"http:\/\/longzhuplatform.com\/?p=11156"},"modified":"2026-07-07T19:05:34","modified_gmt":"2026-07-07T11:05:34","slug":"62-of-ai-brand-recommendations-vanish-after-one-buyer-question-new-clovion-data-via-sejournal-gregjarboe","status":"publish","type":"post","link":"http:\/\/longzhuplatform.com\/?p=11156","title":{"rendered":"62% Of AI Brand Recommendations Vanish After One Buyer Question \u2013 New Clovion Data via @sejournal, @gregjarboe"},"content":{"rendered":"<p><\/p> <div id=\"narrow-cont\"> <p>Zahir Hasan didn\u2019t have to tell me his company\u2019s numbers were wrong.<\/p> <p>I\u2019d sent Hasan, COO of the Oslo-based research firm Clovion AI, a list of methodology questions about \u201cSurviving the AI Funnel,\u201d Clovion\u2019s new study of how Claude, ChatGPT, and Gemini recommend brands across a conversation. Question ten was routine, the kind of thing you ask every research team. The report says the three AI assistants flatly contradict each other on brand facts 15% of the time, based on 33 verified contradictions. Was 33 really enough to support a claim about which model tends to undersell a brand\u2019s features and which tends to oversell them?<\/p> <p>Hasan\u2019s answer wasn\u2019t a defense of the number. It was a correction. \u201cThe real number is 330,\u201d he wrote back. \u201cA designer dropped a zero in layout.\u201d The same slipped decimal, he said, had also turned 2,040 brands into \u201c204\u201d on page seven of the PDF that I\u2019d been sent in advance of its publication. A revised version is coming out this week. So, I got the corrected figures first.<\/p> <p><iframe class=\"sej-iframe-auto-height\" id=\"in-content-iframe\" scrolling=\"no\" src=\"https:\/\/www.searchenginejournal.com\/wp-json\/sscats\/v2\/tk\/Middle_Post_Text\"><\/iframe><\/p> <p>That\u2019s a strange way to start a column about an AI research report, admitting before anything else that the draft report had an error in it. But it\u2019s the most honest way in, because the correction says something the study\u2019s headline stats never could. Reading AI answers correctly, whether you\u2019re a marketer trying to figure out if ChatGPT is recommending your product or a researcher building a study about it, comes down to catching the decimal point before you build a strategy on it.<\/p> <h2>The Funnel, Recapped<\/h2> <p>Set the typo aside for a moment and the underlying research holds up. Clovion ran 69,120 multi-turn conversations across the three assistants in 36 B2B software and fintech categories, asking an opening question like \u201cbest CRM tools?\u201d and then a single realistic follow-up. Re-asking the same question kept 90% of the recommended list intact. Adding one ordinary buyer detail, something as plain as \u201cfor a small team,\u201d kept only 28%. Sixty-two percent of the brands that made the first answer were gone by the second one.<\/p> <p>I asked Hasan whether \u201csmall team\u201d was cherry-picked to produce that drop. It wasn\u2019t. His team also tested \u201cfor a large enterprise\u201d and got almost identical churn, around 72% either way, against roughly 10% when the question was simply repeated. The list isn\u2019t unstable. It\u2019s responsive, and mostly to whether the model has decided who a brand is actually for.<\/p> <p>That\u2019s the part worth sitting with if you do SEO or brand strategy for a living. Being named in an AI answer is not the same thing as being trusted by it. A model that puts you in its first CRM list can still cut you the moment a buyer gets specific, and Clovion\u2019s data says that happens most of the time, not some of the time.<\/p> <h2>The Correction Changes the Shape of the Smallest, Most-Cited Number<\/h2> <p>Here\u2019s where the fixed decimal actually matters for how you should read this study. The old figure, 33 verified contradictions, was small enough that any per-model claim built on it was standing on thin ice. Corrected, it\u2019s 330, and the per-model breakdown Hasan shared is far more telling than the aggregate 15% figure the draft report leads with: Claude underclaims a brand\u2019s own features 160 times against 10 overclaims. ChatGPT underclaims 70 times and never overclaims. Gemini runs the other way, overclaiming 80 times against 30 underclaims.<\/p> <p>Hasan\u2019s working theory, drawn from a separate, not-yet-published Clovion study on where each model sources its answers, is that Gemini leans more heavily on marketing material and video, so it tends to credit a brand with whatever it\u2019s hyping. Claude and ChatGPT lean more on documentation and product pages, describe the core product accurately, and hedge toward \u201cdoesn\u2019t have it\u201d when a newer feature isn\u2019t well documented. If that holds up under the study Clovion hasn\u2019t released yet, it means the direction of an AI assistant\u2019s error about your product is a function of what kind of content you\u2019ve put in front of it, and where that content lives.<\/p> <p>I\u2019ve spent more than 20 years telling clients that ranking well and being described accurately are two different problems. This is the clearest evidence I\u2019ve seen that they\u2019re now the same problem, playing out inside a single conversation, and that the fix depends on which assistant is doing the misdescribing.<\/p> <h2>Why Nobody Catches the Missing Zero<\/h2> <p>Frederick Vallaeys has a story in his book \u201cThe AI-Amplified Marketer\u201d that explains exactly why a dropped decimal survives all the way to publication. An automated report once flagged \u201cgreat performance\u201d on a keyword because its cost per acquisition was running much higher than the target. Somewhere in the system, high had gotten swapped for good, when a high CPA is bad news, not good news. Anyone skimming the summary would have nodded along, because the sentence read smoothly even though its meaning had flipped.<\/p> <p>Vallaeys ties this to research on predictive processing, the idea that fluent readers aren\u2019t decoding every word, they\u2019re predicting what comes next based on context and moving on. That\u2019s how \u201cteh\u201d reads as \u201cthe\u201d and a missing \u201cnot\u201d slides right past you. As Vallaeys puts it, our mental model of the sentence overrules the text in front of us. A confident, well-formatted PDF is the easiest place in the world for that to happen, and a dropped zero in a layout file is a much smaller, much more forgivable version of the same failure.<\/p> <p>It\u2019s also why the fix isn\u2019t \u201ctrust the report less.\u201d It\u2019s \u201ckeep a human pilot in the loop who checks the number instead of the vibe of the paragraph around it.\u201d Thirty-three contradictions and 330 contradictions don\u2019t just differ by a factor of ten. They support entirely different confidence levels about whether a per-model pattern is real. Two hundred four brands and 2,040 brands aren\u2019t the same study. If Clovion hadn\u2019t caught it, and if I hadn\u2019t asked, the smaller, shakier numbers would have kept circulating as fact, cited by exactly the kind of trade press that\u2019s supposed to catch this.<\/p> <h2>What Clovion Isn\u2019t Claiming, and Why That\u2019s the Honest Part<\/h2> <p>The report is careful to say the link between how a model perceives your fit and whether it recommends you is \u201ca strong, consistent coupling, not a proven causal law.\u201d I pushed Hasan on what a real causal test would look like. His answer: change one thing, a brand\u2019s public positioning content, leave everything else alone, and see whether the models\u2019 behavior moves relative to brands nobody touched. Clovion hasn\u2019t run that test yet. He also conceded the more uncomfortable possibility directly, that a brand\u2019s actual real-world positioning is probably driving both how the model describes it and whether it gets recommended, which would make positioning the real lever and the model\u2019s \u201cperception\u201d just a symptom, not a cause.<\/p> <p>That\u2019s an unusually candid answer from a company selling AI visibility monitoring, and it\u2019s exactly why I trust the rest of what Hasan told me. He also had no data on how fast an AI\u2019s perception of a brand shifts after that brand changes its own content. \u201cWe didn\u2019t do a before-and-after test,\u201d he said. \u201cTreat it as worth testing, not guaranteed in X weeks.\u201d Anyone telling you they can promise a specific timeline for moving Claude\u2019s or Gemini\u2019s opinion of your brand is guessing, by Clovion\u2019s own admission.<\/p> <h2>What To Actually Do About It<\/h2> <p>There are three things that you should do, based on what Hasan told me and what the corrected data supports.<\/p> <p>First, track the whole conversation, not the first answer. If you\u2019re monitoring AI visibility with a single-prompt check, you\u2019re measuring the top of a funnel that loses 62% of its contents one sentence later. Build your monitoring around the follow-up questions your real buyers actually ask.<\/p> <p>Second, fix the assistants one at a time, in order. Hasan was direct that a single content change won\u2019t move all three models at once, because they pull from different sources. His suggested order: correct flat factual errors first, since those are cheap wins, then go after the segment-fit combinations that matter most to your pipeline, checking each assistant across several runs rather than trusting any single answer.<\/p> <p>Third, don\u2019t cite a stat you haven\u2019t traced to its source, including this one. Clovion\u2019s own report needed a correction on its most technical, most citable number. Before you build a column, a client deck, or a content brief around any AI research percentage, ask where the underlying count came from and whether anyone\u2019s checked the math since it left the design software.<\/p> <p>I\u2019ve watched SEO go through a few of these moments, from Panda to mobile-first indexing to the slow bleed of zero-click search. Each one rewarded the practitioners who checked the primary source instead of repeating the headline number. AI visibility is shaping up the same way. The brands that win the disappearing act Clovion documented won\u2019t be the ones with the best press release about their AI Overviews strategy. They\u2019ll be the ones who read the report closely enough to ask what a \u201c33\u201d really meant, and who keep asking that question after this one.<\/p> <p><em>Zahir Hasan is COO of Clovion AI, based in Oslo, Norway. Clovion\u2019s corrected version of \u201cSurviving the AI Funnel,\u201d reflecting the figures in this column, is expected this week.<\/em><\/p> <h3>More Resources<\/h3> <\/div> <p>Generative AI,SEO,SEO Strategy#Brand #Recommendations #Vanish #Buyer #Question #Clovion #Data #sejournal #gregjarboe1783422334<\/p> ","protected":false},"excerpt":{"rendered":"<p>Zahir Hasan didn\u2019t have to tell me his company\u2019s numbers were wrong. I\u2019d sent Hasan, COO of the Oslo-based research firm Clovion AI, a list of methodology questions about \u201cSurviving the AI Funnel,\u201d Clovion\u2019s new study of how Claude, ChatGPT, and Gemini recommend brands across a conversation. Question ten was routine, the kind of thing [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":11157,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[405,34627,44508,450,8210,3860,7323,80,40666],"class_list":["post-11156","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-accessibility","tag-brand","tag-buyer","tag-clovion","tag-data","tag-gregjarboe","tag-question","tag-recommendations","tag-sejournal","tag-vanish"],"acf":[],"_links":{"self":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/posts\/11156","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11156"}],"version-history":[{"count":0,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/posts\/11156\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/media\/11157"}],"wp:attachment":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11156"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11156"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}