{"id":732975,"date":"2026-06-12T14:45:23","date_gmt":"2026-06-12T11:45:23","guid":{"rendered":"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/"},"modified":"2026-06-12T14:45:23","modified_gmt":"2026-06-12T11:45:23","slug":"what-co-mentions-reveal-about-the-ai-recommendation-gap","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/","title":{"rendered":"What co-mentions reveal about the AI recommendation gap"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a37aaf62f9d0\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #dd3333;color:#dd3333\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #dd3333;color:#dd3333\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a37aaf62f9d0\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Being_understood_by_AI_isnt_the_same_as_being_recommended_by_it_New_research_reveals_the_gap_between_the_two\" >Being understood by AI isn&#8217;t the same as being recommended by it. New research reveals the gap between the two.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#What_brands_did_we_test_and_how_did_we_test_them\" >What brands did we test and how did we test them?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Recommendations_What_the_co-mention_data_showed\" >Recommendations: What the co-mention data showed<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Nike_the_hero_Same_KG_description_completely_different_results\" >Nike, the hero: Same KG description, completely different results<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#The_third-party_citation_weight_in_recommendation_vs_recognition_data\" >The third-party citation weight in recommendation vs. recognition data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#What_the_co-mention_structure_means_for_PR_and_content_strategy\" >What the co-mention structure means for PR and content strategy<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Editorial_roundups_and_comparison_pieces\" >Editorial roundups and comparison pieces\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Podcast_appearances\" >Podcast appearances<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Analyst_and_industry_reports\" >Analyst and industry reports<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Retailer_and_comparison_taxonomy\" >Retailer and comparison taxonomy<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#A_note_on_the_data_and_what_comes_next\" >A note on the data and what comes next<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/buradabiliyorum.com\/en\/what-co-mentions-reveal-about-the-ai-recommendation-gap\/#Topics_on_this_page\" >Topics on this page<\/a><\/li><\/ul><\/li><\/ul><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"subhead\" itemprop=\"alternativeHeadline\"><span class=\"ez-toc-section\" id=\"Being_understood_by_AI_isnt_the_same_as_being_recommended_by_it_New_research_reveals_the_gap_between_the_two\"><\/span>Being understood by AI isn&#8217;t the same as being recommended by it. New research reveals the gap between the two.<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><\/p>\n<div class=\"bialty-container\">\n<p>We\u2019ve spent the last two years optimizing for AI visibility by focusing on what we say about ourselves: writing better About pages, adding clear schema and SameAs markup, structuring content more effectively, and providing more direct answers.<\/p>\n<p>All of these principles still <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/download-scripts-themes-apps\/\" data-internallinksmanager029f6b8e52c=\"9\" title=\"Download Scripts &amp; Themes &amp; Apps\" target=\"_blank\" rel=\"noopener\">app<\/a>ly and are essential for the qualification phase of an LLM\u2019s brand processing (clarity + relevance). But a study Jo\u00e3o da Silva and I conducted using Friction AI\u2019s platform puts a number on a factor the industry has been circling around but couldn\u2019t prove.<\/p>\n<p>Among brands that were already recognized (where the LLM could describe them accurately), Knowledge Graph (KG) strength predicted visibility within the category each brand was coded to. What it didn\u2019t predict was whether a brand would surface in an adjacent category query, even if it belonged there from a business perspective. In other words, recognition didn\u2019t guarantee recommendation. That\u2019s the framing gap.<\/p>\n<h2 id=\"what-brands-did-we-test-and-how-did-we-test-them\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_brands_did_we_test_and_how_did_we_test_them\"><\/span>What brands did we test and how did we test them?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For this case study, we tested 12 athleisure and activewear brands across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews: 14,140 API runs over seven days, using UK geography with web search enabled.<\/p>\n<p>For each brand, we ran two types of prompts:<\/p>\n<ul class=\"wp-block-list\">\n<li>Recognition prompts (\u201cWhat is [Brand]?\u201d and \u201cDescribe [Brand]\u201d)<\/li>\n<li>Recommendation prompts (\u201cBest athleisure brands,\u201d \u201cTop 10 athleisure brands,\u201d and \u201cWhich athletic apparel brands are worth buying in 2026?\u201d)<\/li>\n<\/ul>\n<p>The brands spanned three Knowledge Graph tiers, assigned by Google KG resultScore (the raw score returned by Google\u2019s Knowledge Graph Search API \u2014 a proxy for how strongly an entity is established in Google\u2019s index), so we could test whether KG strength predicted recommendation visibility:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Low KG: <\/strong>LNDR, TALA, Gymshark, Varley.<\/li>\n<li><strong>Mid KG: <\/strong>Reebok, Outdoor Voices, Rhone Apparel, Sweaty Betty.<\/li>\n<li><strong>High KG: <\/strong>Alo Yoga, Nike, lululemon, New Balance.<\/li>\n<\/ul>\n<p>Spoiler ahead: The high-KG brands didn\u2019t dominate recommendations. The mid-KG tier showed the largest average gap between recognition and recommendation.<\/p>\n<p>Within the high-KG tier, some brands were universally recommended, while others were nearly invisible in recommendation prompts, despite being perfectly recognized across every LLM we tested.<\/p>\n<div style=\"background: radial-gradient(circle at 30% 40%, rgba(184, 111, 255, 0.15), rgba(0, 169, 255, 0.15) 40%, #CDE8FD 70%); padding: 30px; width: 100%; max-width: 802px; color: #000000 !important; font-family: Arial, sans-serif; margin: 25px 0 30px 0; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); position: relative; box-sizing: border-box;\">\n<div style=\"width: 100%; max-width: 100%; margin-bottom: 20px; text-align: left; padding-right: 20px; box-sizing: border-box;\">\n<div id=\"semrush-one-headline\" class=\"headline-responsive\" style=\"font-family: Oswald, sans-serif; font-size: 30px; font-weight: normal; margin: 0; color: #000000 !important; line-height: 1.2;\">\n        Your customers search everywhere. Make sure your brand <span style=\"background: linear-gradient(90deg, #D56EFE 0%, #068EF8 51%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;\">shows up<\/span>.\n      <\/div>\n<p id=\"semrush-one-subhead\" style=\"font-family: Roboto, sans-serif; font-size: 18px; font-weight: 300; line-height: 25px; margin: 12px 0 0 0; color: #000000 !important;\">\n        The SEO toolkit you know, plus the AI visibility data you need.\n      <\/p>\n<\/p><\/div>\n<div style=\"margin-bottom: 15px;\">\n      <span id=\"semrush-one-cta\" style=\"display: inline-block; background-color: #FF642D; color: white; height: 44px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px; padding: 0 24px; font-weight: bold; white-space: nowrap; box-sizing: border-box; text-decoration: none; line-height: 44px;\">Start Free Trial<\/span>\n    <\/div>\n<div style=\"font-size: 12px;\">\n<div style=\"font-family: Roboto, sans-serif; font-weight: 300; color: #000000; margin-bottom: 4px;\">Get started with<\/div>\n<p>      <img loading=\"lazy\" width=\"400\" height=\"52\" decoding=\"async\" http: alt=\"Semrush One Logo\" style=\"height: 16px; width: auto; display: block;\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2025\/11\/semrush-one.webp\"><img loading=\"lazy\" width=\"400\" height=\"52\" decoding=\"async\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2025\/11\/semrush-one.webp\" alt=\"Semrush One Logo\" style=\"height: 16px; width: auto; display: block;\">\n    <\/div>\n<\/p><\/div>\n<style>\n  @media (max-width: 768px) {\n    .headline-responsive {\n      font-size: 30px !important;\n      line-height: 1.3 !important;\n    }\n  }\n<\/style>\n<h2 id=\"recommendations-what-the-comention-data-showed\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Recommendations_What_the_co-mention_data_showed\"><\/span>Recommendations: What the co-mention data showed<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>We mapped how often brands appeared together in athleisure content across external sources (articles, reviews, comparison pieces, and editorial lists) crawled via API from UK-indexed sources.<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1562\" height=\"680\" http: alt=\"Co-mentions Frictional\" class=\"wp-image-479832\" srcset=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai.png 1562w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai-768x334.png 768w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai-1536x669.png 1536w\" data-lazy-sizes=\"(max-width: 1562px) 100vw, 1562px\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai.png\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1562\" height=\"680\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai.png\" alt=\"Co-mentions Frictional\" class=\"wp-image-479832\" srcset=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai.png 1562w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai-768x334.png 768w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/co-mentions_frictionai-1536x669.png 1536w\" sizes=\"(max-width: 1562px) 100vw, 1562px\"><\/figure>\n<\/div>\n<p>Some of the most interesting results include:<\/p>\n<ul class=\"wp-block-list\">\n<li>lululemon + Alo Yoga: 534 co-mentions.<\/li>\n<li>lululemon + Nike: 482 co-mentions.<\/li>\n<li>Alo Yoga + Nike: 449 co-mentions.<\/li>\n<li>Gymshark + lululemon: 264 co-mentions.<\/li>\n<li>Gymshark + Alo Yoga: 252 co-mentions.<\/li>\n<\/ul>\n<p>These brands appear together repeatedly in the same articles, roundups, and editorial comparisons across independent sources. Together, they form a cluster that the LLM treats as \u201cathleisure.\u201d<\/p>\n<p>Now look at the other end of the spectrum. New Balance co-occurs with lululemon in athleisure content so rarely that it doesn\u2019t appear in the top pairs at all. Nike co-occurs with lululemon roughly 50 times more often than New Balance does.<\/p>\n<p>Nike, New Balance, and Reebok share the exact same Google Knowledge Graph description: \u201cFootwear company.\u201d From an entity standpoint, they start from the same position. But Nike is inside the athleisure cluster. New Balance and Reebok are entirely outside it.<\/p>\n<p>The LLM isn\u2019t evaluating these brands independently and deciding which ones fit athleisure. It\u2019s pattern-matching against associations built from external content. If a brand hasn\u2019t appeared consistently alongside lululemon, Alo Yoga, and Gymshark in the content the model trained on \u2014 or retrieves from \u2014 it doesn\u2019t belong in that cluster because the semantic association was never built.<\/p>\n<h2 id=\"nike-the-hero-same-kg-description-completely-different-results\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Nike_the_hero_Same_KG_description_completely_different_results\"><\/span>Nike, the hero: Same KG description, completely different results<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Nike, New Balance, and Reebok share the same KG entity description: \u201cFootwear company.\u201d LLM probing across all five systems assigns all three unanimously to the athletic_footwear category, so from a pure entity-clarity standpoint, they start from the same position.<\/p>\n<p>However, their recommendation rates in athleisure queries aren\u2019t remotely equivalent.<\/p>\n<p>Nike surfaces in 71% of athleisure recommendation prompts, while New Balance and Reebok appear in 0% across all five LLMs and all 14,140 runs.<\/p>\n<p>The difference isn\u2019t how they\u2019re defined (\u201cFootwear company\u201d). It\u2019s which conversations they appear in and which other brands appear alongside them.<\/p>\n<p>LLMs don\u2019t infer category adjacency. If a brand hasn\u2019t been consistently mentioned alongside the relevant players in a category \u2014 in press, reviews, editorial content, and comparison pieces \u2014 the model doesn\u2019t make the leap. Jason Barnard describes this well: if A plus B should equal J, you have to construct that path explicitly. The model won\u2019t build it for you.<\/p>\n<p>New Balance\u2019s co-mention density lives in running and performance content. Nobody built the semantic bridge from running \u2192 athletic lifestyle \u2192 athleisure in external content, so the model doesn\u2019t cross it. The Knowledge Graph says \u201cFootwear company,\u201d and the third-party corpus confirms footwear. Athleisure queries retrieve the athleisure corpus, and New Balance isn\u2019t in it.<\/p>\n<p><!-- START INLINE FORM --><\/p>\n<p><!-- END INLINE FORM --><\/p>\n<hr class=\"wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background\">\n<h2 id=\"the-thirdparty-citation-weight-in-recommendation-vs-recognition-data\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_third-party_citation_weight_in_recommendation_vs_recognition_data\"><\/span>The third-party citation weight in recommendation vs. recognition data<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>When we split citations by prompt type \u2014 recognition vs. recommendation \u2014 a pattern emerges that should reframe where most GEO budgets are being spent.<\/p>\n<p>For recognition prompts \u2014 where the user has already typed your brand name \u2014 own-brand content is the dominant source:<\/p>\n<ul class=\"wp-block-list\">\n<li>ChatGPT cited own-brand content 49% of the time.<\/li>\n<li>Perplexity: 36%.<\/li>\n<li>Claude: 23%.<\/li>\n<\/ul>\n<p>This is where your About page and homepage are used for clarity, and your services, category, and guide pages are used for relevance.<\/p>\n<p>Recommendation prompts give us completely different results. When the user hasn\u2019t named your brand and is asking for the best option in a category, own-brand citations drop to 18% on ChatGPT and to effectively zero on Gemini, Claude, Perplexity, and Google AI Overviews. Third-party sources account for 82% to 100% of what gets cited across all five systems.<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1565\" height=\"311\" http: alt=\"the citation rate of owned content on LLM recommendation vs recognition answers.\" class=\"wp-image-479833\" srcset=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation.png 1565w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation-768x153.png 768w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation-1536x305.png 1536w\" data-lazy-sizes=\"(max-width: 1565px) 100vw, 1565px\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1565\" height=\"311\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation.png\" alt=\"the citation rate of owned content on LLM recommendation vs recognition answers.\" class=\"wp-image-479833\" srcset=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation.png 1565w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation-768x153.png 768w, https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/owned-content-citation-1536x305.png 1536w\" sizes=\"auto, (max-width: 1565px) 100vw, 1565px\"><\/figure>\n<\/div>\n<p>The GEO community has argued for some time that external signals matter more than on-site optimization for recommendation visibility, and this data puts specific numbers behind that argument. It also shows that external signals aren\u2019t all the same thing.<\/p>\n<ul class=\"wp-block-list\">\n<li>Entity clarity gets a brand recognized. That\u2019s a problem you solve on your own site.<\/li>\n<li>External credibility gets it considered. That\u2019s a PR and corroboration problem.<\/li>\n<li>Co-mention density in the right category cluster places a brand in the concept graph for a specific recommendation query. That\u2019s a category-positioning problem.\u00a0<\/li>\n<\/ul>\n<p>These are three separate problems that require different solutions. Conflating them is why many GEO recommendations stop short.<\/p>\n<p>The practical addition to any GEO audit is this: after checking entity clarity and external credibility, audit where you appear in relation to others.<\/p>\n<ul class=\"wp-block-list\">\n<li>Are your press mentions listing you alongside your actual category competitors?<\/li>\n<li>Do the roundups that include you also name the brands that dominate your target category?\u00a0<\/li>\n<\/ul>\n<p>If not, the LLM has probably never learned to associate you with that category because it has never seen you in that \u201ccompany.\u201d Unlike entity clarity or schema, it\u2019s not something you can fix on your own website. That\u2019s the gap.<\/p>\n<h2 id=\"what-the-comention-structure-means-for-pr-and-content-strategy\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_the_co-mention_structure_means_for_PR_and_content_strategy\"><\/span>What the co-mention structure means for PR and content strategy<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>As we\u2019ve seen so far, being mentioned in a category isn\u2019t enough. Being mentioned alongside the right brands in a category is what places you in the concept graph for that cluster.<\/p>\n<p>A press mention that describes a brand as \u201cperformance apparel\u201d in isolation does little to advance its athleisure concept graph placement.\u00a0<\/p>\n<p>A press mention that lists it alongside lululemon, Alo Yoga, and Gymshark in an editorial comparison does considerably more because it builds the co-occurrence signal the model needs to associate the brand with that cluster.<\/p>\n<p>The same logic applies across content type.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-editorial-roundups-and-comparison-pieces-nbsp\"><span class=\"ez-toc-section\" id=\"Editorial_roundups_and_comparison_pieces\"><\/span>Editorial roundups and comparison pieces\u00a0<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Being included in \u201cbest of\u201d lists that name your category competitors is worth more to your concept graph than a standalone brand profile. The cluster signal comes from appearing in the same article as the brands that define the category.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-podcast-appearances\"><span class=\"ez-toc-section\" id=\"Podcast_appearances\"><\/span>Podcast appearances<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>If the host introduces you in relation to specific named brands, or compares your approach to a category leader, that co-occurrence gets indexed.\u00a0<\/p>\n<p>A bio that says \u201cfounder of [Brand], which competes with lululemon and Gymshark in the premium athleisure space\u201d does different work than a bio that says \u201cfounder of [Brand], a performance apparel company.\u201d<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-analyst-and-industry-reports\"><span class=\"ez-toc-section\" id=\"Analyst_and_industry_reports\"><\/span>Analyst and industry reports<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Category-level reports that group brands together are high-signal co-mention sources. Being included in a sector analysis alongside your category peers places you in the concept graph in a way that standalone coverage doesn\u2019t.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-retailer-and-comparison-taxonomy\"><span class=\"ez-toc-section\" id=\"Retailer_and_comparison_taxonomy\"><\/span>Retailer and comparison taxonomy<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Being stocked and categorized alongside category leaders in a major retailer\u2019s taxonomy is a co-mention signal. The retailer\u2019s category page is external content that places your brand in a cluster.<\/p>\n<p>The goal is visibility in the right company.<\/p>\n<div style=\"background: radial-gradient(circle at 30% 40%, rgba(184, 111, 255, 0.15), rgba(0, 169, 255, 0.15) 40%, #CDE8FD 70%); padding: 30px; width: 100%; max-width: 802px; color: #000000 !important; font-family: Arial, sans-serif; margin: 25px 0 30px 0; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); position: relative; box-sizing: border-box;\">\n<div style=\"width: 100%; max-width: 100%; margin-bottom: 20px; text-align: left; padding-right: 20px; box-sizing: border-box;\">\n<div id=\"semrush-one-headline-bottom\" class=\"headline-responsive\" style=\"font-family: Oswald, sans-serif; font-size: 30px; font-weight: normal; margin: 0; color: #000000 !important; line-height: 1.2;\">\n        See the <span style=\"background: linear-gradient(90deg, #D56EFE 0%, #068EF8 51%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;\">complete picture<\/span> of your search visibility.\n      <\/div>\n<p id=\"semrush-one-subhead-bottom\" style=\"font-family: Roboto, sans-serif; font-size: 18px; font-weight: 300; line-height: 25px; margin: 12px 0 0 0; color: #000000 !important;\">\n        Track, optimize, and win in Google and AI search from one platform.\n      <\/p>\n<\/p><\/div>\n<div style=\"margin-bottom: 15px;\">\n      <span id=\"semrush-one-cta-bottom\" style=\"display: inline-block; background-color: #FF642D; color: white; height: 44px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px; padding: 0 24px; font-weight: bold; white-space: nowrap; box-sizing: border-box; text-decoration: none; line-height: 44px;\">Start Free Trial<\/span>\n    <\/div>\n<div style=\"font-size: 12px;\">\n<div style=\"font-family: Roboto, sans-serif; font-weight: 300; color: #000000; margin-bottom: 4px;\">Get started with<\/div>\n<p>      <img loading=\"lazy\" width=\"400\" height=\"52\" decoding=\"async\" http: alt=\"Semrush One Logo\" style=\"height: 16px; width: auto; display: block;\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2025\/11\/semrush-one.webp\"><img loading=\"lazy\" width=\"400\" height=\"52\" decoding=\"async\" src=\"https:\/\/searchengineland.com\/wp-content\/seloads\/2025\/11\/semrush-one.webp\" alt=\"Semrush One Logo\" style=\"height: 16px; width: auto; display: block;\">\n    <\/div>\n<\/p><\/div>\n<style>\n  @media (max-width: 768px) {\n    .headline-responsive {\n      font-size: 30px !important;\n      line-height: 1.3 !important;\n    }\n  }\n<\/style>\n<h2 id=\"a-note-on-the-data-and-what-comes-next\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"A_note_on_the_data_and_what_comes_next\"><\/span>A note on the data and what comes next<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This study covers a single category \u2014 athleisure and activewear \u2014 with 12 brands tested in the UK. The co-mention figures are raw co-occurrence counts from UK-indexed sources crawled via API, covering content indexed at the time of the study in May 2026. Cross-category validation and additional geography testing are in progress.<\/p>\n<p>The full paper, \u201c<a rel=\"nofollow\" target=\"_blank\" href=\"https:\/\/zenodo.org\/records\/20445151\" target=\"_blank\" rel=\"noopener\">The Recognition-Recommendation Gap: Empirical Evidence That Category Coding, Not Knowledge-Graph Strength, Determines Brand Visibility in Generative AI Output<\/a>,\u201d has been published by Jo\u00e3o da Silva and me on Zenodo and documents the methodology, brand sample, prompt set, and extraction code in sufficient detail for independent replication.<\/p>\n<p>But the pattern in the co-mention data is clear enough to act on now. Three brands share the same Knowledge Graph description: one appears in 71% of athleisure recommendation responses, and two appear in 0%. The structural difference is co-mention density in category-aligned third-party content.<\/p>\n<p>The question worth asking about any brand is this: In the content that talks about your category, are you in the room, and are you in the right company?<\/p>\n<div class=\"ttd-topics-display\">\n<div class=\"ttd-topics-content\">\n<h5><span class=\"ez-toc-section\" id=\"Topics_on_this_page\"><\/span>Topics on this page<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<div class=\"ttd-topics-links\">Artificial intelligenceAlo YogaGenerative AINew BalanceNikeAI OverviewsApplication programming interfaceBrandBrand managementChatGPTClaudeGeminiJason BarnardKnowledge GraphKnowledge graphLarge language modelPerplexity AISearch engine optimizationSemantic searchUnited Kingdom<\/div>\n<\/div>\n<div class=\"ttd-topics-show-extra-button\">+15 more<\/div>\n<\/div>\n<\/div>\n<blockquote><p><strong><span style=\"color: #ff6600;\">If you liked the article, do not forget to share it with your friends. Follow us on\u00a0<span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" href=\"https:\/\/news.google.com\/publications\/CAAqBwgKMN63nwsw68G3Aw\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Google News<\/a><\/span>\u00a0too, click on the star and choose us from your favorites.<\/span><\/strong><\/p><\/blockquote>\n<blockquote>\n<p style=\"text-align: center;\"><strong>If you want to read more like this article, you can visit our <span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" href=\"https:\/\/buradabiliyorum.com\/en\/category\/technology\/\" target=\"_blank\" >Technology<\/a><\/span> category.<\/strong><\/p>\n<\/blockquote>\n<p><span style=\"color: black;\"><a style=\"color: #ff9900;\" href=\"https:\/\/searchengineland.com\/co-mentions-ai-recommendation-gap-479829\" target=\"_blank\" >Source<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Being understood by AI isn&#8217;t the same as being recommended by it. New research reveals the gap between the two. We\u2019ve spent the last two years optimizing for AI visibility by focusing on what we say about ourselves: writing better About pages, adding clear schema and SameAs markup, structuring content more effectively, and providing more&#8230;<\/p>\n","protected":false},"author":1,"featured_media":732976,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/searchengineland.com\/wp-content\/seloads\/2026\/06\/What-co-mentions-reveal-about-the-AI-recommendation-gap.png","fifu_image_alt":"","footnotes":""},"categories":[18],"tags":[],"class_list":["post-732975","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology"],"_links":{"self":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/732975","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/comments?post=732975"}],"version-history":[{"count":0,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/732975\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media\/732976"}],"wp:attachment":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media?parent=732975"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/categories?post=732975"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/tags?post=732975"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}