{"id":8082,"date":"2026-04-09T09:24:01","date_gmt":"2026-04-09T09:24:01","guid":{"rendered":"https:\/\/www.adlift.com\/in\/?post_type=blog_post&#038;p=8082"},"modified":"2026-04-09T09:36:14","modified_gmt":"2026-04-09T09:36:14","slug":"logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend","status":"publish","type":"blog_post","link":"https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/","title":{"rendered":"Logged In or Logged Out: Do AI Platforms Choose Different Brands to Recommend?"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_66_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" 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: #999;color:#999\" 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><\/span><\/a><\/span><\/div>\n<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:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#We_ran_1530_prompts_across_ChatGPT_Gemini_and_Perplexity_Here_is_what_we_found title=\"We ran 1,530+ prompts across ChatGPT, Gemini and Perplexity. Here is what we found.\u00a0\">We ran 1,530+ prompts across ChatGPT, Gemini and Perplexity. Here is what we found.\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#Before_the_results_how_do_AI_platforms_actually_choose_which_brands_to_recommend title=\"Before the results: how do AI platforms\u00a0actually choose\u00a0which brands to recommend?\u00a0\">Before the results: how do AI platforms\u00a0actually choose\u00a0which brands to recommend?\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#What_do_the_latest_LLM_research_findings_actually_tell_us_about_login_state title=\"What do the latest LLM research findings\u00a0actually tell\u00a0us about login state?\">What do the latest LLM research findings\u00a0actually tell\u00a0us about login state?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#If_the_brands_stay_the_same_what_exactly_does_change_between_logged-in_and_logged-out title=\"If the brands stay the same, what exactly does change between logged-in and logged-out?\">If the brands stay the same, what exactly does change between logged-in and logged-out?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#ChatGPT_Gemini_and_Perplexity_each_behave_differently_but_which_differences_actually_matter_for_your_brand title=\"ChatGPT, Gemini and Perplexity each behave differently,\u00a0but which differences actually matter for your brand?\u00a0\u00a0\">ChatGPT, Gemini and Perplexity each behave differently,\u00a0but which differences actually matter for your brand?\u00a0\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#Is_AI_recommending_your_brand_right_now_and_what_should_you_do_if_it_is_not title=\"Is AI recommending your brand right now, and what should you do if it is not?\">Is AI recommending your brand right now, and what should you do if it is not?<\/a><\/li><\/ul><\/nav><\/div>\n<p><i><span data-contrast=\"auto\">We ran 1,530+ prompts across ChatGPT, Gemini and Perplexity in both authenticated and anonymous sessions.\u00a0Here&#8217;s\u00a0what the latest LLM research found.<\/span><\/i><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">There is an assumption quietly spreading through marketing teams right now: that AI personalises its recommendations based on who the user is. Log into ChatGPT, and surely it learns your preferences, your history, your context and recommends accordingly. Stay anonymous, and you get something more generic.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">It sounds completely logical. But is it true?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We decided to find out. In the largest LLM research study we have run to date,\u00a0AdLift\u00a0and\u00a0<\/span><b><span data-contrast=\"auto\">Tesseract<\/span><\/b><span data-contrast=\"auto\">\u00a0submitted\u00a01,530+ identical prompts to ChatGPT, Gemini and Perplexity simultaneously, once in a logged-in authenticated session and once in a logged-out anonymous session, across six industries and hundreds of brand comparisons.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The headline finding was an average Overlap Coefficient of 90.4%. In plain English: nine out of ten brands recommended to a logged-in user were also recommended to an anonymous one. Login state barely moves the needle on which brands AI recommends. What it does change, sometimes quite dramatically, is everything else.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"We_ran_1530_prompts_across_ChatGPT_Gemini_and_Perplexity_Here_is_what_we_found\"><\/span><span class=\"TextRun SCXW104486745 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW104486745 BCX0\">We ran 1,530+ prompts across ChatGPT, Gemini and Perplexity. Here is what we found.<\/span><\/span><span class=\"EOP SCXW104486745 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span data-contrast=\"auto\">The question at the centre of this LLM research study 2026 is one that should matter to every brand thinking seriously about AI visibility: does an AI recommendation system behave differently depending on whether the user is\u00a0identified\u00a0or anonymous?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">To answer it, we ran a controlled study across six industries:\u00a0<\/span><b><span data-contrast=\"auto\">Insurance, Healthcare, E-Commerce, Travel and Hospitality, SaaS and B2B.<\/span><\/b><span data-contrast=\"auto\">\u00a0These cover a wide range of consumer and business purchase decisions. For each industry, we built a curated brand dictionary and ran structured prompts covering general best-of queries, price-sensitivity queries, service-specific\u00a0queries\u00a0and demographic-targeted queries.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Every single prompt was\u00a0submitted\u00a0twice, at the same time: once by an authenticated user and once by an anonymous one. Responses were recorded verbatim. Brand mentions were extracted using whole-word pattern matching. The result was 1,530+ paired observations to work through.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We measured brand consistency using the\u00a0<\/span><b><span data-contrast=\"auto\">Overlap Coefficient<\/span><\/b><span data-contrast=\"auto\">, which is simply the proportion of brands in the smaller response that also appear in the larger one. A score of 100% means every brand named to a logged-in user was also named to an anonymous one. Across all sectors and all three platforms, that average landed at 90.4%.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-medium wp-image-7648\" src=\"https:\/\/www.adlift.com\/in\/wp-content\/uploads\/2026\/04\/methodology.png\" alt=\"\" \/><\/p>\n<p><span class=\"TextRun SCXW121015590 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW121015590 BCX0\">That number is the core finding of this large language model research. But the detail underneath it, broken down by sector and by platform, is where things get genuinely interesting.<\/span><\/span><span class=\"EOP SCXW121015590 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Before_the_results_how_do_AI_platforms_actually_choose_which_brands_to_recommend\"><\/span><span class=\"TextRun SCXW19669787 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW19669787 BCX0\">Before the results: how do AI platforms\u00a0<\/span><span class=\"NormalTextRun AdvancedProofingIssueV2Themed SCXW19669787 BCX0\">actually choose<\/span><span class=\"NormalTextRun SCXW19669787 BCX0\">\u00a0which brands to recommend?<\/span><\/span><span class=\"EOP SCXW19669787 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span data-contrast=\"auto\">To make sense of what we found, you need a working understanding of how large language models work at the most practical level for marketers.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">LLMs are not search engines. They do not look your brand up in real time and check its latest reviews or its current ad spend. They were trained, at a fixed point in time, on enormous quantities of web content: articles, reviews, forums, product pages, news coverage, research papers and more. The brand recommendations they produce are a function of what appeared in that training data, how often, in what context and with what authority around it.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This is the core\u00a0reason why\u00a0an AI recommendation system is unlikely to change its brand outputs based on whether a user happens to be logged in. The model&#8217;s underlying knowledge, the weights built up during training, does not update between sessions. A logged-in user might get a slightly more structured or contextualised response. But the brand hierarchy the model has learned from training data is not going to shift based on session state.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">There is one important nuance worth flagging. Perplexity, unlike ChatGPT and Gemini, uses retrieval-augmented generation, meaning it searches the live web to supplement\u00a0its responses. This means its outputs can include more current sources and citations. But even here, as the data shows, the core brand set stays highly consistent across session states.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The practical implication, before we even get to the numbers, is this: if you want to change what brands an AI recommends, you need to change what the model has learned, not try to optimise for the session.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-medium wp-image-7649\" src=\"https:\/\/www.adlift.com\/in\/wp-content\/uploads\/2026\/04\/llm-model.png\" alt=\"\" \/><\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_do_the_latest_LLM_research_findings_actually_tell_us_about_login_state\"><\/span><span class=\"NormalTextRun SCXW100878834 BCX0\">What do the latest LLM research findings\u00a0<\/span><span class=\"NormalTextRun AdvancedProofingIssueV2Themed SCXW100878834 BCX0\">actually tell<\/span><span class=\"NormalTextRun SCXW100878834 BCX0\">\u00a0us about login state?<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span data-contrast=\"auto\">Across 1,237 paired observations where both logged-in and logged-out responses were\u00a0available,\u00a0the average Overlap Coefficient\u00a0(OC)\u00a0sits at 90.4%. That is the headline from this latest LLM research. But it is worth pausing on what it\u00a0actually means\u00a0before moving on.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">An OC of 90.4% means that across thousands of individual brand mentions, pulled from responses generated by three different AI platforms across six completely different industries, the core set of brands recommended to an authenticated user and an anonymous user is structurally the same. This is not a rounding error or a marginal result. It is a meaningful finding about how AI brand recommendation behaviour\u00a0actually works.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-medium wp-image-7650\" src=\"https:\/\/www.adlift.com\/in\/wp-content\/uploads\/2026\/04\/sector-breakdown.png\" alt=\"\" \/><\/p>\n<p><span data-contrast=\"auto\">Here is how each sector breaks down:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The sectors with the highest overlap, B2B and E-Commerce, are dominated by a small number of globally recognised brands: AWS, Azure, Microsoft, Google Cloud, Apple, Samsung. These names are so deeply embedded in training data that session-level variation cannot displace them. The sectors with lower overlap, Healthcare in particular, have more complex brand landscapes where supplementary providers show more variability between sessions.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">But even 83.2%, the lowest sector average in the study, is a genuinely strong consistency score. The core recommendations are stable. What shifts is the margin around them.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"If_the_brands_stay_the_same_what_exactly_does_change_between_logged-in_and_logged-out\"><\/span><span class=\"TextRun SCXW69282509 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW69282509 BCX0\">If the brands stay the same, what exactly does change between logged-in and logged-out?<\/span><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span data-contrast=\"auto\">If the brands stay\u00a0largely the\u00a0same, the data still surfaces something worth paying attention to. The way AI platforms respond to logged-in versus logged-out users differs meaningfully in terms of length, structure, emphasis and in one case, access to content altogether.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Response length shifts, sometimes quite dramatically<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The most\u00a0immediately\u00a0visible difference between logged-in and logged-out responses is how long they are. But there is no single consistent direction here. The pattern varies by platform and by sector in ways that reveal something about each platform&#8217;s underlying logic.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">ChatGPT produces longer responses for anonymous users in some sectors, Insurance up 5.8% and SaaS up 7.7%, but notably shorter ones in others. Healthcare responses shrink by 32.9% when the user is logged out, and B2B responses shrink by 26.9%. The pattern suggests ChatGPT adds contextual depth in its logged-in mode for complex or sensitive topics, while defaulting to broader coverage for anonymous users on more consumer-facing queries.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Gemini tends to move in the opposite direction across most sectors, consistently expanding for anonymous users. B2B responses grow by 24.7% logged-out, E-Commerce by 15.4% and SaaS by 13.7%. Its anonymous responses read as more generalist and context-setting, while logged-in responses are more tightly formatted and specific.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Perplexity is the most directionally consistent of the three: its logged-out responses are shorter across five of the six sectors. The SaaS compression is the single most striking data point in this whole LLM case study. Logged-out responses are 54% shorter on average in that sector. And yet the brand overlap still comes in at 97.7%, the highest single platform-sector score in the entire study. Response length and brand fidelity are simply not the same thing.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><strong><span class=\"TextRun SCXW149310553 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW149310553 BCX0\">Brand emphasis shifts even when brand presence does not<\/span><\/span><span class=\"EOP SCXW149310553 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/strong><\/p>\n<p><span data-contrast=\"auto\">The Overlap Coefficient measures whether a brand appears at all. What it does not capture is how prominently a brand features, specifically how many times it gets mentioned within a response. This is where the differences between sessions become more pronounced.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In Insurance, ChatGPT&#8217;s logged-out responses name every tracked brand more\u00a0frequently, but the biggest gains go to challenger and niche providers. Erie is mentioned 29% more often when the user is anonymous. Liberty Mutual gains 34%. Farmers\u00a0gains\u00a039%. The anonymous response casts a wider net, surfacing brands that logged-in responses tend to keep further down the list. The core set does not change. The emphasis does.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In B2B, ChatGPT&#8217;s logged-out responses mention IBM 98% more often and Oracle 47% more, while Google Cloud loses 45% and AWS loses 19% of its mentions. Same brands. Meaningfully different weighting. For any brand in this sector, the question is not just whether you appear. It is whether you appear with the frequency that\u00a0actually reflects\u00a0your market position.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><strong><span class=\"TextRun SCXW121616125 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW121616125 BCX0\">Perplexity&#8217;s access gap is the most significant login effect in the study<\/span><\/span><\/strong><\/p>\n<p><span data-contrast=\"auto\">For all three platforms, login state\u00a0mainly affects\u00a0style and depth. In one case it affects access entirely.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In the Insurance sector, Perplexity returned 26 completely absent logged-out responses. These were prompts that received a full response in the authenticated session but returned nothing in the anonymous one. They were not random. They clustered tightly around three prompt themes: pricing queries, young driver\u00a0queries\u00a0and claims queries.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span class=\"TextRun SCXW161728374 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW161728374 BCX0\">This is the one place in the study where authentication state materially affects content availability rather than content quality. Whether it reflects deliberate rate-limiting, content moderation logic or a retrieval constraint for commercially sensitive topics is not clear from the data alone. But the pattern is far too consistent to be coincidental. For Insurance brands whose relevance peaks on pricing or claims, the most conversion-sensitive queries in the category, anonymous users on Perplexity may simply not receive a recommendation at all.<\/span><\/span><span class=\"EOP SCXW161728374 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><strong><span class=\"NormalTextRun SCXW90454369 BCX0\">If login state does not drive AI-powered brand visibility, what\u00a0<\/span><span class=\"NormalTextRun AdvancedProofingIssueV2Themed SCXW90454369 BCX0\">actually does<\/span><span class=\"NormalTextRun SCXW90454369 BCX0\">?<\/span><\/strong><span class=\"NormalTextRun SCXW90454369 BCX0\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The strategic implication of a 90.4% average Overlap Coefficient is direct: optimising for session state means optimising for the wrong variable entirely. Login personalisation is not the lever for ai brand visibility. Training-time brand salience is.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">What\u00a0determines\u00a0whether your brand appears in ai recommendations is not who is asking the question. It is whether your brand is sufficiently embedded in the web&#8217;s information ecosystem that the model\u00a0encountered\u00a0it,\u00a0frequently, authoritatively and in the right context, during training.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><strong><span class=\"TextRun SCXW211489078 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW211489078 BCX0\">Three things\u00a0<\/span><span class=\"NormalTextRun AdvancedProofingIssueV2Themed SCXW211489078 BCX0\">actually drive<\/span><span class=\"NormalTextRun SCXW211489078 BCX0\">\u00a0AI brand recommendations in practice:<\/span><\/span><span class=\"EOP SCXW211489078 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/strong><\/p>\n<ol>\n<li><b><span data-contrast=\"auto\">Citation depth.<\/span><\/b><span data-contrast=\"auto\">\u00a0How often do authoritative third-party sources reference your brand? News coverage, analyst reports, industry publications, review platforms: the more your brand appears in trusted sources, the more signal the model\u00a0has to\u00a0draw on when forming a response.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Content surface area.<\/span><\/b><span data-contrast=\"auto\">\u00a0How broad is the range of topics your brand appears alongside across the web? A brand that shows up only in its own promotional content is far less visible to an LLM than one that appears in answers to real consumer questions: comparisons, buying guides, how-to articles, expert roundups.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Third-party validation.<\/span><\/b><span data-contrast=\"auto\">\u00a0PR, structured data, accreditations, and review volume all play a role. LLMs learn about brands from the ecosystem that exists around them, not just from the brand\u2019s own website\u2014which is exactly where our\u00a0<\/span><a href=https:\/\/www.adlift.com\/in\/llm-seo-service\/><b><span data-contrast=\"none\">LLM SEO services<\/span><\/b><\/a><span data-contrast=\"auto\">\u00a0come in, strengthening that broader digital footprint.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">The brands that score highest across all three platforms and both session states in this study are not simply the biggest names in their categories. They are the ones most deeply embedded in web discourse, the brands that appear in the content other people write about their industries. That is the quality that ai recommendations reflect.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><i><span data-contrast=\"auto\">&#8220;Your brand&#8217;s presence in AI recommendations is a structural property of your digital authority, not a function of who the user is.&#8221;<\/span><\/i><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"ChatGPT_Gemini_and_Perplexity_each_behave_differently_but_which_differences_actually_matter_for_your_brand\"><\/span><span class=\"NormalTextRun SCXW19395089 BCX0\">ChatGPT, Gemini and Perplexity each behave differently<\/span><span class=\"NormalTextRun SCXW19395089 BCX0\">,\u00a0<\/span><span class=\"NormalTextRun SCXW19395089 BCX0\">but which differences actually matter for your brand?<\/span><span class=\"NormalTextRun SCXW19395089 BCX0\">\u00a0\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span data-contrast=\"auto\">One of the\u00a0more practically useful findings from this llm research study is how differently the three platforms behave, not in the brands they select, but in their response characteristics. Understanding these platform personalities matters for how brands read their AI visibility and decide where to focus.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">ChatGPT has the broadest reach and the highest overall OC<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">ChatGPT achieves the highest average Overlap Coefficient of the three platforms at 92.4%. Its logged-in responses tend to be more structured and citation-rich. Its logged-out responses are broader in scope. In consumer-facing sectors, anonymous users actually receive responses that surface more of the brand landscape, not less. ChatGPT&#8217;s anonymous mode reads a bit like a general industry briefing. Its logged-in mode reads more like a tailored recommendation. The brand set underlying both is nearly identical.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Gemini has the most stable formatting but the most variable OC<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Gemini records the widest range of Overlap Coefficient scores across the study: 92.5% in B2B, but only 77.9% in Healthcare, which is the lowest single platform-sector score we recorded. Its logged-out responses tend to be longer and more generalist, while logged-in responses are more tightly formatted and specific. Healthcare is the clearest example of Gemini&#8217;s supplementary brand set showing sensitivity to session state. The anchor institutions are stable. The second-tier providers shift more than on any other platform.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Perplexity is citation-dense and compressed, but brand-consistent<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Perplexity&#8217;s use of real-time web retrieval makes it structurally different from the other two platforms. It produces the highest citation volume of any platform when logged in, averaging 9.7 source links per response, and drops to 7.6 when the user is anonymous. Its responses are considerably shorter in anonymous mode across most sectors. And yet it achieves strong brand consistency: 90.5% average OC across the study, including\u00a0the highest single score of 98.1% in B2B. The platform strips back the detail for anonymous users while preserving the brand signal almost entirely.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">The platform your brand appears on matters a great deal more than the session state the user is in. Perplexity users see shorter responses but the same brands. Gemini users see slightly more variability in the supplementary set. ChatGPT users see largely the same brands regardless. Login state is genuinely the least interesting dimension here.<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Is_AI_recommending_your_brand_right_now_and_what_should_you_do_if_it_is_not\"><\/span><span class=\"TextRun SCXW203174183 BCX0\" lang=\"EN-IN\" xml:lang=\"EN-IN\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW203174183 BCX0\">Is AI recommending your brand right now, and what should you do if it is not?<\/span><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span data-contrast=\"auto\">Login state is not the lever for ai powered brand visibility. This llm research study makes that clear across 1,530+ observations, six industries and three platforms. The brands that appear consistently, to logged-in and anonymous users alike, are the ones whose digital authority is structural rather than circumstantial.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">That means the actionable questions look different from what most brands are currently asking:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Are you cited in the authoritative sources that LLMs are trained on? Does your content appear in the contexts where buying decisions are actually being researched? Are third parties writing about your brand with the depth and frequency that signals genuine market leadership? Are you tracking your ai brand visibility across platforms as an ongoing programme rather than a one-off snapshot?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">LLMs update. Brand rankings shift over time. A one-time audit tells you where you stand today. A monitoring programme gives you a competitive edge as the landscape evolves.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">AdLift\u00a0a leading\u00a0<\/span><a href=https:\/\/www.adlift.com\/in\/><b><span data-contrast=\"none\">digital marketing agency<\/span><\/b><\/a><span data-contrast=\"auto\">,\u00a0provides the strategy: the SEO, content and PR programmes that build the citation footprint that makes ai brand visibility possible in the first place.\u00a0<\/span><a href=https:\/\/tesseract.adlift.com\/ rel=\"nofollow\"><b><span data-contrast=\"none\">Tesseract<\/span><\/b><\/a><b><span data-contrast=\"auto\">\u00a0<\/span><\/b><span data-contrast=\"auto\">provides the infrastructure: real-time LLM brand monitoring, Overlap Coefficient tracking and AI visibility intelligence across all three platforms.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We ran 1,530+ prompts across ChatGPT, Gemini and Perplexity in both authenticated and anonymous sessions.\u00a0Here&#8217;s\u00a0what the latest LLM research found.\u00a0 There is an assumption quietly spreading through marketing teams right now: that AI personalises its recommendations based on who the user is. Log into ChatGPT, and surely it learns your preferences, your history, your context &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Logged In or Logged Out: Do AI Platforms Choose Different Brands to Recommend?&#8221;<\/span><\/a><\/p>\n","protected":false},"author":130,"featured_media":8087,"parent":0,"menu_order":0,"template":"","format":"standard","meta":[],"post-tag":[],"blog-category":[135],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>LLM Research: Do AI recommendations change when users are logged in?<\/title>\n<meta name=\"description\" content=\"Do AI recommend different brands when users are logged in? 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Learn from LLM research and understand how large language models work to rank brands.","breadcrumb":{"@id":"https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.adlift.com\/in\/blog\/logged-in-or-logged-out-do-ai-platforms-choose-different-brands-to-recommend\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.adlift.com\/in\/"},{"@type":"ListItem","position":2,"name":"Blog","item":"https:\/\/www.adlift.com\/in\/blog_post\/"},{"@type":"ListItem","position":3,"name":"Logged In or Logged Out: Do AI Platforms Choose Different Brands to Recommend?"}]}]}},"_links":{"self":[{"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/blog_post\/8082"}],"collection":[{"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/blog_post"}],"about":[{"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/types\/blog_post"}],"author":[{"embeddable":true,"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/users\/130"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/media\/8087"}],"wp:attachment":[{"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/media?parent=8082"}],"wp:term":[{"taxonomy":"post-tag","embeddable":true,"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/post-tag?post=8082"},{"taxonomy":"blog-category","embeddable":true,"href":"https:\/\/www.adlift.com\/in\/wp-json\/wp\/v2\/blog-category?post=8082"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}