{"id":6741,"date":"2025-12-16T16:11:02","date_gmt":"2025-12-16T21:11:02","guid":{"rendered":"https:\/\/beomniscient.com\/?p=6741"},"modified":"2025-12-17T12:13:51","modified_gmt":"2025-12-17T17:13:51","slug":"llm-brand-recommendations-customer-journey","status":"publish","type":"post","link":"https:\/\/beomniscient.com\/blog\/llm-brand-recommendations-customer-journey\/","title":{"rendered":"Do LLMs recommend brands even when users aren\u2019t shopping? [Research]"},"content":{"rendered":"\n<p>More and more consumers are leveraging LLMs to find, evaluate, and even purchase products.&nbsp;<\/p>\n\n\n\n<p>In 2025, we conducted research on <a href=\"https:\/\/beomniscient.com\/blog\/from-prompt-to-purchase\/\">buyer behavior in the LLM era<\/a> to understand how buyers move through the journey across channels and stages. That research showed buyers already use LLMs to establish selection criteria, shortlist brands, and compare competitors.&nbsp;<\/p>\n\n\n\n<p>That raised an interesting question:<\/p>\n\n\n\n<p>What happens when users aren\u2019t explicitly asking to buy anything?<br><br>As an avid ChatGPT user, I\u2019m consistently prompting things like \u201cMy WiFi sucks even though it\u2019s $90 a month. Is that normal in New York?\u201d or \u201cWhat\u2019s the best time to take magnesium?\u201d.<br><br>Basically, I (and many others) use ChatGPT as a de facto diary &#8211; complaining, problem solving, solution seeking, and only rarely, explicitly seeking product recommendations and evaluations.<\/p>\n\n\n\n<p>However, those queries (often referred to as \u201cpain point\u201d queries or problem statements) feel like a perfect opportunity for the LLM to mention alternative WiFi providers or supplement brands, <em>even though I never asked for them<\/em>.<br><br>So I designed an experiment to measure how often LLMs mention brands, broken down by funnel stage, to see if (and how often) LLMs recommend brands even without being asked.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-methodology\">Methodology<\/h2>\n\n\n\n<p>I created 180 prompts, split evenly across three stages:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem Unaware<\/strong>: General topic information or expression statements<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center\"><em>&#8220;How is go-to-market changing in the face of generative AI?&#8221;<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem Aware<\/strong>: Expressing or explaining a problem, not yet exploring solutions<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center\"><em>&#8220;I need to hire my first sales person and have no idea what I&#8217;m doing.\u201d<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Solution Aware<\/strong>: Brand comparisons or recommendations, explicit purchase intent<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center\"><em>&#8220;Best GEO agencies to drive AI visibility&#8221;<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1460\" height=\"821\" data-src=\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-2-edited.jpeg\" alt=\"\" class=\"wp-image-6771 lazyload\" data-srcset=\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-2-edited.jpeg 1460w, https:\/\/beomniscient.com\/wp-content\/uploads\/image-2-edited-300x169.jpeg 300w, https:\/\/beomniscient.com\/wp-content\/uploads\/image-2-edited-1024x576.jpeg 1024w, https:\/\/beomniscient.com\/wp-content\/uploads\/image-2-edited-768x432.jpeg 768w\" data-sizes=\"(max-width: 1460px) 100vw, 1460px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1460px; --smush-placeholder-aspect-ratio: 1460\/821;\" \/><\/figure>\n\n\n\n<p>The prompts spanned four industries, broken into four sub-categories:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>B2B SaaS: SEO, CRM Tools, and Cybersecurity<\/li>\n\n\n\n<li>Consumer Products: Electronics, Beauty, and Fitness<\/li>\n\n\n\n<li>Professional Services: Consulting, Real Estate, and Legal<\/li>\n\n\n\n<li>Finance: Personal Finance, Credit Cards, and Insurance<\/li>\n<\/ul>\n\n\n\n<p>Using <a href=\"http:\/\/peec.ai\">Peec.ai<\/a>, I collected 5,323 total LLM outputs across 5 different large language models. I then used natural language processing in Python to flag brand recommendations and compare how often brands appeared across stages, industries, sentiment, and prompt structure.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-brand-recommendations-from-llms-closely-follow-purchase-intent\"><strong>Brand recommendations from LLMs closely follow purchase intent<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"576\" data-src=\"https:\/\/beomniscient.com\/wp-content\/uploads\/Customer-Intent-1024x576.png\" alt=\"\" class=\"wp-image-6774 lazyload\" data-srcset=\"https:\/\/beomniscient.com\/wp-content\/uploads\/Customer-Intent-1024x576.png 1024w, https:\/\/beomniscient.com\/wp-content\/uploads\/Customer-Intent-300x169.png 300w, https:\/\/beomniscient.com\/wp-content\/uploads\/Customer-Intent-768x432.png 768w, https:\/\/beomniscient.com\/wp-content\/uploads\/Customer-Intent-1536x864.png 1536w, https:\/\/beomniscient.com\/wp-content\/uploads\/Customer-Intent.png 1920w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/576;\" \/><\/figure>\n\n\n\n<p>One of the strongest predictors of whether an LLM recommends a brand is how close the user is to a decision.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Problem Unaware prompts generated brand recommendations in answers 19% of the time.<\/li>\n\n\n\n<li>Problem Aware prompts increased brand recommendations to 28%.<\/li>\n<\/ul>\n\n\n\n<p>That means <em>even when users aren\u2019t asking for products or services, LLMs introduce brands in up to \u2153 of the answers.<\/em><\/p>\n\n\n\n<p>Once users explicitly asked for recommendations or comparisons:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Solution Aware prompts triggered brand recommendations 79% of the time.<\/li>\n<\/ul>\n\n\n\n<p>Why not 100%? In some cases, LLMs responded with selection criteria, category explanations, or high-level guidance instead of naming brands directly.<\/p>\n\n\n\n<p>For example, when asked \u201c<em>What are the best skincare brands for repairing a weakened skin barrier?<\/em>\u201d, one response focused on desirable brand attributes rather than actual brands, advising readers to \u201c<em>look for brands that prioritize barrier-supportive ingredients like ceramides, fatty acids, and niacinamide<\/em>\u201d.<br><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-brand-recommendations-from-llms-vary-widely-across-industries-and-categories\"><strong>Brand recommendations from LLMs vary widely across industries and categories<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"576\" data-src=\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-202-edited-1024x576.png\" alt=\"\" class=\"wp-image-6772 lazyload\" data-srcset=\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-202-edited-1024x576.png 1024w, https:\/\/beomniscient.com\/wp-content\/uploads\/image-202-edited-300x169.png 300w, https:\/\/beomniscient.com\/wp-content\/uploads\/image-202-edited-768x432.png 768w, https:\/\/beomniscient.com\/wp-content\/uploads\/image-202-edited-1536x863.png 1536w, https:\/\/beomniscient.com\/wp-content\/uploads\/image-202-edited.png 1674w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/576;\" \/><\/figure>\n\n\n\n<p>Across all prompts analyzed:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>B2B SaaS saw brand recommendations in 57% of outputs<\/li>\n\n\n\n<li>Consumer products followed at 43%<\/li>\n\n\n\n<li>Finance dropped to 38%<\/li>\n\n\n\n<li>Professional services lagged at 29%<\/li>\n<\/ul>\n\n\n\n<p>Zooming in reveals even bigger differences:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>In SEO-specific prompts, brands were mentioned in 77% of LLM outputs, <em>triggering the highest promotional behavior across all categories<\/em><\/li>\n\n\n\n<li>Electronics queries contained brands in 65% of outputs<\/li>\n\n\n\n<li>Legal service brands were mentioned just 19% of the time<\/li>\n<\/ul>\n\n\n\n<p>LLMs appear far more willing to name brands in categories where products are standardized, comparisons are expected, and perceived risk is lower.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-valence-matters-prompts-with-neutral-sentiment-drove-the-highest-percentage-of-brand-recommendations\">Valence matters: prompts with neutral sentiment drove the highest percentage of brand recommendations<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1620\" height=\"911\" data-src=\"https:\/\/beomniscient.com\/wp-content\/uploads\/Sentiment-edited.png\" alt=\"\" class=\"wp-image-6776 lazyload\" data-srcset=\"https:\/\/beomniscient.com\/wp-content\/uploads\/Sentiment-edited.png 1620w, https:\/\/beomniscient.com\/wp-content\/uploads\/Sentiment-edited-300x169.png 300w, https:\/\/beomniscient.com\/wp-content\/uploads\/Sentiment-edited-1024x576.png 1024w, https:\/\/beomniscient.com\/wp-content\/uploads\/Sentiment-edited-768x432.png 768w, https:\/\/beomniscient.com\/wp-content\/uploads\/Sentiment-edited-1536x864.png 1536w\" data-sizes=\"(max-width: 1620px) 100vw, 1620px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1620px; --smush-placeholder-aspect-ratio: 1620\/911;\" \/><\/figure>\n\n\n\n<p>Initially, I expected emotionally charged prompts to trigger more recommendations, especially negative ones like <em>\u201cOur customer data is scattered across spreadsheets, why is this so hard to manage?\u201d<\/em>.<\/p>\n\n\n\n<p>Instead, neutral prompts often signal evaluation mode.<strong> <\/strong>They\u2019re analytical, not emotional, which gives LLMs \u201cpermission\u201d to surface brands as options rather than responses to frustration.<\/p>\n\n\n\n<p>Many Solution Aware prompts (e.g. <em>\u201cbest tools\u201d<\/em> or <em>\u201ctop brands\u201d<\/em>) naturally fall into this neutral category, which likely contributes to the effect.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-different-ai-models-recommend-products-and-brands-at-different-rates-nbsp\">Different AI models recommend products and brands at different rates&nbsp;<\/h2>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" width=\"1578\" height=\"887\" data-src=\"https:\/\/beomniscient.com\/wp-content\/uploads\/AI-Model-edited.png\" alt=\"\" class=\"wp-image-6777 lazyload\" data-srcset=\"https:\/\/beomniscient.com\/wp-content\/uploads\/AI-Model-edited.png 1578w, https:\/\/beomniscient.com\/wp-content\/uploads\/AI-Model-edited-300x169.png 300w, https:\/\/beomniscient.com\/wp-content\/uploads\/AI-Model-edited-1024x576.png 1024w, https:\/\/beomniscient.com\/wp-content\/uploads\/AI-Model-edited-768x432.png 768w, https:\/\/beomniscient.com\/wp-content\/uploads\/AI-Model-edited-1536x863.png 1536w\" data-sizes=\"(max-width: 1578px) 100vw, 1578px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1578px; --smush-placeholder-aspect-ratio: 1578\/887;\" \/><\/figure>\n\n\n\n<p>Each AI model has its own algorithm, and they change frequently.<\/p>\n\n\n\n<p>As a result, <a href=\"https:\/\/beomniscient.com\/blog\/answer-engine-optimization\/\">AEO<\/a> isn\u2019t really a single channel. It\u2019s a collection of distinct platforms, each with different tendencies around when and how they mention brands.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-the-customer-journey-still-matters-in-ai-search\">The customer journey still matters in AI search<\/h2>\n\n\n\n<p>Overall, what does this research mean? How can you use it to better inform your organic growth strategy?<\/p>\n\n\n\n<p>First, it\u2019s clearly still important to understand the customer journey and the various questions, queries, and prompts that lie along it. LLMs still follow customer journey and intent, though it may truncate the journey at certain points, and users may be introduced to products earlier in the traditional funnel than once expected.&nbsp;<\/p>\n\n\n\n<p>Second, as expected, brand recommendations are most common when a user explicitly asks for solutions, products, or tools to solve their problems. This is no secret.&nbsp;<\/p>\n\n\n\n<p>As the industry discovers \u201cbest X\u201d listicles and comparison pages, what we find more surprising and interesting is how often brands and products show up in answers to questions that don\u2019t explicitly ask for products.&nbsp;<\/p>\n\n\n\n<p>A significant portion of the time, brands show up early in LLM outputs, even when users<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Express problems<\/li>\n\n\n\n<li>Ask neutral, evaluative questions<\/li>\n\n\n\n<li>Explore criteria rather than solutions<\/li>\n<\/ul>\n\n\n\n<p>This adds some extra interest to what <a href=\"https:\/\/beomniscient.com\/services\/generative-engine-optimization\/\">GEO<\/a> will mean moving forward. Winning in AI can go beyond ranking for \u201cbest tools\u201d queries. It\u2019s about earning presence across the full buyer journey, including moments where users aren\u2019t consciously shopping yet.<\/p>\n\n\n\n<p>From a content strategy perspective, this means you shouldn&#8217;t sleep on pain points, middle-of-the-funnel topics, how to content, or even definitional or high level education. Not only do they tie together a cohesive entity for your site and brand, driving topical authority and comprehensive topic coverage, but they also (sometimes) shake off solution stage brand recommendations in the process.&nbsp;<\/p>\n\n\n\n<p>The brands that show up most consistently in AI answers aren\u2019t just visible at decision time, they\u2019re woven into how LLMs explain problems, frame categories, and guide evaluation.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>More and more consumers are leveraging LLMs to find, evaluate,&#8230;<\/p>\n","protected":false},"author":51,"featured_media":6770,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[192,191],"tags":[],"wf_post_folders":[],"class_list":{"0":"post-6741","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-search","8":"category-research"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Do LLMs recommend brands even when users aren\u2019t shopping? 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[Research]\" \/>\n<meta property=\"og:description\" content=\"More and more consumers are leveraging LLMs to find, evaluate, and even purchase products.&nbsp; In 2025, we conducted research on buyer behavior in the\" \/>\n<meta property=\"og:url\" content=\"https:\/\/beomniscient.com\/blog\/llm-brand-recommendations-customer-journey\/\" \/>\n<meta property=\"og:site_name\" content=\"Omniscient Digital\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-16T21:11:02+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-17T17:13:51+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/beomniscient.com\/wp-content\/uploads\/Brand-Recommendations-Blog-Cover-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2048\" \/>\n\t<meta property=\"og:image:height\" content=\"1152\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Cate Dombrowski\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@beomniscient_\" \/>\n<meta name=\"twitter:site\" content=\"@beomniscient_\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Cate Dombrowski\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/beomniscient.com\\\/blog\\\/llm-brand-recommendations-customer-journey\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/beomniscient.com\\\/blog\\\/llm-brand-recommendations-customer-journey\\\/\"},\"author\":{\"name\":\"Cate Dombrowski\",\"@id\":\"https:\\\/\\\/beomniscient.com\\\/#\\\/schema\\\/person\\\/b45be551e75ad452519e7331d9405e1c\"},\"headline\":\"Do LLMs recommend brands even when users aren\u2019t shopping? 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