How category framing changes which brands AI recommends

A study of 12 athletic apparel brands in the U.K. over seven days with 14,140 API runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews found that changing the category word in a prompt—from 'athleisure' to 'athletic footwear'—dramatically shifted which brands LLMs recommended. New Balance jumped from 1% to 90% recommendation rate under the footwear framing, while lululemon dropped from 90% to 0% under the same shift, showing that category coding from Knowledge Graph descriptions and third-party content determines AI visibility more than brand recognition alone.

SEO https://searchengineland.com/library/seo » How category framing changes which brands AI recommends The language your customers use to search can shape your brand's AI visibility. Learn what new research reveals about category coding. Most brands approaching AI visibility ask the wrong question: How do we get stronger as an entity so that LLMs https://searchengineland.com/llm-optimization-tracking-visibility-ai-discovery-463860 recommend us more? In entity SEO https://searchengineland.com/guide/entity-first-content-optimization , we tend to say, “Build the Knowledge Graph, add schema, and get more press.” But that logic assumes the LLM is evaluating the brand and deciding whether it’s good enough to recommend for any query related to what the brand sells. The LLM evaluates the query and matches it against whatever category associations it has built for the brand from third-party content. The difference matters enormously in practice. As we’ve seen in multiple scenarios, recognition isn’t the same as recommendation https://searchengineland.com/brand-ai-recommendation-set-477229 . So being a recognized brand isn’t synonymous with being a strong brand. What matters is whether the category your customers are using to search for you matches the category the LLM has coded you into. What the data showed João da Silva and I conducted a study of 12 athletic apparel brands in the U.K. over seven days, with 14,140 API runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. We tested the same brands using two different category framings: athleisure and athletic footwear. After looking at the results derived from co-mentions https://searchengineland.com/co-mentions-ai-recommendation-gap-479829 and putting numbers on the impact of framing on category recognition for LLMs, we took the test one step further and changed the category register in the prompt. The results were symmetric to a degree that rules out noise: Brand | Knowledge Graph KG score | Athleisure rate | Footwear rate | Δ | Verdict | |---|---|---|---|---|---| | New Balance | 64,235 | 1% | 90% | +89 | Jumped footwear-coded | | Nike | 25,996 | 77% | 90% | +13 | Small shift footwear-coded with strong athleisure co-mentions | | Alo Yoga | 3,062 | 63% | 0% | -63 | Dropped athleisure-coded | | lululemon | 810 | 90% | 0% | -90 | Dropped athleisure-coded | | Sweaty Betty | 751 | 9% | 0% | -9 | Stable | | Reebok | 665 | 1% | 20% | +19 | Small shift | | Outdoor Voices | 455 | 26% | 0% | -26 | Small shift | | Rhone Apparel | 400 | 5% | 0% | -5 | Stable | | Varley | 381 | 6% | 0% | -6 | Stable | | TALA | 356 | 5% | 0% | -5 | Stable | | Gymshark | 277 | 37% | 0% | -37 | Dropped athleisure-coded | | LNDR | 2 | 0% | 0% | 0 | Stable | Notes : - New Balance goes from 1% to 90%. - lululemon goes from 90% to 0%. The variation is approximately 0.9 points in both directions simultaneously. We’re not talking about correlation here, but a controlled observation: what happens when we change only one variable — the category word in the prompt. Be the brand AI recommends. See your AI visibility https://www.semrush.com/ai-seo/overview?utm campaign=ic sel 0101ai&utm source=searchengineland.com&utm medium=overlay&onboarding=off See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends. Why this happens: Category coding Nike, New Balance, and Reebok share the exact same Google Knowledge Graph KG description: “Footwear company,” so all three are recognized perfectly by every LLM we tested. From an entity standpoint recognition , they start from an identical position. However, their behavior under different category framings isn’t identical at all. The reason is what the paper formalizes as category coding: the combination of the KG description field and the third-party content corpus that has accumulated around a brand in a given category. The KG description anchors a brand to a category in the model’s representation impacts recognition . The third-party corpus — articles, reviews, editorial comparisons, and roundups — fills in the detail of what that category association actually looks like impacts recommendation . Looking at the example from New Balance New Balance’s KG description says “Footwear company,” and the third-party corpus that has accumulated around it corroborates the category by focusing on topics related to running shoes, performance footwear, and athletic training. When a user asks about athleisure brands, the model doesn’t find New Balance in that corpus because there is no third-party association. But it does find lululemon, Alo Yoga, and Gymshark: all brands whose corpus is built from fashion publications, lifestyle editorial, and activewear roundups. When we changed the query to athletic footwear, the retrieval flipped: New Balance is suddenly in the right corpus, and lululemon is not. The model itself can’t and isn’t making a judgment about brand quality or belonging. What an LLM does is pattern-match a query category against a content category. If those two things align, the brand surfaces. If they don’t, it doesn’t, regardless of how established the brand is. So, can you just recode your KG description? Some brands reading this will consider the obvious shortcut: Change the KG description. If “Footwear company” is anchoring you to the wrong category, recode it to “Apparel company,” and the problem is solved. However, the KG description is only half of what determines category coding. The other half is the third-party content corpus that has accumulated around your brand, and that doesn’t change because you updated a field in the Knowledge Graph. If your entire external content history is performance footwear, running, and athletic training, changing the description gives the model a new anchor with nothing attached to it. The corpus still says what it always said. The corrective lever is third-party content investment in the specific category framing your customers are using: in the publications the model retrieves from, alongside the brands that already define that space. The KG description can support that work once the corpus exists. What this means for your GEO strategy The standard GEO https://searchengineland.com/what-is-generative-engine-optimization-geo-444418 advice is to strengthen your entity: a consistent name, clean schema, a strong About page, and more press coverage. That advice is correct for getting recognized and even recommended within the brand’s coded category, but it isn’t sufficient for getting recommended in adjacent category queries. What determines recommendation in adjacent categories is whether the third-party content corpus around your brand matches the category framing your customers are actually using. The questions worth asking about any brand are: - Are we visible in AI? - What category has the LLM coded us into? - Is that the category our customers are querying? If a brand is strong in one category, but its customers are increasingly using adjacent category language to search for example, athleisure instead of sportswear, or performance wellness instead of fitness , and the brand’s third-party corpus hasn’t kept pace with that language shift, the brand will be invisible in exactly the queries customers are using. Nike is the study’s clearest positive case, surfacing in both athleisure 77% and athletic footwear 90% queries, despite being KG-coded as a footwear brand. The reason is that Nike has accumulated enough athleisure-coded third-party content, including editorial coverage in fashion publications, inclusion in activewear roundups, and co-mentions with other athleisure brands, to register as category-eligible in both framings. It built a sub-stream in the adjacent category that New Balance didn’t. If AI can’t find you, customers won’t either. See your AI visibility https://www.semrush.com/ai-seo/overview?utm campaign=ic sel 0102ai&utm source=searchengineland.com&utm medium=overlay&onboarding=off Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions. What is the audit question everyone should be asking? Before investing further in entity optimization, it’s worth running a simple diagnostic: Take the five or six different ways your customers might phrase a category query for what you do, and test each one across two or three LLMs. Note which formulations surface your brand and which don’t. For the ones that don’t, the questions to ask are: - Does third-party content about your brand actually use that language? - Are you being written about in publications that cover that category? - Are you appearing in editorial roundups that use that phrasing? If the answer is no, you know where to start: getting into the external conversations that speak the language of that query. Closing that gap means becoming a participant in the category comparison content that defines who belongs in that space. This article is based on findings from “ The recognition-recommendation gap: Empirical evidence that category coding, not knowledge-graph strength, determines brand visibility in generative AI output ,” co-authored with João da Silva and published open access on Zenodo. Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.