seoPublished on July 21, 20265 min read

Brand Categorization in AI: How Search Language Determines Recommendations

New study reveals that how customers phrase their searches determines whether LLMs recommend your brand — not the strength of the brand itself.

AI SEOLLM OptimizationMarketing DigitalSEOInteligência ArtificialVisibilidade de MarcaChatGPTGoogle AI Overviews

Bitclever AI Research

Author: Bitclever AI Research ## Executive Summary A recent study of 12 sportswear brands in the UK, involving 14,140 API runs across five generative AI platforms, reveals that the category framing used by customers in their searches is decisive for brand visibility in AI responses. The research, conducted by João da Silva and presented on Search Engine Land, challenges the traditional approach to LLM optimization based solely on strengthening brand entity signals. ## What Happened The study analyzed tested 12 sportswear brands using two distinct category formulations — "athleisure" and "athletic footwear" — to assess how different semantic framings influence recommendations generated by language models. The study spanned seven consecutive days and involved 14,140 API calls distributed across five leading generative AI engines: ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. The methodology relied on co-mention analysis — that is, how frequently a brand is mentioned alongside a given category in the responses generated by LLMs. The results showed that the same brand can achieve radically different levels of visibility depending on which of the two categories is used to phrase the question, even when the brand sells products that theoretically fit both categories. According to the authors, this finding challenges a common assumption in the entity SEO industry — namely, that LLMs assess a brand's strength and credibility before deciding whether to recommend it. In reality, the model compares the category implicit in the user's question with the category associations it has already built about the brand from third-party content — not with the brand itself. ## Why This Matters This study introduces a critical distinction that many marketing and SEO teams have yet to incorporate into their strategies: recognition is not synonymous with recommendation. A brand can be widely recognized by LLMs — appearing in multiple responses, mentioned frequently — without this translating into actual recommendations when the customer uses a category phrasing different from the one the model has associated with the brand. This finding has profound implications for how we think about optimization for generative AI engines (often referred to as AI SEO or LLM optimization). If the model's internal categorization doesn't match the actual language consumers use in their searches, even strong, well-established brands can become invisible in relevant purchasing scenarios. Furthermore, the study reinforces that building Knowledge Graphs, implementing schema markup, and securing media coverage — tactics traditionally recommended in entity SEO — may not be sufficient on their own. It is necessary to actively understand and influence how third-party content categorizes the brand, since these are the external signals LLMs use to build their category associations. ## Business Impact For organizations that depend on digital channels for lead generation and sales, this study suggests several immediate practical implications: **Audit of current categorization**: Companies should investigate how LLMs currently categorize their brand, comparing that categorization with the actual language used by their customers during the discovery and consideration phases of the purchase journey. **Language alignment across channels**: There is a need to ensure consistency between the terminology used on the corporate website, in third-party content (press, reviews, comparisons), and the language actually searched by end users. **Co-mention monitoring**: The ability to track how — and under which categories — the brand is mentioned in generative AI responses becomes a critical competitive intelligence skill, analogous to traditional rank tracking in classic SEO. **Risk of selective invisibility**: Brands that invest heavily in one positioning category (e.g., "technical footwear") may be completely absent from queries related to adjacent categories (e.g., "athleisure"), even when they have relevant products for both. These dynamics particularly affect sectors with overlapping categories — fashion, technology, consumer goods — where the boundaries between product subcategories are often ambiguous for both consumers and the AI models themselves. ## Bitclever Perspective At Bitclever, we closely follow the evolution of the digital discovery landscape, recognizing that optimization for generative AI engines represents an emerging discipline distinct from traditional SEO. This study confirms a trend we have observed in our digital consulting projects: the need to map not only a brand's online presence, but also how that presence is interpreted and categorized by AI systems. We help companies conduct in-depth diagnostics of their visibility on platforms such as ChatGPT, Gemini, Perplexity, and Google AI Overviews, identifying misalignments between the category language used by customers and the category coding assigned by the models. This work combines technical SEO skills, semantic content analysis, and third-party content strategy — areas where Bitclever has well-established expertise. Rather than applying isolated tactics such as schema markup or mention generation, we advocate a holistic approach that starts with genuine research into customer language, followed by an audit of how that language does — or does not — translate into the category associations already existing about the brand within the content ecosystem indexed by LLMs. Only from this diagnosis is it possible to design effective content and digital PR strategies to correct any misalignments. ## Conclusion The study presented demonstrates that visibility in generative AI does not depend exclusively on a brand's strength or reputation, but rather on the alignment between the category language used by consumers and the categorization that language models have already built about that brand from third-party content. For businesses, this means that AI optimization strategies need to go beyond generic brand entity strengthening, incorporating rigorous analysis of customers' actual search language and how that language is — or isn't — reflected in the responses generated by leading generative AI engines. As these platforms become increasingly relevant discovery channels, organizations that invest early in understanding this categorization dynamic will be positioned for significant competitive advantage.

Sources

https://searchengineland.com/category-framing-brands-ai-recommends-482715

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Brand Categorization in AI: How Search Language Determines Recommendations

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