Why do consumer preferences matter in GEO?
Understanding consumer preferences has always been key for a brand to connect with its audience. With AI search now providing new ways for consumers to discover information, those consumer preferences are also shaping how visible brands are. Tools like ChatGPT and Google’s AI Mode reward the same qualities that users look for in real life: trust, clarity, transparency and reputation.
This article examines the findings from our various AI visibility reports across different industries and why being recognised for aligning with consumer priorities, both on-site & off-site, is now essential for standing out in search.
Why consumer preferences matter in GEO
AI search works differently from traditional search. Instead of showing a list of links, it pulls information from trusted sources and provides a single, conversational answer.
This is a shift that brands need to get ahead of. While SEO has mainly been about relevance and authority, Generative Engine Optimisation (GEO) brings something new to the table: confidence.
Confidence is derived from the signals that users rely on when choosing a brand. For example, our AI Visibility Report of UK Banks found that ‘security’, ‘customer service’ and ‘fee transparency’ carried more weight than neobank favourites like ‘UX’ and ‘speed of onboarding’ for AI when providing recommendations for business bank accounts.
In short, what users value has become what AI learns to value.
What consumer preference data reveals
Consumer preferences follow predictable but revealing patterns across different industries. Our AI visibility reports across the UK property & banking sectors revealed that the most valued consumer preferences broadly fell into three categories:
- Trust & transparency
- Value & clarity
- Experience & reassurance
For example, in Blue Array’s AI Visibility Report of UK Banks, neobanks such as Monzo and Starling scored highly for consumer satisfaction, but were less visible across AI search compared to legacy brands like Barclays and HSBC.
When surfacing citations for ‘business bank accounts’, ‘security’, ‘customer service’, and ‘fee transparency’ carried more weight than neobank favourites like ‘UX’ and ‘speed of onboarding.’ These themes reflect what AI models have been trained to interpret as indicators of reliability and long-term trust. For example, even though Starling led in ‘ease of use’ within consumer preference models, it appeared less frequently in queries where models prioritised ‘security’ or ‘support.’
This research underlines that AI visibility mirrors visibility around consumer preferences. If people can’t easily find or evaluate what they care about on your site or in wider sources, AI likely can’t either.
How AI understands and replicates consumer preferences
Large-language models (LLMs) – advanced AI programs that use deep learning to understand, generate, and process human-like text – are trained to replicate consumer preferences by analysing vast, multi-sourced datasets that capture consumer behaviour, feedback, and demographics. The trillions of data points used to train AI systems allow them to infer what constitutes a good answer, based on the recognised consumer preferences.
In practice, AI is not neutral. It simply reflects recognised patterns of human judgement. When the data points to consumers having preferences for, e.g. ‘trust’, ‘speed’ & ‘clarity’ when it comes to a particular service, those preferences then become embedded in the model’s internal weighting.
Recent studies reinforce this behavioural mirroring. For example, Li et al. (2025) demonstrated that LLMs can segment audiences based on expressed preferences, clustering users not just by demographics but by decision-making style (e.g. price-sensitive vs trust-led).
In summary, brands that communicate consumer-led information are more likely to be used within responses by AI systems.
Surfacing consumer preferences across content
In the era of AI search, every piece of brand content (off and on-site) is an opportunity to signal relevance and trust.
Product & service pages
These will be foundational to how AI models perceive your brand in relation to related consumer preferences. It’s important that brands use these pages to highlight key features relevant to your target market. For example, in our AI Visibility Report of UK Banks, highlights ‘security, service & trust signals as strategic gaps for Neobanks, stating “While they appeared to have been focused on price, UX, features, and “ease of use” when communicating their offerings, that left gaps in other areas such as security, customer service and support, which the models consider to be of importance in consumer preferences.” The most logical place for these brands to communicate their strength in these areas would be on their key product pages.
Brand & about pages
Credibility isn’t confined to just products and services. AI systems weigh how transparently brands communicate things such as expertise, experience, trustworthiness, etc. Pages like ‘About us’, ‘Reviews’ and ‘Case studies’ that include regulatory details, customer promises, team expertise, social proof and more, strengthen brand confidence for both users and AI search.
Informational content
Your blog, guides, and resource content play a much bigger role in GEO than many brands realise. These pages help AI systems understand why your products or services matter, not just what they are.
Ensure you map each piece of content to a real consumer question or concern. If you’re not answering what your audience actually asks, you’re leaving space for competitors to do it for you.
Informational content should bridge the gap between user curiosity and brand expertise. It’s one of the clearest ways to show both people and AI that your brand genuinely understands its customers, and that you’re a true authority in your category.
Off-site & reputation signals
What others say about your brand can have a huge impact on how AI systems perceive your brand. As we noted in our AI Visibility Report of UK Banks:
“AI tools aren’t aiming for fairness; they aim for confidence. That confidence comes from what’s published about a brand: its clarity, structure, and presence across credible sources. Brands that earn trust through indexable content, strong reputations, and third-party mentions will win in AI search.”
PR is a brand’s biggest lever when it comes to building an off-site narrative around consumer preference. We discuss this further in our AI Visibility Report of Property Brands:
“Brands that have been focusing their marketing efforts on great brand storytelling and PR will do well when it comes to being discovered via AI Search. As the theory of how AI tools ‘learn’ about a brand and its product offering, this is where PR plays a crucial role. Brands need to focus on telling a comprehensive story of their mission to their customers, their products, their USPs and what they essentially want to be known for. A brand that garners authority is a brand that earns trust, and this is the strategy brands must play.”
A framework to build a GEO strategy around consumer preferences
A successful GEO strategy aligns what consumers value with how AI interprets content. Our five-step framework underpins this alignment:
- Discover: Identify which attributes customers care about most in your category
- Audit: Map how those attributes are represented across your content and off-site ecosystem
- Optimise: Integrate customer preferences into key on-site content, social activity, and PR campaigns for off-site visibility
- Monitor: Track AI citations and mentions across multiple models to gauge alignment
- Iterate: Update content regularly as both consumer expectations and model behaviour evolve
This framework helps bridge the gap between SEO optimisation and AI interpretation.
Conclusion
Consumer preferences have always driven business strategy. What’s changed is that AI systems now read, interpret, and act on those preferences.
Brands that make their value signals explicit will become the most confidently recommended voices in AI search.
Want to understand how your brand performs in AI search and whether you’re surfacing the right consumer signals?