Your products in AI buying recommendations. Visible where customers ask.
ChatGPT and other AI tools are increasingly giving buying recommendations. We make sure your products get recommended.
- Product feeds optimised for LLMs
- Context-rich data that AI understands
- Measure visibility on real prompts
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Erkend groei
FD Gazelle winnaar -
Kwaliteit
Emerce100 notering -
Specialisme
Specialisten in-house -
Omvang
Mediabudget onder beheer
Why outsource to Brandfirm?
GEO shopping requires a new way of thinking about product data. Not just technical feed optimisation, but also adding context that AI tools understand. We combine years of experience with e-commerce feeds with a head start in AI applications. While others are still waiting, we're already building visibility in the search interface of tomorrow.
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Emerce 100Top 10 best digital marketing agencies -
Fonk 150Best full service agencies (medium size) -
Erasmus & nlgroeitTop 250 growth companies 2021 & 2022 -
FD Gazellen7 years in a row FD Gazellen from ’16 t/m ‘23
Your products in AI buying recommendations. Visible where customers ask.
These are clients we're already growing with.
How AI shopping is changing the rules of product visibility
More and more consumers are asking AI tools for buying advice. Not through a search bar with filters, but with a simple question: "What's a nice yellow jacket?" The difference is in what happens next. The LLM knows the user. Knows the style, the budget, the preferences from previous conversations. And matches that context to products that fit.
Traditional product feeds are built on keyword matching. Title, colour, size, price. Sufficient for Google Shopping, but not for AI. An LLM looks for products that match what it already knows about the user. Is your yellow jacket suited for someone with a business style and a high budget? Or for someone who's into outdoor and survival? If that context isn't in your feed, the LLM can't match.
GEO shopping is about enriching your product feed with context-rich data. Not just what you sell, but for whom and why. That means determining per product or product category which use cases, audiences, and preferences are relevant. And structuring that information so AI tools can reason with it.
Most online stores aren't doing this yet. Their feeds are copies of what they send to Google. That's a missed opportunity, because the shift to AI shopping is moving fast. Those who invest in context-rich product data now are building a lead that's hard to close.
How we approach it in four steps
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We start with a thorough analysis of your current product feed. What data is already there? Where are the gaps? And how do your products currently perform in AI tools? That gives us the starting point.
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Per product or product category, we determine the relevant context. Who is this product for? What situation does it fit? Which preferences and budgets match? We structure that information for AI.
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We enrich your product feed with the context data. Technically correct, so AI tools can read and interpret the information. No loose text blocks, but structured data that LLMs understand.
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We measure visibility on real prompts. Do your products appear in AI answers? For which questions do they, and for which don't they? We use those insights to continuously improve.
Still have questions? We'll keep it brief.
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ChatGPT matches products to users based on context, not just keywords. The AI knows the user's preferences, style, and budget from previous conversations. It then matches that information to product data. Products with rich context information in their feed, such as target audience, use case, and price segment, have a greater chance of being recommended than products with only basic specifications.
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A Google Shopping feed is built on keyword matching. Title, colour, size, price. Sufficient for being found on exact search terms. An AI-optimised feed also contains context information about the target audience, use case, and price segment of each product. Where Google searches on attributes, an LLM looks for products that match what it already knows about the user. Without that context, the AI can't match.
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Most product feeds contain only technical specifications and are optimised for keyword matching. AI shopping works differently. An LLM tries to match products to a user's context, not to a search term. If your feed doesn't describe who a product is suited for, what occasion it fits, or what problem it solves, the AI lacks the information to recommend your product.
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Adding context means determining per product or product category which target audience, use case, and preferences are relevant. Think of style, budget, occasion, and application. You structure that information in your feed so AI tools can reason with it. Brandfirm analyses your current product data, builds context profiles, and enriches your feed with structured data that LLMs understand and can match to user profiles.
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Visibility in AI shopping is measured by monitoring whether your products appear in answers to relevant prompts. This differs from traditional metrics like impressions or clicks. You test with real purchase queries and analyse for which questions your products are recommended and for which they're not. Those insights determine where your feed needs to be enriched to increase visibility.
