GEO for E-commerce
How AI engines choose and recommend e-commerce brands today.
We run real buyer prompts against ChatGPT, Gemini, Perplexity with web search on, and record which e-commerce brands they name and which sources they cite. This page is one live view into that dataset - it updates as new probes land.
What buyers ask AI
Real questions people put to AI assistants about e-commerce brands.
What AI actually recommends
Probed from a Spain context · July 18, 2026Live answers per engine - exactly what each one named, in order.
| Prompt | ChatGPT | Gemini | Perplexity |
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| Best online mattress brand |
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| Best sustainable clothing brands |
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| Best online furniture store |
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| Best direct-to-consumer coffee brands |
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| Best online store for running shoes |
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AI ranking factors
Where these engines actually look before naming anyone - citation share by source type.
Shares are computed from the sources engines actually grounded on in our shopping probes - not an editorial guess. Unknown domains land in “other” and get reviewed monthly.
How e-commerce brands win AI visibility
What the data above means in practice - and the playbook that follows from it.
E-commerce is where AI answers most directly replace revenue-bearing clicks: a shopper who asks ChatGPT for “the best online mattress brand” gets a finished shortlist, and brands outside it never see the visit. The evidence engines assemble that shortlist from is broader than any other vertical we track - editorial reviews (Wirecutter-style testing sites), aggregated customer sentiment (Trustpilot, BBB), community threads (Reddit’s “what do you actually use” genre), YouTube reviews, and increasingly the engines’ own shopping infrastructure fed by product feeds. Winning here means being present across evidence types, because different engines weight them very differently.
The product-feed layer is the newest and least understood lever. ChatGPT’s shopping surfaces pull structured product data - titles, prices, availability, review counts - and brands with clean feeds and Product schema get rendered as actual product cards, not just text mentions. This is mechanical work most DTC brands haven’t done for AI: consistent GTINs, complete Merchant Center feeds, Product/Offer/AggregateRating markup on every PDP. When the answer format is a shopping shelf, structured data is not an SEO nicety - it is the ticket to existing at all.
Ship clean product feeds + schema
Complete Merchant Center feeds, consistent GTINs, Product/Offer/AggregateRating markup on every product page - the entry ticket to AI shopping surfaces.
Build review-platform trust
Trustpilot and BBB profiles active and responded-to. Engines cite aggregate ratings and quote recurring themes from review text.
Earn community proof
Be genuinely present where your category is discussed on Reddit. One organic high-vote recommendation compounds for years in AI answers.
Target testers, not compilers
Pitch publications that actually test products (methodology, photos, updates). Engines increasingly cite demonstrable testing over affiliate roundups.
Publish answer-shaped category content
Buying guides that honestly answer “best X for Y” - including competitors - make your domain part of the material engines summarize.
Track your product prompts
Run your category prompts monthly per engine, note which stores get named and which sources earned it, and close the gaps.
FAQ
Answers to common questions about this report.
What is GEO for e-commerce?
Can ChatGPT really recommend my products?
Which sources do AI engines cite for shopping?
How is this data collected?
How can my store get started?
See where your store appears across all AI engines.
The data above is the industry view. Run a free scan to get the same report for your store: where you show up, who outranks you, and what to fix first.
