GEO for Ecommerce
Shopping assistants inside ChatGPT, Gemini and Amazon's own AI features are starting to recommend specific products and merchants directly. For ecommerce, GEO overlaps heavily with structured product data — the same feeds that power Google Shopping increasingly feed AI shopping recommendations too.
Tactics that matter most
- Implement full Product schema (price, availability, reviews, brand) on every product page — this is the primary machine-readable signal AI shopping features draw on
- Keep pricing and stock status accurate in real time — AI answers quoting stale prices or out-of-stock items erode trust and get corrected against you
- Build genuinely useful buying-guide content ("best X for Y use case") — these pages get synthesized into AI comparison answers more than product pages alone
- Encourage and display real customer reviews with Review/AggregateRating schema, since review volume and rating are commonly cited signals
- Allow Amazonbot and other shopping-relevant crawlers if you want visibility in AI-assisted shopping features beyond your own site
- Write specific, sensory product descriptions rather than manufacturer boilerplate — generic copy is the easiest thing for an AI model to skip in favour of a page that actually describes the product
Product schema is doing more work than you think
Structured product data used to be mostly a Google Shopping concern. It's now the backbone of AI shopping too — when Perplexity or a shopping-focused assistant compares three water bottles, it's pulling price, availability, brand and rating straight out of your Product and AggregateRating markup, not parsing prose. A product page with clean schema but thin copy will often out-perform a beautifully written page with none, at least for the head-to-head comparison prompts that drive purchase decisions.
Buying guides earn citations product pages can't
A single product page answers "tell me about this item." A buying guide answers "which one should I buy," which is the far more common shopping prompt. Guides that compare options honestly, state trade-offs, and recommend a pick for specific use cases ("best for small kitchens," "best under £50") get lifted into AI comparison answers repeatedly, because they've already done the synthesis work the model would otherwise have to do itself.
Freshness matters more here than almost anywhere else
Stock status and pricing decay fast, and an AI assistant that quotes a price you no longer honour, or recommends a product that's out of stock, creates a bad experience it will eventually correct against you. Keep feeds and on-page data synced in near real time where you can, and treat stale product data as a trust problem, not just an inventory one.