When a shopper asks ChatGPT "what are the best waterproof hiking boots under $200 for wide feet?", the AI doesn't browse your store the way a person does. It scans structured data and text for products that explicitly match four constraints: waterproof, hiking, under $200, wide fit. If your catalog doesn't state those attributes in a machine-readable way, your boots are invisible — filtered out before the shopper ever sees them.
This guide is about doing that at catalog scale. Classic ecommerce SEO optimized for keywords like "hiking boots." AI shopping optimizes for attributes and relationships, so an agent can confidently say "this is exactly what the user asked for." Below are five steps: maximize attribute density, add complete `Product`/`Offer` schema, structure product Q&A, keep your feed clean, and monitor which SKUs actually win the AI card.
Key takeaways
- AI agents filter products by explicit attributes, not marketing adjectives. "Comfortable winter jacket" is invisible to a shopper searching for "waterproof down parka, 700 fill, black." - Fill every relevant field. Push attribute completion toward 95%+ across material, color, size, fit, GTIN, and use case — the fields agents match against. - Structure your Q&A. Sizing, care, compatibility, and returns belong in `FAQPage` schema and clean on-page blocks, not buried in a paragraph, so agents can lift the answer. - Complete `Product`/`Offer` schema — including price, availability, `AggregateRating`, and `MerchantReturnPolicy` — is how the model retrieves accurate facts instead of hallucinating them. - GEOly's catalog and Share of Card analysis show which SKUs get an AI card, which attributes are missing, and where retailer quotes intercept your traffic — so you fix causes at scale.
Step 1: Maximize attribute density
AI models filter on specific attributes, not vague marketing language. A title like "comfortable winter jacket" tells an agent almost nothing; a shopper looking for a "waterproof down parka, 700 fill, black" will never match it. The gap between those two is attribute density, and closing it is the highest-leverage catalog change you can make.
Fill every field a shopping feed and structured-data spec offers. Be specific: material is "100% recycled polyester," not just "polyester"; pattern is "geometric," not "cool design"; occasion is "business casual, wedding guest." Populate size, color, fit, GTIN, brand, and use case on every SKU. Aim for 95%+ attribute completion across the catalog, and treat empty fields as lost recommendations.
Gotcha: specificity has to be accurate, not padded. Stuffing irrelevant attributes to look complete confuses matching and can misfile a product into queries it can't satisfy. Precise beats verbose.
Step 2: Implement product Q&A schema
Shoppers ask agents concrete questions — "can this be machine washed?", "does it fit wide feet?", "what's the return window?" — and the catalog that answers them explicitly wins the recommendation. Don't bury those details in a description paragraph; structure them so an agent can extract the answer directly.
