By Steve Merrill, Founder of WRKNG Digital | October 14, 2026
The ten attributes AI shopping assistants weigh most are title, description, category, price, availability, identifiers, core attributes, reviews, shipping terms, and variants. This expands our earlier breakdown of the product attributes AI weighs most with updated detail and two fields stores keep missing. Here's the full checklist.
1. Clear Product Title
The title carries the most meaning a model reads. Name the actual product and its defining trait in plain words. Vague or slogan-stuffed titles get matched to the wrong queries or none at all.
2. Detailed Text Description
A model lifts clean sentences from your description. Write what the product is, who it's for, and why it works. Hype gives a model nothing to quote.
3. Accurate Category
Wrong category data buries your product in the wrong bucket. Map to standard categories so AI matches it to the right shopper intent. Google's product data spec defines the fields (Google Merchant product data spec).
4. Price and Currency
Comparison answers drop products they can't price. Keep price current and correctly formatted with currency. This is a common silent exclusion.
5. Availability
Stale availability breaks trust and collapses recommendations. Sync stock in real time. A model favors sources it can rely on in the moment.
6. Product Identifiers
GTINs, MPNs, and SKUs let AI confirm exactly what you sell. Consistent identifiers raise match confidence. Missing ones leave the model guessing.
7. Core Attributes
Size, color, material, and fit belong in structured fields as text. These are the details shoppers ask AI about by name. Buried in an image, they're invisible.
8. Reviews and Ratings
This is one of the two fields stores miss most. AI weighs social proof it can verify. Review and AggregateRating schema help when you have real customer reviews, per Google's guidance (Google review snippet docs).
9. Shipping and Return Terms
The second commonly missed field. AI and agents factor cost and policy into recommendations. State shipping cost, timing, and returns in readable text. Buried terms cost you the mention.
10. Structured Variant Data
Variants have to be clear and structured so AI recommends the right configuration. A wrong-variant recommendation is a failure shoppers blame on you. Clean variant data keeps it correct.
How We Chose This List
Ordered by weight in AI recommendation decisions, based on patterns across our Shopify audits. Reviews and shipping terms earned their spots because stores overlook them and AI clearly uses them.
FAQ
What's new in this expanded checklist?
Reviews, ratings, and shipping terms get full treatment here. They're the attributes stores most often leave out of the feed.
Which attribute matters most?
The title, then the description. They carry the meaning AI uses to match your product to a shopper's question.
Do reviews really affect AI recommendations?
Yes. AI weighs verifiable social proof. Review schema on real customer reviews gives a model a signal it trusts.
How do I audit all ten attributes?
Check your feed field by field against this list, then validate your schema. Blank attributes and stale data are the usual culprits.
Want a full attribute audit of your product data? Get your AEO readiness audit from WRKNG Digital.
