By Steve Merrill, Founder of WRKNG Digital | September 10, 2026
I've audited a lot of Shopify catalogs this year. The same fields are missing over and over. Not exotic ones. Basic data an AI assistant needs to recommend you.
Fill these eight and you close most of the gap between invisible and recommended. Here's the list.
Which product data fields do most Shopify stores miss?
The ones that prove a fit and lower risk. Material, dimensions, identifiers, structured reviews, and more. Align them to the Google Merchant product data specification. Here are the eight I see missing most.
1. Material or composition
Often vague or absent. "Premium fabric" isn't a value a model can use. Name the real material.
This field proves a fit for people who care about what a product is made of.
2. Dimensions and weight
Frequently left blank or trapped in an image. A model can't answer a size question without them.
Put real numbers in labeled fields.
3. GTIN or MPN
The identifier that ties your product to external data. Missing on most stores I check.
Add the right ID so your product connects to proof beyond your own page.
4. Structured reviews
Plenty of stores show reviews as design but never mark them up as data. A model can't read them.
Use structured ratings tied to the product.
5. Compatibility or fit info
Does it work with, fit, or suit a specific case? Often missing, which blocks a match to the buyer's request.
State compatibility plainly and in your data.
6. Accurate availability
Availability that doesn't reflect real inventory. A model won't recommend what it can't confirm is buyable.
Sync availability to live stock.
7. Complete, current price
A price missing from the data or out of sync with the feed. It drops you from budget questions.
Keep it live in your Product schema.
8. A factual description
A description made of adjectives with no quotable fact. A model needs a clean sentence to cite.
Lead with the fact. A WRKNG audit flags which of these eight are empty on your best sellers.
Further reading
- Google Merchant product data specification
- Schema.org Product reference
- Google review snippet structured data
Frequently Asked Questions
What product data fields does AI need on Shopify?
The fields that prove a fit and lower risk: real material, dimensions and weight, a GTIN or MPN, structured reviews, compatibility info, accurate availability, a current price, and a factual description. Most stores are missing several of these.
Why is missing material data a problem?
Because a vague value like premium fabric tells a model nothing it can match to a request. Buyers ask about what a product is made of, and without a named material in your data, an assistant can't confirm the fit and skips you.
Do I need GTIN or MPN on my products?
Yes, in most cases. A product identifier ties your item to external data and proof, following the Google Merchant product data specification. Without it, your product floats alone with no outside validation for a model to lean on.
How do I find which fields I'm missing?
Audit your best sellers against the fields AI needs, or run a WRKNG audit that flags empty and vague fields automatically. Start with revenue-driving products, since that's where missing data costs you the most recommendations.
Want to know if AI assistants can actually find and recommend your store? Get a free AI-visibility read on your Shopify store at WRKNG Digital. We show you exactly what ChatGPT, Perplexity, and Google AI see when they look at your products.
