Catalog Quality Score: What It Is and How to Improve It

A catalog quality score is a measure of how complete, valid, consistent and fresh a product catalog is. Its job is practical: turn a catalog audit into one readiness signal and a prioritised list of field-level issues, so a retailer knows what to fix first and can see whether the catalog is getting better over time.

The number matters less than the list under it. A score of 72 tells you little. The fact that 340 products are missing GTINs and 1,100 have descriptions under 30 words tells you exactly where the work is.

The usual dimensions:

• Completeness. Are the required and category-relevant attributes filled in?
• Validity. Are values well formed: numeric prices, valid identifiers, correct formats?
• Consistency. Is the same product described the same way across fields, page, schema and feed?
• Freshness. Are price and availability current?
• Images. Do they meet size and format expectations?
• Identifiers. Are GTIN, MPN and brand present and usable for matching?
• Taxonomy. Do products map to a recognised category taxonomy such as Google Product Categories?
• Merchant data. Are shipping, returns and seller attributes present where a channel requires them?

Field checks roll up to a product score, and product scores roll up to the catalog score. Issues are weighted by severity: a missing field a destination requires outranks a recommended attribute.

The two answer different questions:

• Catalog quality score. How good your catalog data is, field by field and product by product.
• AI readiness score. How ready a product page is for AI shopping overall: the data, plus page content, schema, crawlability and whether the page answers the questions shoppers ask. See the free AI readiness report.

A strong catalog is a prerequisite for a strong readiness score, and readiness also depends on the page content and schema around the feed.

In order of impact:

1. Fix the highest-severity issues. Missing identifiers, invalid prices and missing availability, especially where a destination requires them. These remove products from channels outright.
2. Complete the attributes that matter. Not every field for every product. Start with the attributes your shoppers filter on and ask assistants about, for the products that drive your revenue. See product data enrichment.
3. Write descriptions that answer questions. A valid 20-word description passes the format check and fails the shopper.
4. Keep price and availability fresh. Stale stock hurts the score and the shopper.
5. Re-measure. Track the score over time so improvement is visible and regressions are caught early.

Across a real catalog this is steady work, and a score on its own tends to become a backlog nobody has time for. That is the gap Paz fills. A Paz expert reads the issue list, decides what to fix first based on your priorities and the shoppers you sell to, prepares the changes from your sources, and checks them against agreed publishing guidelines. Approved fixes publish through supported store and feed connections, and the next measurement shows what cleared. See the product content service.

Use the score for prioritisation and trend. Whether products appear in AI answers is a separate measurement, covered by AI visibility and found rate. The two move together more often than not, because assistants recommend from the data the score grades, and reading them side by side is how you see which fixes paid off.