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

A Catalog Quality Score is an internal measure of how complete, valid, consistent, and fresh a product catalog is. Its job is practical: turn a catalog audit into a single readiness signal and a prioritized list of field-level issues so a retailer knows what to review first.

Paz computes and displays catalog validation and quality scores as all-tier infrastructure, then surfaces the field-level issues behind the score so teams can prioritize catalog work.

The dimensions a Catalog Quality Score usually checks are signals of catalog completeness and destination readiness:

• Completeness. Whether required attributes are filled in across products.
• Validity. Whether values are well formed, such as numeric prices, valid identifiers, and correct formats.
• Consistency. Whether the same product is described the same way across fields and channels.
• Freshness. Whether price and availability are current, not stale.
• Image quality. Whether images meet size and format expectations.
• Identifiers. Whether GTIN, MPN, and brand are present and usable for matching.
• Taxonomy. Whether products map to a recognized category taxonomy such as Google Product Categories.
• Merchant data. Whether shipping, returns, and seller attributes are present where a channel requires them.

The two scores are related but 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 or store is for AI shopping overall, which includes catalog quality plus page-level and structural factors. See AI readiness.

A strong catalog is a prerequisite for a strong readiness score, but readiness also depends on factors a catalog feed alone does not capture, such as server-side rendering and crawlability.

A Catalog Quality Score rolls up in layers:

1. Field checks. Each field on each product is checked against the rules for its dimension.
2. Product score. Field checks roll up to a score for that product.
3. Catalog score. Product scores roll up to a catalog-wide score.

The single number is useful for tracking trend over time, but the actionable part is the field-level issue list underneath it. That list is what a team works through. Paz surfaces those field-level issues so the score is tied to specific, fixable problems.

Not every issue matters equally. A missing field marked required by a destination is more urgent than a recommended attribute, so issues are prioritized by severity.

Remediation in Paz runs through the catalog optimization workflow:

1. Propose. The optimizer suggests a fix for the flagged field.
2. Preview. A person reviews the proposed change.
3. Approve or reject. The person decides whether it ships.
4. Revert. If a change does not help, it can be rolled back.

This keeps a human in control of the catalog and avoids automated changes a merchandiser did not review. See AI Catalog Management for the broader workflow.

AI shopping systems depend on product data that they can parse and keep current. Fixing missing attributes, invalid identifiers, and stale availability improves catalog readiness.

A Catalog Quality Score pairs with AI Shopping Visibility: the score prioritizes internal catalog work, while visibility monitoring observes what appeared on supported surfaces. See also AI Product Found Rate.

To improve a Catalog Quality Score, in order of impact:

1. Fix the highest-severity issues. Review missing identifiers, invalid prices, and missing availability first, especially where a destination marks those fields as required.
2. Enrich for completeness. Fill recommended attributes and write descriptions that answer shopper questions. See product data enrichment.
3. Keep price and availability fresh. Stale availability hurts both the score and the shopper experience.
4. Use optimizers with review. Let the optimizer propose fixes, preview them, and approve what ships. See product feed optimization for AI.
5. Re-run the audit. Track the score over time so improvement is visible and regressions are caught.

For a practical walkthrough, see the feed optimization guide and run a free AI-readiness report.

Use the score as an internal readiness and prioritization signal, then use field-level issues to guide the work. Third-party platforms independently determine acceptance, inclusion, placement, and recommendations.