Answers

What is catalog quality scoring?

Catalog quality scoring measures how complete, consistent, and usable product data is for discovery, conversion, marketplaces, and AI answers.

Definition

Catalog quality scoring is the instrument that converts a catalog's quality into a repeatable number — a weighted, per-category composite of checks such as required-attribute coverage, value validity, and duplication, computed the same way every run so scores are comparable over time. The score is not the quality; it is a deliberately lossy summary of it, designed to be tracked, compared, and used to trigger work.

Key points

  • A score is a weighted composite — attributes do not matter equally, and a scoring model that treats them equally produces a number nobody trusts.
  • Weights should follow consequence: an attribute that blocks a feed or a filter outweighs a nice-to-have descriptor.
  • Scores are computed per category, because required attribute sets differ; a single global score averages away the categories actually in trouble.
  • Thresholds turn the number into a decision — publish, hold, or route for enrichment — and without them the score is decoration.

How is a catalog quality score constructed?

A team defines a check set per category — required attributes present, values inside an allowed vocabulary, units normalized, identifiers unique, images meeting spec — and assigns each check a weight reflecting what breaks when it fails. Each product yields a sub-score per dimension; those roll up to a product score, then to category and catalog roll-ups. Thresholds are then attached: below one line a SKU is held from syndication, below another it is queued for enrichment, and a per-category average that drops week over week raises a flag. The weighting scheme and the thresholds are versioned, because changing either silently makes historical scores incomparable.

Common pitfalls

  • A score nobody acts on. If no threshold routes work, blocks a publish, or owns a target, the dashboard becomes a vanity metric that improves only when someone games the checks.
  • Flat weighting, which lets ten trivial descriptors outrank one missing GTIN and hides the failures that actually cost money.
  • Changing weights or check definitions mid-stream without versioning, so a jump in the score reflects a changed ruler rather than a changed catalog.

FAQ

How is scoring different from measuring catalog quality?

Scoring sits on top of measurement and adds three things measurement does not have: weights that declare which failures matter more, roll-up rules that combine per-attribute results into product, category, and catalog figures, and thresholds that turn a figure into an action. Take those three away and what remains is a measurement report — which plenty of teams produce and never score.

What is a good catalog quality score?

There is no portable answer, because the number depends entirely on your own weights and check set. A score is meaningful only against your own baseline and thresholds; comparing your composite to another organization's compares two different rulers, not two catalogs.

Source

Most scoring models take their required-versus-recommended attribute distinction from a published specification; Google's Merchant Center product data specification marks attributes as required, optional, or conditionally required by category, which is exactly the structure a weighted, per-category score is built on.

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