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October 20, 2026
Update. From 26 October 2026, Completeness is available to every Cernel customer, with no add-on to enable. It now lives in the Catalog: turn on Insights for the category’s completeness banner and the fill figure on each tracked attribute column. The per-product Score column described below has been replaced, and the Market Insights category scorecard mentioned below no longer shows completeness: that page is now Search demand and shows search demand only. See Completeness Score for how it works today.
“How complete is our product data?” is a simple question with a frustrating answer, because the honest response has always been to go and look. You scroll the catalog, open a few products, and form an impression. That impression is slow to build and easy to get wrong. The Completeness Score answers the question directly. Turn on Insights in your Catalog and every product carries a score, every attribute column shows how widely it is filled, and the category as a whole gets a completeness figure. The gaps stop being something you hunt for and become something you can see.

A score on every product

With Insights on, a Score column appears next to each product, reading as a fraction like 8/12: eight of the twelve tracked attributes are enriched on that product. Each attribute column header also shows how widely that attribute is filled across the products in view, so an attribute that is missing on most of a category jumps out at once and points you straight at the next enrichment run.

Measure what actually matters

A category can carry many attributes, but not all of them count towards how “done” it is. Completeness is measured against the attributes you choose to track, so the score reflects the data you care about instead of every field a product could hold. Stop tracking the ones you do not need, and they stop dragging the picture down.

From one product to the whole category

The same idea scales up and down. A single product’s detail page shows completeness as a ring, measured against a category you pick. A category’s scorecard in Market Insights shows completeness as a headline metric with a per-attribute breakdown. It is one measure, read at whatever altitude you need.

Separate the setup from the running

Completeness is also how you run enrichment as an operation, not just a number to look at. The new Completeness filter narrows the catalog to the products that still have gaps, and the enrichment flow lets you select all attributes at once. Put together, filling gaps becomes a single repeatable action: filter to the products where some tracked attributes have no value, select everything, and enrich only what is missing.
That split matters because it separates two jobs that used to be tangled together. Configuration, which agents run, which attributes are tracked, how prompts are written, is set up once. Operations, keeping the catalog complete as products come and go, is now a simple loop anyone can run without touching that setup. Apply the same filter in an Automation and the loop runs on its own.

Why it matters

You cannot improve what you cannot see. A completeness score turns a vague sense of “our data could be better” into a specific list of which products and which attributes are behind, so enrichment effort goes where it counts and progress is something you can watch climb.

Read the step-by-step guide

The full reference covers the Insights banner, the column fill figures, tracked attributes, and how to raise a category’s completeness.