Free template
Catalog Attribute Coverage Audit
A per-SKU scoring grid for catalog data quality — identifier, schema, rendering and attribute coverage — with three worked examples showing how the scoring works.
CSV · 15 columns · 3 worked examples
Who it's for: Anyone with a catalog big enough that "our product data is fine" is a guess rather than a fact.
What's in it
Exactly what you're getting.
- 115 columns covering identifier presence, brand consistency, taxonomy discipline, attribute coverage, server-side rendering, schema validity and reviews.
- 2Boolean columns scored 1/0 so you can average a whole column and get a real catalog-level number.
- 3A coverage_pct formula that pairs with the Critical rows from the product attribute schema template.
- 4Three worked example rows showing what a passing, failing and mixed SKU look like — including the most expensive failure, a script-rendered price.
- 5A gap_notes column, because the specific gap is what gets fixed, not the score.
How to use it
The order that actually works.
Most of these templates fail the same way — filled in completely, applied to nothing. This is the sequence we'd follow.
- 1Export your catalog and paste one row per SKU. Delete the three example rows once you've read them.
- 2Sort by revenue, not by score. Fixing your top 20 sellers beats fixing 200 long-tail SKUs, and it's the fastest route to a measurable change.
- 3Average each boolean column to get catalog-level numbers you can report — "61% of SKUs have a GTIN" is a sentence a CFO understands.
- 4Re-score the same SKUs after the fixes so you have a before and after.
The thinking behind it
Catalog Debt: Measuring What Incomplete Data Costs You →The full guide this template came out of. No email required — it's just on the site.
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The free AI Shelf Check does the measurement side for you — whether ChatGPT, Perplexity and Gemini recommend your products or a competitor's. No call required.
Weighing whether to do this in-house? Compare the approaches