Product Data Completeness Scorer
Score product data completeness in your browser. Check required, recommended, and AI-search attributes per SKU. No upload, no login, instant results.
Paste a product feed or upload a CSV to get a product data completeness score for every SKU — a per-record and catalog-wide measure of how many required, recommended, and AI-search-critical attributes are actually populated. Distributors, marketplaces, and API teams use it to find the records that are blocking syndication, paid feeds, and citations from AI shopping assistants.
Product Data Completeness Scorer
The interactive version of this tool is coming soon. It will run entirely in your browser — no login, no upload limits.
Planned tool: product data completeness score
Need this now? Talk to ClaroWhat it checks
The scorer reads your column headers and values, then evaluates each row against a weighted attribute model. It reports:
- Required-field coverage — identifiers (GTIN/MPN/SKU), title, brand, price, and primary category. A missing required field is treated as a hard fail, not a soft deduction.
- Recommended-field coverage — description, images, unit of measure, packaging quantity, and key specs. A furniture record with dimensions but no material, or an MRO part with a description but no manufacturer, scores lower here.
- AI-search and GEO attributes — the structured fields that AI engines and shopping agents rely on to cite a product: normalized brand, GTIN, explicit attribute–value pairs, and availability.
- Filled-but-empty values — cells that look populated but contain placeholders like
N/A,TBD,0,-, orsee description. These inflate naive completeness counts and are flagged separately. - Per-attribute fill rate — the percentage of the catalog populated for each column, so you can see whether one bad attribute (for example, missing
coloron 80% of CPG SKUs) is dragging the whole score down. - A weighted completeness score — a 0–100 number per record and for the catalog overall, plus a plain-language band (poor / fair / good / AI-ready).
How the completeness score is calculated
There is no single industry standard that defines a “complete” product record, so the scorer uses a transparent weighted model rather than a black box. Required attributes carry the most weight because a record missing a GTIN or price typically cannot be published at all. Recommended and AI-search attributes carry progressively less weight but still move the number, which is why two records with the same required fields can score differently.
- 1Map columnsHeaders are matched to a canonical attribute set (title, brand, identifier, price, description, images, UoM, specs). Unrecognized columns are listed so nothing is silently dropped.
- 2Evaluate each cellEvery value is classified as populated, empty, or placeholder. Placeholder detection catches the cells that fake completeness.
- 3Apply weightsRequired, recommended, and AI-search attributes are scored and combined into a single 0–100 completeness score per record.
- 4Roll up the catalogPer-record scores aggregate into a catalog score and a per-attribute fill-rate table so you can prioritize the highest-impact gaps.
Everything runs in your browser. Your file is parsed locally in JavaScript and never uploaded to a server, so you can score a confidential industrial distribution price file or an unreleased CPG assortment without it leaving your machine. There is no login and no file-size cap beyond what your browser’s memory allows.
Related resources
Tool
Attribute Coverage Analyzer
Break a catalog down by per-attribute fill rate to find which fields are dragging completeness down.
Tool
AI Citability Checker
See whether an AI assistant can actually verify and cite a single product from its data.
Guide
Product Data Requirements for AI Search Visibility
Which attributes AI engines need before they will surface your products.
Playbook
Make Your Catalog AI-Search Ready (GEO)
A step-by-step workflow for closing the gaps this scorer surfaces.
Glossary
What Is Generative Engine Optimization?
Why complete, structured product data is the foundation of GEO.
Claro
Automated enrichment
See how Claro fills required and AI-search attributes across an entire catalog with provenance.
FAQ
What is a good product data completeness score?
For a catalog to syndicate cleanly and be citable by AI shopping agents, aim for full coverage on required fields and 80%+ on recommended and AI-search attributes. A record in the “AI-ready” band has every required field populated plus structured brand, identifier, and attribute–value pairs. A score in the fair or poor band usually means missing identifiers, placeholder values, or sparse specs.
How is data completeness measured?
Completeness is the share of expected attributes that are actually populated with usable values. A simple version counts non-empty cells, but that overstates quality because placeholders like “N/A” count as filled. This scorer uses a weighted model — required fields matter more than nice-to-haves — and treats placeholder values as empty, so the number reflects how publishable and AI-ready a record really is.
Why do AI search engines care about completeness?
Generative engines cite products they can verify. When a record is missing a GTIN, brand, or explicit specs, the engine has nothing concrete to anchor a recommendation to, so it favors a competitor with fuller data. Completeness is the prerequisite for being surfaced at all — see the guide on product data requirements for AI search.
Does my file get uploaded anywhere?
No. Parsing and scoring happen entirely in your browser using local JavaScript. The file never leaves your device, there is no login, and nothing is stored, which is why you can safely score confidential price files or pre-launch assortments.
What file formats can I score?
Paste tab- or comma-separated rows directly, or upload a CSV exported from your PIM, ERP, or spreadsheet. As long as the first row contains column headers, the scorer maps them to its canonical attribute set and flags any columns it does not recognize.