Methodology · PCS v1.0
How the Pimfy Catalog Score works
72 isn't an opinion. Every point is computed by a fixed rule you can check — the AI never grades your catalog.
What is the Pimfy Catalog Score?
A 0–100 score for a fashion catalog's data quality: the weighted average of 7 components, each verified by deterministic rules against a fashion taxonomy and each channel's published requirements. Current methodology version: PCS v1.0.
Reports cite the version they were computed with, so a score is always traceable to the exact rules that produced it.
How is the score calculated?
Each product is checked against 7 weighted components (attribute completeness 25, descriptions 20, title quality 15, images 10, seo 10, variant hygiene 10, collection consistency 10 — weights sum to 100). The catalog score is the average of per-product scores, computed over sellable products only: test items, gift cards and empty drafts are excluded first.
| Component | Weight | What the rules verify |
|---|---|---|
| Attribute completeness | 25 | share of the attributes the product's detected category requires (fashion taxonomy) that are present anywhere in the listing |
| Descriptions | 20 | description exists · at least 300 characters · not a near-duplicate of another product (trigram similarity ≥ 85%) |
| Title quality | 15 | length in the 15–70 character range · not ALL CAPS · no SKU code in the title · contains the category keyword |
| Images | 10 | 3 or more images · alt text on at least half of them |
| SEO | 10 | search snippet (meta-description fallback) fills at least 70 characters · title within 70 characters |
| Variant hygiene | 10 | sizes/colors structured as variants, not split into separate listings · standard option naming (Size, Color, Material…) |
| Collection consistency | 10 | attribute vocabulary and title pattern coherent with the rest of the category |
catalog score = weighted average of per-product component scores — over sellable products only (test items, gift cards and empty drafts are excluded before anything is scored, and the report shows how many).
Why do attributes weigh more than images?
Attributes are what channels require and what buyers filter by — a missing fabric composition can block an Amazon listing outright, and per Coresight Research, AI shopping agents rely on structured, machine-readable product data to evaluate and recommend products. Descriptions and titles carry search and conversion. Images affect conversion but rarely block a listing that meets the minimum. Weights are reviewed as channels change their requirements — which is exactly why the methodology carries a version.
What role does AI play in the score?
None. The rules that score are code. AI does two things only: classify a product's category (so the right attribute requirements apply) and rebuild listings in the preview. The AI never grades — it only rebuilds.
What are the channel-ready scores?
Shopify-ready and Amazon-ready measure how much of that specific channel's required data is already present per product, aggregated across the catalog. A catalog can read well and still miss marketplace fields — that's why they differ from the catalog score.
The per-category requirements behind those scores are public — see the listing requirement guides for every category on Amazon apparel and Shopify.
See your own numbers
The free scan runs this exact methodology against your live catalog — no signup to start, and the report is yours either way. Scan your catalog.
