Methodology

Shopify store scoring: measured, not estimated

Every Shopify store publishes its catalog. We read it directly — so catalog size, price positioning and growth are observed facts, not inferences.

What we read, and where it comes from

Shopify exposes a public catalog endpoint on every store. No login, no scraping of rendered pages, no guessing: the data below is what the store itself publishes about its own products.

Signal Type What it tells you
Product count measured Store size. The headline filter — a 12-product store and a 5,000-product store are different businesses with different budgets.
Price min / avg / max measured Positioning. A $28 average is volume retail; a $340 average is considered purchase, and a different sales motion.
New products (30d) measured The growth signal. Stores adding products weekly are actively investing; a static catalog is coasting.
Last product added measured The best liveness test there is. A store that has added nothing in six months is effectively dormant, whatever its homepage says.
Vendor count measured One vendor is a DTC brand. Forty vendors is a multi-brand retailer — completely different buyer.
Out-of-stock ratio measured Inventory health. A high ratio means supply problems, or a store winding down.
Discount depth measured Average markdown against compare-at price. Reveals a discount-led brand versus a full-price one.
Catalog age measured Days since the oldest product. Separates a mature store from one launched last quarter.
Product types measured The store's own taxonomy — more accurate than any classifier we could run over its homepage.

Why we cap the sample

We read up to 500 products per store. That is more than enough for stable price and vendor statistics, and it keeps our request footprint small — a courtesy to the stores we index. For very large catalogs, treat product count as a floor rather than an exact total.

Detection on custom domains

Almost every serious Shopify store runs on its own domain, often behind Cloudflare, so the domain name tells you nothing. Detection uses CDN and markup fingerprints instead. Measured against DNS-level ground truth across our index — every domain whose infrastructure proves it is Shopify — recall is 100%, and markup detection additionally catches ~25,000 stores whose DNS gives nothing away.

Measured facts versus scored estimates

This is the important distinction, and it is why these signals are kept separate from the revenue model. Catalog data is observed. Revenue brackets are inferred. We publish both, clearly labelled, rather than blending them into a single opaque "store score" whose provenance you cannot check.

Combine them yourself, deliberately

Product count plus average price plus new-products-per-month is a better proxy for GMV than any single number we could invent — and because you can see each input, you can weight them for your market instead of accepting ours.

Who buys this, and what they filter on

Shopify app developers

Catalog size is your qualification. An inventory or bulk-editing app is irrelevant below 200 products and compelling above 2,000. Add new-products-per-month to find the stores whose pain is growing right now.

3PL, fulfilment and packaging

Product count with a low out-of-stock ratio and steady additions is a store actually shipping volume. Vendor count separates own-brand manufacturers from resellers — usually a different conversation entirely.

Agencies and consultants

Average price sets your fee ceiling. Pair a high average price with a low SEO score and you have a store with margin to spend and a problem worth fixing — the cleanest prospect there is.

Other scoring methodology

SEO Score Reputation Signals Revenue Estimation Shopify Store Score
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