How to choose a product data API
The questions that actually separate commerce data vendors: coverage definition, freshness, match quality, provenance and export.
Coverage claims are close to meaningless without definitions. “We cover Amazon” could mean anything from a hundred thousand products to a hundred million. Ask how coverage is counted, per seller, and what the median age of a record is.
Freshness should be a distribution, not a promise. Every vendor has stale records; the honest ones tell you how stale and how often.
Match quality is the question nobody asks and everybody should. Ask for a match run on two hundred of your own products, and ask whether per-row confidence is returned. Demo catalogues are pre-verified, so a demo tells you nothing.
Provenance and export are the two that decide whether you can build seriously. Provenance lets you weigh a value instead of trusting it uniformly; bulk export means your analysts are not paginating an API to build a model.
Keep reading
What is commerce intelligence?
Commerce intelligence is the layer above price monitoring: an index of what exists, who sells it, at what, and what that means.
Why percentile beats percent-off
A percentile against a product's own history is a defensible measure of discount depth; percent-off a reference price is not.
Product matching and why it fails quietly
How cross-seller product matching works, the three ways it fails, and why published match confidence matters.
GTIN, UPC, EAN and ASIN
How product identifiers relate, why check digits matter, and what to do when there is no identifier at all.
Data provenance in commerce data
Why every price should carry its origin and a confidence weight, and what goes wrong when it does not.