Two thirds of European bike catalogs ship without a single product identifier
Across 180 storefronts scanned this spring, identifier coverage was the most commonly failed check in the whole score, and also the cheapest to close. It is a data plumbing problem wearing the costume of a content problem.
An assistant answering a shopping question has to decide which product a query refers to. It does that by matching what it can read on your page against what it knows: a GTIN, an MPN, a brand and model string. When none of those are present, your product is an unmatched piece of text competing with pages that carry all three.
In the scans we ran between March and June, 63 percent of storefronts in the cycling and outdoor accessory category emitted no product identifier at all in their markup. Another 21 percent emitted one on some products and not others, usually split cleanly along supplier lines, which tells you where the data was lost.
The failure is upstream, not editorial
The identifiers usually exist. They arrived in the supplier feed, landed in a metafield, and were never mapped into the product markup because nobody asked the theme to emit them. That is why this check fails on stores with otherwise excellent product pages, and why fixing it needs no writing at all.
The pattern in a typical catalog looks like this.
| Where the identifier is | Share of products | Fix |
|---|---|---|
| In a metafield, not in markup | 54 percent | Rule, no credits |
| In the description as free text | 12 percent | Rule with extraction, no credits |
| In the supplier feed only | 19 percent | Import, then rule |
| Genuinely absent | 15 percent | Ask the supplier |
Three quarters of the gap closes with a mapping rule and no writing. The last fifteen percent is a supplier conversation, and for own brand products it is a decision about whether to buy a GTIN range at all.
What closing it is worth
Structured data carries 25 percent of the Feedmind Score, and identifier coverage is a substantial part of that dimension. On the Nordvelo catalog we use as our reference, mapping identifiers on 1,180 of 1,400 products moved the structured data dimension from 44 to 71 and the catalog score from 51 to 58, with no change to a single sentence of copy.
What we cannot tell you is what that score movement did to sales. Nobody can, and the honest version of this article stops here rather than converting a score change into a revenue claim.
How to check your own catalog in five minutes
Open a product page, view source, and search for gtin, mpn and brand. If your theme renders product markup at all, they will be in the JSON-LD block. If the block is there and those fields are missing, you have the common case: the data exists somewhere in Shopify and is not reaching the page.
If you would rather not read source, the free check does it for you across a sample of your catalog and reports coverage as a percentage.