BeCuriouzLearn something new every day
Mapping: making your data speak each retailer's language
💡 Mapping reconciles your schema with each retailer's — both fields and controlled-vocabulary values — and getting value mapping wrong drops you out of filtered results even when the data 'transferred.'
Two retailers rarely describe a product the same way. Your PIM says sleeve_length: "Long"; Amazon's field is SleeveType and expects "Long Sleeve"; Walmart calls it sleeve_style with an allowed value of "Long-sleeve". Mapping is the translation layer that reconciles your schema with each retailer's schema — field to field, and value to value.
Mapping has two levels that both have to be right:
- Field mapping — your material → their fabric_type.
- Value mapping — your "Charcoal" → their accepted enum "Grey", because they don't offer "Charcoal" as a filterable option.
Value mapping is the subtle one. If you send "Charcoal" to a retailer whose color filter only knows "Grey," a shopper filtering by "Grey" won't find you — even though your field technically transferred. You've matched the field but missed the controlled vocabulary, so you fall out of the filtered results.
Getting mapping right is unglamorous, high-leverage work: it's the difference between your attributes appearing as data and your attributes actually powering discovery. It's also why you can't just write content once and blast it everywhere — the golden record is universal, but the mapping to each shelf is bespoke.