Catalog Enrichment Use Case

The challenge

Internal product catalogs are rarely complete. A marketplace onboarding thousands of SKUs from third-party sellers, or a retailer expanding into a new category, often ends up with missing images, inconsistent or absent category tags, and empty ingredient fields — all of which hurt on-site search, filtering, and the ability to build category pages that actually convert.

Manually researching and filling in these gaps product-by-product doesn't scale, and outsourcing the work to a data entry team introduces its own consistency problems, since different people describe and categorize the same product differently.

Incomplete data also compounds downstream — a missing category tag means a product doesn't show up in the right filtered search, and a missing ingredient list means it can't be surfaced to shoppers filtering by ingredient, which is an increasingly common way beauty shoppers narrow down a purchase decision.

Relevant data fields

The fields most useful for enrichment are category_1/2/3, breadcrumbs, primary_image_url, additional_images, description, summary, highlights, and ingredients_formatted — largely drawn from the Product Info and Media groups in our field reference, plus the Ingredients group for personal care categories where ingredient transparency matters to shoppers.

how_to_use and warranty_information from the Usage & Other group are also commonly missing from third-party listings and are straightforward to backfill using the same matching approach.

Who uses this

Marketplace catalog operations teams, e-commerce merchandising teams launching new categories, and private-label brands building out product pages for the first time are the most common users of this use case.

Retailers migrating between e-commerce platforms also use this approach to backfill gaps that appear during a data migration, when fields sometimes get dropped or malformed in the transfer.

Keep exploring

Getting started

Begin with a sample of your catalog to validate match rates before committing to a full backfill — brand and product name matching typically works well for established products but may need manual review for very new or oddly named listings.

Once match quality looks good on a sample, the same process scales to your full catalog, and can be run as a one-time project or set up as an ongoing sync depending on how often your catalog changes.

If you're not sure which fields are worth prioritizing for your specific category, our team can walk through your current catalog gaps and recommend where enrichment will have the most impact on search and conversion.

Common questions

What if a product doesn't match anything in the feed? Not every SKU will have a match, especially very new or niche products — unmatched items simply stay as-is, and coverage naturally improves as new products get picked up by tracking.

Does this replace our own product data team? Not usually — most teams use it to accelerate enrichment for the bulk of a catalog, freeing up their own team to focus on the products that need custom attention.

How often does the enrichment source data change? Products are re-checked on a regular refresh cycle, so a periodic re-sync keeps your catalog aligned as retailers update descriptions, images, or formulations.

Can we automate the matching step ourselves? Yes — most teams script the brand and product name matching against the API output directly, using a fuzzy-match library for near-identical titles rather than reviewing every row by hand.

What happens to enriched fields if a retailer removes a product? Existing enriched data in your own catalog stays as you saved it — BeautyFeeds only reflects the current state of the source retailer's listing, not your downstream copy.