What is FDX Enrichment?
Your product data works harder than any other asset you own. It powers your ads, your marketplace listings, your storefront, your SEO — and increasingly, whether AI assistants like ChatGPT and Perplexity recommend your products at all.
FDX Enrichment turns thin catalog data — short titles, sparse descriptions, missing Q&A, weak SEO metadata — into high-quality, brand-aligned content, at the scale of your whole catalog. Enrich once, and the improved content flows everywhere your feed goes: Google, Meta, TikTok, Amazon, eBay, Walmart, and your own site.
Why enrich?
Shoppers are finding products in new ways. Search is shifting from keywords to conversational questions, and answer engines pull from your product data to decide what to recommend. At the same time, the systems your catalog comes from — ERPs, PIMs, order systems — were never designed to produce compelling, channel-ready copy. Writing and rewriting it by hand doesn’t scale past a few hundred SKUs.
Enrichment closes that gap: it reads your existing product data, applies your brand voice, and generates the copy each destination needs.
Start here
What you get
Four things carry across every team that touches product data:
- Scale. Content for your entire catalog, including the long tail nobody has had time to write.
- Consistency. One voice across every product and every channel, rather than whichever copywriter got there first.
- Control. Nothing overwrites your source data, and nothing ships without your approval.
- Evidence. Every product carries a quality score backed by specific issues, and the performance change is measurable.
What that’s worth depends on your job.
If you run paid media
Titles and descriptions are the levers that move impressions and click-through rate, and enrichment gives you channel-shaped versions of them — written with Feedonomics’ channel conventions already applied, so you spend less time building transformer logic to reshape content after the fact.
Rollout stays yours: bring channels online one at a time, take titles before descriptions, and prove the change with a controlled test before you commit the whole catalog.
→ Where enriched fields go · A/B testing enriched content
If you own the catalog data
You get a quality floor across the whole catalog without standing up a copywriting program — and you get it without giving up control of your pipeline.
Enriched content lands as a raw data override, so your imported data is never replaced and your existing transformers keep working on it. Nothing is invented: if your source data doesn’t support a claim, enrichment won’t make it, and the gap surfaces as a gap you can go fix.
→ What makes good source data · Enrichment, transformers, and exports
If you own the brand
Your voice gets applied consistently to products no one would ever have gotten to — the discontinued line, the low-volume SKUs, the tail that quietly represents most of your catalog.
The claims you can’t make become hard rules rather than hopes. Every run is scored on brand adherence, and you review results side by side with the originals before anything is accepted.
→ Teaching enrichment your brand voice · Understanding your quality scores
If you measure results
You get two kinds of evidence. Per-product quality scores tell you whether the content itself is sound, with specific issues naming the field and the problem rather than a bare number.
Then performance: enriched products can be compared against their own history or against an un-enriched cohort, across paid media, on-site behavior, and AI-assistant traffic.
→ Measuring impact in Analytics · Proving enrichment works
The short version
- Select the products you want to enrich.
- Configure once — your brand voice, keywords, and goals become a reusable generation config.
- Run — enrichment generates content for every selected product.
- Review — every product gets a quality score; you accept or reject results side by side with the originals.
- Publish — accepted content lands in its own new fields; you map those into your exports when you’re ready, channel by channel.
Enrichment writes to new, dedicated fields in your database, applied as a raw data override — your imported source data is never replaced, and you can always compare what came in against what enrichment produced. Because the enriched value sits in the raw layer, everything downstream can act on it: transformers, transformed data overrides, and the feed build process.
Ready to try it? Follow Your first enrichment run. Want the concepts first? Read how an enrichment run works — or jump to the FAQ if you have a specific question.