From Catalogue to Conversion: Feedonomics Data Enrichment
The rules of being found online have changed. A decade ago, a product showed up because its title matched a keyword.
Today, a product shows up because an AI agent like those launched in Copilot, Perplexity, Gemini, and Meta AI acts as a marketplace recommendation engine, understanding what the product is, who it’s for, and why it belongs in the answer to a shopper’s question. That comprehension doesn’t come from clever marketing copy; it comes from clean, structured, richly described product data.
And for most brands, that’s where the gap lives.
Feedonomics Data Enrichment is built to close it. A generative AI service designed to produce rich, structured, brand-accurate product data at scale, it gives businesses the kind of catalogue quality that AI systems — and the shoppers using them — need to find, recommend, and buy.
Why high-quality product data is now a prerequisite
Product data has quietly become the most important asset in a merchant’s tech stack. It’s what fuels visibility on Google Shopping and Meta Ads. It’s what determines whether a product is approved on Amazon, Walmart, or TikTok Shop. And, increasingly, it’s what determines whether an AI answer engine will surface a brand at all.
The problem is scale. Most merchants don’t have ten products; they have hundreds, thousands, sometimes millions. And across those ever-growing catalogues, the gaps are everywhere: missing attributes, inconsistent descriptions, titles written for a category page two years ago that no algorithm respects today. Fixing those gaps manually is, in the words of Sharon Gee, Vice President of Product, AI & Feedonomics at Commerce, “a huge pain and a project that never ends.”
Speaking on stage at Commerce Live 2026, Gee was direct about the stakes: “AI doesn’t ask your permission to present your information to answer a user’s query. It reads your data and it decides. If your catalogue isn’t in the mind of the agent, you’re invisible.”
That is the new baseline. Enriched data isn’t a nice-to-have for SEO performance. It’s the price of admission for participating in modern discovery.
What Feedonomics Data Enrichment actually does
At its core, Feedonomics Data Enrichment is built to do three things at once: fill the gaps in your catalogue, do it at the scale your catalogue actually requires, and keep the output unmistakably yours.
Merchants select which data to enrich, i.e., titles, descriptions, attributes, taxonomy, SEO metadata, channel-specific fields, structured content for AI search experiences. They upload brand and SEO terms that act as guardrails for tone and language. Then enrichment jobs run with an LLM-as-judge step that grades outputs for quality and brand fit, plus a human review step that keeps the merchant in control before anything goes live.
The result is product content that holds up across every surface a brand sells through:
- On-site: Richer, more structured product pages that improve relevance and on-site search.
- Advertising: Complete titles, accurate attributes, and optimised fields that platforms like Google Shopping and Meta Ads can actually use, leading to better targeting and higher Quality Scores.
- Marketplaces: Channel-specific optimisations that meet each destination’s rules, e.g., the missing bullets, the correct taxonomy, the proper attribute fields so listings don’t underperform on Amazon or Walmart because of fixable data gaps.
- AI-powered discovery: Structured, machine-readable content that LLMs and answer engines can confidently match to shopper intent.
It’s the same enriched data, structured once and activated everywhere. That’s the operational shift.
New Balance: Driving Incremental Revenue Through Smarter Feed Management
New Balance and its agency Brave Bison were struggling to scale shopping ad campaigns beyond core European markets. Their previous feed management tool was slow to update, lacked sophisticated data transformation rules, and made it difficult to localize inventory or hit New Balance’s strict ROAS targets, leading to lower conversion rates, weaker CTR, and even account suspension risks in markets like France.
After switching to Feedonomics, Brave Bison consolidated all feed-based marketing, including Google Shopping, Meta, affiliates, and social platforms into one platform. Data enrichment drove the biggest early wins: supplemental feeds enabled four-daily updates to Google Merchant Center, eliminated ads for sold-out products, and ensured sale prices were accurately reflected across markets. New Balance has had zero account suspensions since the switch. Flexible transformation rules also unlocked a full A/B testing roadmap, including title optimizations and custom labels that segment best sellers (imported live from Google Analytics) for smarter bidding.
After one year of improved data quality with Feedonomics, the results spoke for themselves:
- Cost decreased by 38% and CPC decreased by 26%
- Sessions increased by 5% and CVR increased by 15%
- Sales increased by 21%, revenue increased by 22%, and ROAS increased by 95%
The challenges teams face without scalable enrichment
Without a system like this, marketing and ecommerce teams face an unwinnable choice. Either they accept that their catalogues go to market with significant gaps, paying the cost in slower performance, lower discoverability, and missed conversions, or they burn enormous amounts of time fixing things SKU by SKU. Either way, the speed at which they can launch new products, enter new markets, or activate new channels slows to a crawl.
Enrichment removes that bottleneck. When product data is enriched once and structured correctly, marketing, ecommerce, and marketplace teams can move in parallel instead of waiting on manual updates or one-off fixes. Strategy gets to execute on time.
A go-to-market accelerator, not just a content tool
Enrichment is best understood not as a content production tool but as a go-to-market accelerator. As channel ecosystems continue to fragment and evolve to new marketplaces, new ad surfaces, new AI agents with new schema requirements, the connective tissue between strategy and execution is the catalogue itself. If a product catalogue is enriched and structured well, activating a new channel is a configuration change. If it isn’t, it’s a project.
This is especially true for AI-driven discovery. Large language models and answer engines rely on structured, high-quality data to surface products accurately. The brands whose catalogues are ready to be parsed by those systems are the brands whose products will be in the answer. The ones that aren’t, won’t be.
The final word
Data enrichment isn’t about adding more content. It’s about adding the right content, in the right structure, everywhere it’s needed. For an ecommerce business with a thousand SKUs and ambitions to grow across channels, doing that manually is impossible. Doing it with generative AI, in a system that respects brand voice and keeps a human in the loop, is finally practical.
The AI era of product discovery is already here. Feedonomics Data Enrichment is built so merchants can show up in it — confidently, consistently, and at scale.
To learn more, download the Ai Powered Retail Guide here.
