Product data enrichment is the cheapest growth in ecommerce

Most growth levers cost money up front. More ad spend, more sales headcount, a new channel, a replatform. Product data enrichment is the rare one that doesn't. You already own the catalog. Making it complete enough to get found, get chosen, and get kept is the highest-return work most teams are leaving on the table.

The numbers behind that are not subtle. 77% of shoppers say product information matters to their purchase, and 62% say they'll spend more on a product with detailed information (GS1 US). On the flip side, 46% say better descriptions would improve their experience, and nearly two in five returns happen because the item didn't match its listing (DHL's 2025 E-Commerce Trends Report). Same product, different data, completely different outcome.

What enrichment actually means

Enrichment is taking sparse, raw product information and building it into structured, accurate, channel-ready content. It's three kinds of data, not one:

• Technical — dimensions, weight, materials, certifications, compatibility
• Marketing — titles, descriptions, lifestyle imagery, brand copy
• Logistical — shipping weight, packaging size, country of origin, regulatory flags

The difference it makes is concrete. Here's the same jacket, before and after:

	Before	After
Title	Men's jacket, blue	Men's Quilted Puffer Jacket, Navy Blue, Water-Resistant Shell
Description	Warm jacket. Available in multiple sizes.	Lightweight quilted puffer with a water-resistant recycled-polyester shell and 90% recycled fill. Regular fit, packable hood. Built for the commute and the outdoors.
Attributes	Size only	Weight, packable dimensions, fill type, shell material, care, fit
Imagery	One flat-lay	Five lifestyle, one flat-lay, one 360°, one size guide
Logistics	None	Shipping weight, packaged dimensions, origin, HS code

Technically the same SKU. In search, on the shelf, and in your return rate, two completely different products.

Where it pays off

• Search and discovery. Engines and marketplaces match on structured attributes. More signal, better placement — without buying a single extra click.
• Conversion. Online, your listing does the job a salesperson does in a store. When it answers the question the shopper arrived with, they buy.
• Returns. Mismatched expectations start at the listing. Fixing the content is more durable than fixing the returns process after the fact.
• AI readiness. Recommendation engines and shopping assistants lean on clean, structured data. Sparse listings get surfaced less, described wrong, or skipped.

The question nobody frames well: where does the work happen?

Everyone agrees enrichment matters. The disagreement — usually unspoken — is about where the work should live. Three answers are on the market, and the difference is the whole game.

At the exit (feed management). Transform the data on its way out, per channel, with rules. Feeds are great at delivery and format mapping. But enrichment done here fixes the projection, not the product. It never writes back, so you redo the same work on every channel, and your source of truth stays thin underneath green dashboards.

In the cabinet (the PIM). A PIM is the right place to store a clean record. It just doesn't produce one. It won't gather a missing spec, normalize twelve suppliers into one taxonomy, or write a description. Buy a bigger cabinet and the filing still lands on a person.

Upstream, written back to the source. Do the work before the data leaves your single source of truth — then write the enriched result back into it. Now every channel, every marketplace, every assistant, and every system you haven't connected yet draws from one complete record. The work lands in the one place it stops repeating.

"Upstream" doesn't mean "go buy a PIM." It means into whatever your source of truth is — PIM, ERP, commerce platform, or a flat file if you don't have a system yet — and then let your feed tools do the delivery they're genuinely good at.

Enrichment is a loop, not a project

The teams that win treat this as a cycle that runs continuously:

1. Ingest raw data from anywhere — PDFs, CSVs, supplier sheets, webpages, even customer reviews.
2. Clean it: dedupe, standardize units, fix formatting, reconcile conflicts.
3. Enrich it: fill attributes, write copy, assign granular categories, attach media and compliance.
4. Maintain it: watch performance, re-enrich what underperforms, keep it current as products and channel rules change.

Do that once a year as a project and the catalog drifts back to thin by Q3. Run it as a loop and quality compounds instead of decaying.

The new reader changes the math

Here's why this stopped being a tidiness exercise. The audience for your product data isn't only human anymore. When an AI assistant or an agentic-checkout flow does the shopping, it reads structured data wherever it finds it — your PDP, a marketplace listing, a third-party aggregator, a distributor's copy of your SKU. You don't get to choose which surface it hits.

That's exactly why exit-level enrichment falls short and source-level enrichment wins. If the enrichment only lives in the feeds you hand-tuned, you're complete on a few surfaces and invisible on the rest. Fix the product at the source, and every surface a machine might read inherits the same complete answer.

The honest part: this is a lot of work

Done by hand, enrichment runs 30 to 45 minutes per SKU. Multiply by a catalog of tens or hundreds of thousands and "just enrich it" becomes a hiring plan. That's why most catalogs sit half-finished — not because teams don't know what good looks like, but because the volume never fit the headcount.

That's the line we draw at Anglera: your PIM stores the data; Anglera does the work. We run the enrichment loop upstream — gathering, cleaning, enriching, and scoring every SKU against your standards — and write the result back into your source of truth, so it shows up complete everywhere your products get read. The cheapest growth in ecommerce only counts if someone actually does it.