Catalog Optimization
Product Data Enrichment: The 2026 Guide
Most catalogs are complete enough to be listed and nowhere near complete enough to be compared. What product data enrichment covers, which attributes matter, and how to prioritise.
Kanishka Thakur
Product data enrichment is the work of adding the attributes your catalog is missing, so that the systems reading it can do more than identify your products. It covers filling empty fields, standardising inconsistent ones, writing attributes that were never captured, and adding the context a buyer or a machine needs to judge whether a product fits a requirement.
The reason it has become urgent rather than tidy-up work is narrow and specific. For fifteen years, catalogs only had to be complete enough to be listed. Now they have to be complete enough to be compared, because something else is doing the comparing.
Complete enough to list, not complete enough to compare
A product is listable when a surface can identify it: a title, a brand, an identifier, a price, an image, a stock state. Nearly every catalog clears that bar, because failing it meant being rejected and everyone fixed it years ago.
A product is comparable when something can answer a question about its suitability. "Which of these is best for a narrow foot." "Which is safe on colour-treated hair." "Which fits a 15 inch laptop." "Which can I return if it does not fit."
Answering any of those needs attributes most catalogs do not hold:
Attribute type | Examples | Usually present? |
|---|---|---|
Identity | Title, brand, GTIN, MPN, price | Yes |
Classification | Category, product type, variant grouping | Usually, often imprecise |
Physical | Material composition, dimensions with units, weight | Rarely complete |
Fit and compatibility | Sizing guidance, what it works with, what it does not | Rarely present |
Use | What it is for, who it suits, when to use it | Almost never structured |
Care and terms | Washing, storage, warranty, return window | Scattered in prose, if anywhere |
The bottom four rows are where recommendations are decided. They are also, reliably, the four that are missing.
Why the gap exists
It is not negligence. It is a rational response to what the surfaces used to require.
Nobody asked for structured material composition, so nobody captured it. The information often exists somewhere, in a supplier spec, a PDF, a paragraph of product copy, a photograph of a care label. It just was never written into a field a machine can read.
That creates a specific and fixable situation: the fact exists, it is simply not in a readable place. Which is why enrichment is usually a retrieval and structuring problem rather than a research problem.
What to enrich first
Enriching everything is a project that does not finish. Three filters, applied in order.
1. Products with demand you are losing. Start from the questions your category actually gets asked, and find the products that should be appearing in those answers and are not. Enriching a SKU nobody asks about changes nothing.
2. The attributes that decide your category's comparisons. Every category has three or four. For footwear it is width, arch support and material. For skincare it is actives, concentration and skin type. For electronics it is compatibility and dimensions. Write down yours before starting, because the generic list is longer than the list that matters.
3. The products with the most downstream reuse. An attribute written once on a parent product that propagates to forty variants is worth more than one written on a single SKU.
What to deprioritise: products being discontinued, long-tail SKUs with no search demand behind them, and attributes that are nice to have but never appear in a buying question.
Where Nudge fits
Enrichment on its own is close to a commodity. Filling empty fields is available cheaply and from several vendors, and we would not recommend buying a platform to do only that.
What is not commodity is knowing which gaps cost you revenue, and having the fix land everywhere it needs to.
Nudge Catalog Enrichment does three things in sequence:
1) Catalog Analysis scans the catalog and scores each product on what an AI system can and cannot understand about it. Per SKU, not per file. A catalog can pass every validation rule and still fail every comparison question, and only a per-product view shows you that.
2) Enrichment workflows fill the missing attributes, variants and use cases. Every change is one approval, so nothing is written to your catalog without a human accepting it.
3) Publishing sends the result to the surfaces that recommend: ACP, UCP, Shopify's agent endpoints and Google Merchant Center. Four named protocols rather than a claim about breadth.
The part that makes it worth doing inside a platform rather than as a point tool is the join. Nudge Shopper Insights tells you which products should be appearing in which shopping questions and are not, which is what turns "enrich the catalog" into a ranked queue. Nudge Funnels builds the page the resulting visit lands on. One enrichment changes both sides of the click: the same fix lands in the answer and on the page.
It is judged on agentic revenue, not on fields filled. Coverage is an input. Revenue from products that are now recommendable is the output.
The check that catches the expensive failure
An attribute can be enriched correctly and still fail, if your sources disagree.
Your catalog says the material is 80% merino. Your product page says 100% merino in the description. A system reading both has a contradiction to resolve, and the safe resolution is to recommend a product whose sources agree with each other.
Divergence between the catalog, the structured data on the page and the rendered page is invisible to any tool that audits one of the three. Take twenty enriched products and compare all three before declaring the work done.
How to measure enrichment
Four numbers, and the order matters because the first two are inputs that teams often mistake for outcomes.
Attribute coverage by category. An input. Useful for tracking progress, useless as a success measure.
Products passing a comparison test. Take the three questions your category gets asked and check how many products can be answered from structured data alone.
Recommendation share. How often your products appear in the shopping questions your category gets asked, measured against named competitors.
Revenue from AI-referred and agent-led visits. The honest test. Both are routinely under-counted, because agent traffic often does not fire standard analytics events.
Record all four before you start. Enrichment programmes that cannot show a before are very hard to argue for a second time.
Frequently asked questions
What is product data enrichment?
Adding the attributes a catalog is missing so that systems reading it can judge whether a product fits a requirement, not just identify it. It covers filling empty fields, standardising inconsistent ones, and structuring information that exists only in prose or documents.
Why does product data enrichment matter now?
Because AI systems answering shopping questions need to reason about products, not just match them. Reasoning needs attributes like material, dimensions, compatibility and intended use, which most catalogs never captured because no earlier surface required them.
What attributes should I enrich first?
The three or four that decide comparisons in your category, on the products that have demand you are currently losing. The generic completeness checklist is much longer than the list that actually changes outcomes.
How do you measure product data enrichment?
Attribute coverage is the input. The outcomes are how many products can be answered from structured data alone, your recommendation share against named competitors, and revenue from AI-referred and agent-led visits. Record all of them before starting.
Is product data enrichment the same as catalog management?
No. Catalog management organises and distributes the data you have. Enrichment creates data you do not have. Most catalogs are well managed and still incomplete in the ways that decide recommendations.
