What Is Product Data Enrichment?
Product data enrichment is the work of completing and improving the information attached to each product: attributes, descriptions, categories, images and the answers to the questions shoppers ask. The goal is that anyone, human or machine, can find the product and understand whether it fits.
Raw catalog data from an ERP or PIM is usually thin: a SKU, a short title, a price, perhaps a one-line description. Enrichment adds what is missing. Materials, dimensions, fit, compatibility, care, intended use, and the structured attributes each channel requires.
The source of that information matters. Good enrichment draws on what the business already knows, such as supplier data, the brand's own copy, customer questions and reviews, and the priorities of the team selling the product. Invented detail is worse than a gap.
Why Enrichment Matters Now
AI assistants filter on attributes. A product missing the attribute a shopper asked about is skipped, however well it fits.
When a shopper asks an assistant for waterproof hiking boots for wide feet under 200 dollars, the assistant filters on exactly those facts: waterproof rating, width options, price, intended use. A product that has them in its data can be recommended. A product that has them only in a photo or a PDF cannot.
Research on generative engine optimization points the same way. Specific, sourced detail improved inclusion in AI answers in the Princeton and Georgia Tech study (Aggarwal et al., SIGKDD 2024); repeating keywords did not. For a product page that means complete attributes and a description that answers real questions.
The same information drives traditional search and Google Shopping eligibility, so enrichment usually pays back across channels rather than in one.
What the Work Involves
Descriptions, attributes, categories, images and channel requirements. Most of it is judgment about what matters for each product.
- Descriptions: turning a short title into plain language that explains what the product is, who it is for and what makes it different.
- Attribute completion: adding the missing specifications: materials, dimensions, weight, compatibility, certifications.
- Category mapping: aligning each product to the taxonomy a channel expects, such as Google product categories.
- Images: alt text, additional angles and lifestyle shots where they help.
- Channel requirements: meeting the specific rules of Google Merchant Center, OpenAI's product feed specification and marketplaces.
- Shopper questions: answering on the page the questions buyers actually ask about that product.
Across thousands of products the hard part is not knowing what to do, it is deciding which products and fields matter most and then doing the work consistently.
How Paz Handles Enrichment
A Paz expert decides what to complete first based on your priorities, writes from your sources, and our experts publish within agreed guidelines and bring exceptions to you.
Paz does this work for ecommerce teams. A Paz expert starts from your commercial priorities, such as the collections that matter this season or the products losing ground, and from the questions shoppers are asking about them. The platform surfaces the gaps across the catalog. The expert completes attributes, rewrites descriptions and adds product answers using your own sources, written for the shoppers you sell to.
Our experts publish within agreed guidelines to supported Shopify or Merchant Center fields. PIM source corrections go to your PIM owner, and the work is tracked against a baseline so you can see what moved. See how the product content service works, or check one product page to see what is missing today.
FAQ
How does product data enrichment differ from feed management?+
Can AI automate product data enrichment?+
Where should enriched product data live?+
Related terms
How AI-ready are your products?
Check one product page for the information gaps that keep it out of AI answers and search results. If you want the fixes handled, Paz experts do that work for ecommerce teams.