What is Attribute extraction?

Attribute extraction converts raw product text, specs, and images into consistent, structured fields—brand, color, size, material, fit, compatibility so that site search, filters, and recommendations can use them reliably.

Without clean attributes, shoppers see irrelevant results, filters hide the right items, and analytics can't segment inventory. With consistent attributes, you improve findability, facet quality, and ranking; you also unlock rules like “show in-stock size 42 first” or “boost sustainable materials,” and enable downstream tasks like deduping, bundling, or pricing intelligence.

Most eCommerce teams blend rules, ML models, and human validation. They start by defining canonical vocabularies (e.g., color and size lists), normalize synonyms, and resolve conflicts between title, bullets, and images.

Quality is tracked with spot checks, agreement on edge cases, and error budgets per attribute. Freshness matters: attributes are re-extracted when sellers edit titles, regions change naming, or new variants appear.

Example

A marketplace onboarded thousands of shoe SKUs with inconsistent size formats (US, UK, EU) and free-text colors. After extraction and normalization, size filters finally matched inventory, “black running shoes size 42” returned in-stock pairs, and learning-to-rank could weigh attributes like pronation support. Conversion from search improved because the system understood both the query and the catalog in the same language.

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