How to Prepare Ecommerce Product Data for AI Shopping

• What exactly is it?
• Who is it for?
• What problem does it solve?
• What are its key specifications?
• What size or dimensions is it?
• What material is it made from?
• What makes it different from similar products?
• What variants are available?
• How much does it cost?
• Is it currently available?

If several answers require you to visit another system or ask the merchandising team, your product data probably has gaps.

AI-powered shopping makes this kind of clarity increasingly important because natural-language product discovery depends on understanding the details behind an item, not just matching an exact product name.

How Should Ecommerce Teams Handle Large Product Catalogs?

Manually reviewing thousands of products is not realistic for most ecommerce businesses.

Start with the products that matter most.

Prioritize:

1. Best-selling products
2. High-margin products
3. Products with strong organic demand
4. Products with high impressions but low clicks
5. Products with frequent feed errors
6. Products with incomplete attributes
7. Products with many variants
8. Products that frequently go in and out of stock

Then create rules for recurring problems.

For example, a catalog audit might identify that many products are missing:

• Brand
• GTIN
• Material
• Color
• Size
• Product type
• Detailed descriptions

Fixing these gaps systematically is more practical than trying to rewrite an entire catalog at once.