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Better data · better recommendations · more revenue

Product Feed Optimization
for AI-Powered Recommendations

AI recommendations are only as good as the product data they're built on. A feed with vague titles, missing categories, and no tags produces generic suggestions. A well-structured feed produces specific, high-converting recommendations.

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The 6 fields that drive AI recommendation quality

Title
✓"Blue Linen Shirt - Men's Slim Fit - XL"
✕"Shirt 1234"

Include: material, gender, fit, size. The AI uses the title to match customer queries semantically.

Description
✓Fabric, care instructions, dimensions, use case
✕Marketing copy only, no product facts

Descriptions train the AI on product specifics. Facts outperform adjectives for recommendation accuracy.

Category
✓"Apparel > Men > Tops > Shirts > Formal"
✕"Clothes"

Deep category hierarchy lets the AI filter by type when the customer's query is category-level.

Tags
✓"summer, linen, office, slim-fit, sale"
✕(empty)

Tags are the AI's secondary matching layer. Use occasion, season, material, and style tags.

Sale price
✓salePrice: 89.90 (was 119.90)
✕Only listing the sale price without the original

When both prices are present, the AI mentions the saving proactively — a proven conversion trigger.

Availability
✓Real-time stock status per variant
✕Static 'in stock' with no update frequency

Stale availability data causes the AI to recommend out-of-stock items. Sync at least twice daily.

Related guides

AI product recommendations →WooCommerce AI integration →Shopify AI integration →

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