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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.

Optimize My Feed

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 recommendationsWooCommerce AI integrationShopify AI integration

Connect your feed in minutes

SaleSense syncs your Google Shopping or custom XML feed automatically, twice daily. Start free — no developer needed.

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