Product Recommendations are AI-driven suggestions that predict which products a customer is most likely to purchase, based on their behavior, purchase history, and real co-purchase patterns.
Common recommendation approaches:
- 1Association rule-based: "Frequently bought together..." — Uses Market Basket Analysis to surface products that genuinely co-occur in real orders
- 2Content-based filtering: "Similar to items you've viewed..." — Uses product attribute similarity
- 3Behavioral targeting: "Based on what you've browsed..." — Uses browsing behavior and engagement signals
- 4Hybrid approaches: Combine multiple methods for better accuracy
Where recommendations appear in e-commerce:
- Product detail pages ("Frequently Bought Together")
- Cart page ("You might also need...")
- Post-purchase emails ("Complete your collection")
- Homepage personalization ("Recommended for you")
- Search results re-ranking
Impact on revenue:
Product recommendations typically drive 10-30% of e-commerce revenue. The key is relevance — irrelevant recommendations not only fail to convert, they can actively harm the shopping experience and erode trust.
The most effective recommendation systems combine multiple data signals: what products are actually purchased together (Market Basket Analysis), what customers have browsed (behavioral data), and which customer segments respond to which product categories (segmentation).
