Product Recommendation Engine
AI-powered software that analyses customer behaviour and product data to suggest relevant products to shoppers, powering "You may also like" and "Customers also bought" features.
In-Depth Explanation
A product recommendation engine uses algorithms and machine learning to suggest relevant products to individual shoppers based on their behaviour, preferences, and purchase history. Recommendations power many of the personalised product suggestions seen across e-commerce sites and marketing emails.
Recommendation approaches:
- Collaborative filtering: Recommends based on similar customers' behaviour ("Customers who bought X also bought Y")
- Content-based filtering: Recommends based on product attributes ("Similar products to what you viewed")
- Hybrid: Combines collaborative and content-based approaches
- Session-based: Recommends based on current browsing session behaviour
- Contextual: Adjusts recommendations based on time, device, or season
Where recommendations appear:
- Product pages: "You may also like", "Frequently bought together"
- Cart page: "Complete your order" suggestions
- Homepage: "Recommended for you" based on history
- Category pages: "Trending in this category"
- Email: Personalised product recommendations in emails
- Search results: Recommended products alongside search results
- Post-purchase: "Based on your recent purchase" in follow-up emails
Recommendation engine platforms:
- Nosto: AI-powered personalisation for e-commerce
- Dynamic Yield: Personalisation and recommendation engine
- Barilliance: E-commerce personalisation suite
- Klevu: AI search and recommendations
- Shopify built-in: Basic product recommendations
- Klaviyo: Personalised product recommendations in email
Key recommendation metrics:
- Click-through rate on recommendations
- Conversion rate of recommended products
- Revenue attributed to recommendations
- Average order value impact
- Customer engagement with recommendation widgets
Business Context
Product recommendations typically generate 10-35% of total e-commerce revenue, with personalised recommendations converting 5-10x better than non-personalised product suggestions.
How Clever Ops Uses This
Clever Ops implements AI-powered product recommendation engines for Australian e-commerce businesses. We integrate recommendation platforms with e-commerce stores and email marketing tools, configure algorithms for each client product catalogue, and track the revenue contribution of recommendations to continuously optimise their effectiveness.
Example Use Case
"A sports equipment retailer implements Nosto recommendation engine, showing personalised "Complete your setup" recommendations on product pages and in cart, generating an additional 18% of total revenue through recommended product purchases."