Product Recommendation Engines: How Personalisation Lifts eCommerce Revenue
How recommendation engines work, the main approaches (collaborative, content-based, hybrid), where to place recommendations and how to measure their impact.
When shoppers see products that match their interests, they browse longer and buy more. Recommendation engines make that happen automatically, turning browsing and purchase data into relevant suggestions across your store and app.
Key takeaways
- Start simple: bestsellers and frequently bought together already add value.
- Hybrid models combine behaviour and product attributes for better relevance.
- Placement matters as much as the algorithm.
- A/B test to prove revenue impact.
Main approaches
| Approach | How it works | Strength |
|---|---|---|
| Popularity | Bestsellers and trending items | Works with little data |
| Content-based | Similar attributes, categories, descriptions | Handles new products |
| Collaborative filtering | People who bought X also bought Y | Discovers non-obvious links |
| Hybrid | Combines the above | Best overall relevance |
Where to place recommendations
- Product pages: similar items and frequently bought together
- Cart: complementary add-ons
- Homepage: personalised picks for returning visitors
- Search: boosting relevant results
- Emails: post-purchase and abandoned-browse recommendations
Want smarter personalisation? See our recommendation engine solution or discuss your catalogue.
Measuring impact
Run A/B tests comparing pages with and without recommendations. Track click-through on recommendation widgets, conversion rate, average order value and revenue per session. Monitor diversity too, so customers are not shown the same few products repeatedly.
Privacy and trust
Use first-party data responsibly, explain personalisation in your privacy policy and give users control where appropriate. Relevant, respectful recommendations build loyalty.
