A personalized product recommendation suggests a product or offer using what a customer has bought, browsed, or engaged with. The aim is to make a message useful at the customer’s current stage, rather than send the same promotion to everyone. A recommendation might introduce a complementary item after a purchase or anticipate a changing need over time. section 1.3.2 section 7.3
The most useful signal depends on the decision being made:
- Recent purchases can suggest a relevant next step. A customer who buys hiking boots might receive an email about hiking socks, backpacks, or gear-care advice. section 7.3
- Patterns over time can signal a new life stage. Falling diaper purchases, for example, could prompt potty-training tips and related offers instead of a generic repeat-purchase message. section 1.3.2
- Audience segments can make recommendations more precise. Purchase history and browsing behavior can guide targeted email campaigns; results can then show which product categories merit more attention. section 7.7.6
Recommendations can appear in email or in-app messages, with SMS and push notifications offering other ways to reach customers based on their behavior. A data clean room offers a different activation option: an e-commerce platform can combine purchase history with a publisher’s website-behavior data to inform recommendations. The channel or data arrangement should serve the customer need, not dictate the offer. section 7.3 section 8.2
Measure the lift, then refine
A specialty retailer segmented customers by purchases and browsing, then used targeted email and paid social. In an incrementality test, customers receiving personalized product recommendations generated 32% more revenue than a control group. Some product categories responded better than others, so the retailer prioritized those categories and adjusted its messaging. The 32% figure describes this test, not a result every campaign should expect. section 7.7.6
Treat recommendation quality as an ongoing decision. Use the behavior customers share to form a relevant suggestion, test whether it adds value, and feed the result into the next campaign. Clear data-use policies and customer control over data and communication preferences help make that relationship sustainable. section 1.3.2 section 7.7.6
