The "Recently Purchased" recommendation algorithm tailors recommendations based on a user's recent purchase history. By analyzing the user's most recent transactions, this algorithm suggests items that are closely related to their latest purchases.
The goal is to enhance user engagement and satisfaction by presenting products that align with the user's current preferences and buying patterns. This algorithm is particularly effective for promoting cross-selling opportunities, encouraging users to explore and purchase additional items that complement their recent acquisitions.
How does it work?
In a Experro, the "Recently Purchased" algorithm identifies users, analyzes their recent purchase history, and recommends additional items that complement their latest transactions. These suggestions are presented in the Experro interface, encouraging users to explore and make further purchases based on their recent buying patterns. The algorithm dynamically updates recommendations as users continue to make new purchases, ensuring ongoing relevance.
When should you use this algorithm?
Do you have too much user data? Do you want to offer personalized recommendations to keep users coming back? Collaborative filtering is your go-to solution. It helps you with online shopping, watching movies, and connecting with friends by suggesting things you'll like.
Example
Here is an example of how the Recently Purchased algorithm could be used in Experro:
- A shopper visits an e-commerce website and views a product page for a designer chair.
- Experro uses the Recently Purchased algorithm to predict which other products the shopper is likely to be interested in based on their recently purchased history of products.
-
It displays a list of recently purchased products for the shopper on the product page.
To create or learn more about widgets, go to our Widget Configurator article and get started.