Case Study

How UberEats Used Personalized Recommendations to Increase Revenue


Increase in Revenue per Customer


When Uber launched their Eats service in Singapore, a key objective was to increase repeat orders on the application to maximize lifetime value. 

Analyses showed that high-value customers that ordered frequently did so by having consistent variety in restaurant and cuisine selection.

The key opportunity was to create more personalized ways to up-sell customers into ordering different varieties of restaurants to increase repeat orders and thus customer lifetime value.


A collaborative filtering recommendation algorithm was developed which recommended new restaurants for users to order from based on their order history and behavior. 

This was applied to Uber’s re-marketing campaigns via push, e-mail, and in-app notifications to incentivize users to order from different types of restaurants.


The strategy applied to Uber’s marketing campaigns successfully converted users into selecting different varieties of restaurants, increasing orders and revenue per customer by 7%.

Our data scientists have worked with companies like

Why Measurly

Gain your competitive edge

Tailored to your business

Data models fully customized to your business and unique challenges

Best-in-class methodology

Advanced ML techniques coupled with powerful human guidance

Built for scale & automation

Fully automated models that allow you to improve engagement at scale

Increase your customer lifetime value with predictive ML personalization

Book a free tailored demo to learn how Measurly can help your business grow.