Case Study
How UberEats Used Personalized Recommendations to Increase Revenue
+7%
Increase in Revenue per Customer
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%.
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