Abstract

A comprehensive understanding of individual customer value is crucial to any successful customer relationship management strategy. It is also the key to building products for long-term value returns. Modeling customer lifetime value (CLTV) can be fraught with technical difficulties, however, due to both the noisy nature of user-level behavior and the potentially large customer base. Here we describe a new CLTV system that solves these problems. This was built at Groupon, a large global e-commerce company, where confronting the unique challenges of local commerce means quickly iterating on new products and the optimal inventory to appeal to a wide and diverse audience. Given current purchaser frequency we need a faster way to determine the health of individual customers, and given finite resources we need to know where to focus our energy.

Our CLTV system predicts future value on an individual user basis with a random forest model which includes features that account for nearly all aspects of each customer’s relationship with our platform. This feature set includes those quantifying engagement via email and our mobile app, which give us the ability to predict changes in value far more quickly than models based solely on purchase behavior. We further model different customer types, such as one-time buyers and power users, separately so as to allow for different feature weights and to enhance the interpretability of our results. Additionally, we developed an economical scoring framework wherein we re-score a user when any trigger events occur and apply a decay function otherwise, to enable frequent scoring of a large customer base with a complex model. This system is deployed, predicting the value of hundreds of millions of users on a daily cadence, and is actively being used across our products and business initiatives.


Filed under: Classification


Comments