Customer churn prediction for web browsers

Abstract

In the competitive web browser market, identifying potential churners is critical to decreasing the loss of existing customers. Churn prediction based on customer behaviors plays a vital role in customer retention strategies. However, traditional churn prediction algorithms such as Tree-based models cannot exploit the temporal characteristics of browser customers behaviors, while sequence models cannot explicitly extract the information between multiple behaviors. To meet this challenge, we propose a novel model named Multivariate Behavior Sequence Transformer (MBST) with two complementary attention mechanisms to explore the temporal and behavioral information separately. Furthermore, a Tree-based classifier is attached for churn prediction instead of using the multilayer perceptron. Extensive experiments on a real-world Tencent QQ browser dataset with over 600,000 samples demonstrate that the proposed MBST achieves the F-score of 82.72% and the Area Under Curve (AUC) of 93.75%, which significantly outperforms state-of-the-art methods in terms of churn prediction.