ChOracle: A Unified Statistical Framework for Churn Prediction

TL;DR

ChOracle is a unified statistical framework that predicts user churn by modeling return times with Temporal Point Processes and RNNs, incorporating latent loyalty variables for improved accuracy.

Contribution

It introduces a novel RNN-based model combining Temporal Point Processes and latent variables for more general and efficient churn prediction.

Findings

Outperforms existing models on real-world datasets

Effectively models user loyalty through latent variables

Provides a scalable approach for churn prediction

Abstract

User churn is an important issue in online services that threatens the health and profitability of services. Most of the previous works on churn prediction convert the problem into a binary classification task where the users are labeled as churned and non-churned. More recently, some works have tried to convert the user churn prediction problem into the prediction of user return time. In this approach which is more realistic in real world online services, at each time-step the model predicts the user return time instead of predicting a churn label. However, the previous works in this category suffer from lack of generality and require high computational complexity. In this paper, we introduce \emph{ChOracle}, an oracle that predicts the user churn by modeling the user return times to service by utilizing a combination of Temporal Point Processes and Recurrent Neural Networks. Moreover, we incorporate latent variables into the proposed recurrent neural network to model the latent user loyalty to the system. We also develop an efficient approximate variational algorithm for learning parameters of the proposed RNN by using back propagation through time. Finally, we demonstrate the superior performance of ChOracle on a wide variety of real world datasets.