Customer Churn Prediction Model Based on LSTM and CNN in Music Streaming

Abstract

It is critical for companies to accurately predict customer churn if they want to achieve sustained development. Previous studies have used many machine learning methods to predict customer churn. The common models cannot make full use of time series features. To overcome this shortcoming, we propose a model based on LSTM and CNN which has cross-layer connections between the LSTM layers and the convolution layers. This model can simultaneously learn latent sequential information and capture important local features from time series features. Moreover, we introduce a method of constructing new features by training an XGBoost model on the existing features. Experimental results on the real-life dataset show that the model we proposed performs better than other comparison models.