Risk assessment of customer churn in telco using FCLCNN-LSTM model

Telco players are constantly evaluating their loss in customer revenue or customer churn, especially on the prevention, control, and risk assessment of churn. In this paper, a domain-order based shallow fusion deep learning model, the Fully Connected Layer Convolutional Neural Network - Long Short-Term Memory (FCLCNN-LSTM) is designed and applied to a decision support system to assess the risk of customer churn. First, a majority least absolute shrinkage and selection operator (Maj-LASSO) algorithm is proposed to identify churn predictors that have an important impact on classification. The proposed Maj-LASSO algorithm is different from previous studies that solved the study of unbalanced data and feature selection separately, but solved both as one problem to give the feature selection method under unbalanced data. Then, a fully connected layer based on multiple ReLU neurons is used to balance the importance of the features. Doing so addresses the problem where some key features are overlooked due to their unequal contribution to the risk assessment of telco customer churn. Third, a 2D convolutional neural network based on domain information and an LSTM model based on ordinal information are combined to learn the complex mapping between the features and labels, to improve the accuracy and generalizability of the predictive model. The proposed FCLCNN-LSTM model is different from the previous single spatial model or temporal model, but improves the network classification feature extraction capability from both temporal and spatial dimensions, thus innovatively solving the problem of model development for large-scale and high-dimensional data, and applying it in the field of telecommunication customer churn prediction. Finally, using three public customer churn datasets from the Kaggle and UCI platform, the FCLCNN-LSTM model is compared against other classification models, namely, Logistic Regression, Support Vector Machine, Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Network, and Long Short-Term Memory. The experimental results inform that the accuracy of the FCLCNN-LSTM is 3.43% higher than that of the other comparison models, while the AUC is 4.84% higher. Thus, the FCLCNN-LSTM model provides a better decision-making reference for telco players to identify potential churners.