An Optimized Customer Churn Prediction Approach Based on Regularized Bidirectional Long Short-Term Memory Model

Customer churn is the rate at which customers discontinue doing business with a company over a given time period. It is an essential measure for businesses to monitor high churn rates, as they often indicate underlying issues with services, products, or customer experience, resulting in considerable income loss. Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth. Traditional machine learning (ML) models often struggle to capture complex temporal dependencies in client behavior data. To address this, an optimized deep learning (DL) approach using a Regularized Bidirectional Long Short-Term Memory (RBiLSTM) model is proposed to mitigate overfitting and improve generalization error. The model integrates dropout, L2-regularization, and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns. Moreover, this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model. Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models, such as Long Short-Term Memory (LSTM) and Deep Neural Network (DNN), in churn prediction performance and stability. The proposed approach achieves 96.1% accuracy, compared with LSTM and DNN, which attain 94.5% and 94.1% accuracy, respectively. These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.