RETRACTED: A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Abstract

Customer churn is an important issue in increasing both the long and short-term revenues of a company. If companies identify customers’ churn behavior, they can prevent customer loss, ensure customer loyalty, and in turn gain better financial returns. The telecommunication sector is a customer-oriented sector that requires customer retention to survive in the market and, in this sector customer loss is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customers’ churn behavior. In this study, a customer churn analysis was conducted with publicly shared Telco telecom data. Predictive models were built with machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Voting), and deep learning (LSTM) methods. Besides, a 3-layered LSTM model was proposed. Accuracy, F1, precision, and recall rates were used to evaluate the models. As a result, the novel 3-layered LSTM model achieved 91% accuracy, 87% precision, 84% recall, and 89% F1 values. The proposed model is competitive with the literature.