Forecasting Customer Churn using Machine Learning and Deep Learning Approaches

Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and Call-Detailed Record (CDR). For this purpose, logistic regression, random forest, decision tree, k-nearest neighbor, extreme gradient boosting, and naive Bayes techniques are evaluated as machine learning approaches while artificial neural networks and convolutional neural networks are assessed as deep learning models. Experiment results indicate that the random forest method exhibits superior performance with 79.94% accuracy for the IBM Watson dataset and 96.34% accuracy for the Call Detailed Report (CDR) dataset. To demonstrate the effectiveness of the suggested framework, a comparison with the state-of-the-art studies is performed.