An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naive Bayes

Customer churn is a complex challenge for burgeoning competitive organizations, especially in telecommunication. It refers to customers that swiftly leave a company for a competitor. Acquiring new customers has cost the telecommunication industry more than keeping existing customers. Traditionally, customer churn prediction (CCP) models are applied to aid in analyzing customer behavior and achieving prediction accuracy, which allows the telecommunication industry to target prior retention efforts toward them. However, only accurate CCP based on the available data or already trained supervised model is inadequate for efficient churn prediction, as existing approaches have not been shown or designed to learn with the skill of adaptation to respond quickly to changes in the customer behavior or a decision. Therefore, it is essential to design an approach that easily adapts to learn from new decision scenarios and provides instant insights. This study proposes an adaptive learning approach for this perplexing problem of CCP using the Naive Bayes classifier with a Genetic Algorithm (subclass of an Evolutionary Algorithm) based feature weighting approach. Further, the performance of the proposed approach is evaluated on publicly available datasets (i.e., BigML Telco churn, IBM Telco, and Cell2Cell) which significantly enhances the prediction performance as compared to the baseline classifier (i.e., Naive Bayes with default setting, Deep-BP-ANN, CNN, Neural Network, Linear Regression, XGBoost, KNN, Logit Boost, SVM, and PCALB methods) in terms of average precision of 0.97, 0.97, 0.98, a recall rate that stands at 0.84, 0.94, 0.97, and F1-score of 0.89, 0.96, 0.97, an MCC of 0.89, 0.96, 0.97, and accuracy 0.95, 0.97, 0.98 on subject datasets, respectively. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Customer churn is a complex challenge for competitive organizations, and this study proposes an adaptive learning approach using the Naive Bayes classifier with a Genetic Algorithm to improve churn prediction. The proposed approach outperforms baseline classifiers and enhances prediction performance on publicly available datasets.