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

• Published in: Applied soft computing Vol. 137; p. 110103
• Main Authors: Amin, Adnan, Adnan, Awais, Anwar, Sajid
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2023
• Subjects: Adaptive learning; Churn prediction; Evolutionary algorithm; Genetic Algorithm; Naïve Bayes; Telecommunication; Adaptive learning; Telecommunication; Naïve Bayes; Churn prediction; Evolutionary algorithm; Genetic Algorithm
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110103

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 Naïve 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., Naïve 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. •Assigned weights to features using genetic algorithm without any support of the domain expert or feature engineering to improve the classifier’s performance.•Introduces a novel approach for Adaptive Customer Churn Prediction in the telecommunication industry that is capable of accurately predicting hard instances in data generation over time.•Designed a simulated expert to enhance the previous learning experience of the classifier by mapping the misclassified instances.