Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning

Abstract

Customers are prominent resources in every business for its sustainability. Therefore, predicting customer churn is significant for reducing churn, particularly in the high-churn-rate telecommunications business. To identify customers at risk of churning, tactical marketing actions can be strategized to raise the likelihood of the churn-probable customers remaining as customers. This might provide a corporation with significant savings. Hence, in this work, a churn prediction system is developed to assist telecommunication operators in detecting potential churn customers. In the proposed framework, the input data quality is improved through the processes of exploratory data analysis and data preprocessing for identifying data errors and comprehending data patterns. Then, feature engineering and data sampling processes are performed to transform the captured data into an appropriate form for classification and imbalanced data handling. An optimized ensemble learning model is proposed for classification in this framework. Unlike other ensemble models, the proposed classification model is an optimized weighted soft voting ensemble with a sequence of weights applied to weigh the prediction of each base learner with the hypothesis that specific base learners in the ensemble have more skill than others. In this optimization, Powell’s optimization algorithm is applied to optimize the ensemble weights of influence according to the base learners’ importance. The efficiency of the proposed optimally weighted ensemble learning model is evaluated in a real-world database. The empirical results show that the proposed customer churn prediction system achieves a promising performance with an accuracy score of 84% and an F1 score of 83.42%. Existing customer churn prediction systems are studied. We achieved a higher prediction accuracy than the other systems, including machine learning and deep learning models.

The causes of bank customer churn based on XGBoost and LightGBMmodels: the evidence from the Kaggle dataset

“…However, some studies suggest that XGBoost can be used in combination with other ensemble methods for achieving state-of-the-art performance [27]. In addition, Khoh et al [28] have proposed an optimized weighted soft voting ensemble learning model, shown through empirical results to have higher prediction accuracy than machine learning and deep learning models, among others, for customer churn prediction systems. Algorithmic optimization methods were also utilized by Vani Haridasan et al [29], who employed an arithmetic optimization algorithm (AOA) in conjunction with a stacked bidirectional long short-term memory (SBLSTM) model, demonstrating potential in performance compared to recent approaches.…”