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

Wee How Khoh, Ying Han Pang (), Shih Yin Ooi, Lillian-Yee-Kiaw Wang and Quan Wei Poh
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Wee How Khoh: Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
Ying Han Pang: Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
Shih Yin Ooi: Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
Lillian-Yee-Kiaw Wang: School of Information Technology, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Subang Jaya 47500, Malaysia
Quan Wei Poh: Winnefy Enterprise, Jalan SD 2/6, Taman Sri Duyong 2, Melaka 75460, Malaysia

Sustainability, 2023, vol. 15, issue 11, 1-21

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.

Keywords: churn prediction; business sustainability; telecommunication; ensemble learning; weight optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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