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Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model

Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model

Sivasankar Karuppaiah, N. P. Gopalan
Copyright: © 2021 |Volume: 14 |Issue: 2 |Pages: 13
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781799860013|DOI: 10.4018/JITR.2021040109
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MLA

Karuppaiah, Sivasankar, and N. P. Gopalan. "Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model." JITR vol.14, no.2 2021: pp.174-186. http://doi.org/10.4018/JITR.2021040109

APA

Karuppaiah, S. & Gopalan, N. P. (2021). Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model. Journal of Information Technology Research (JITR), 14(2), 174-186. http://doi.org/10.4018/JITR.2021040109

Chicago

Karuppaiah, Sivasankar, and N. P. Gopalan. "Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model," Journal of Information Technology Research (JITR) 14, no.2: 174-186. http://doi.org/10.4018/JITR.2021040109

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Abstract

In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of prediction followed by a recommendation to reduce customer churn. The first level involves identifying effective models to predict potential churners. This is followed by result segregation, CLV-based prediction, and user shortlisting for offers. Experimental results indicate high efficiencies in predicting potential churners and non-churners. The proposed model is found to reduce the overall loss by up to 50% in comparison to state-of-the-art models.

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