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Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning

Author

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  • K. Coussement

    (UMR CNRS 8179 - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

  • K.W. de Bock

    (UMR CNRS 8179 - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

Abstract

The online gambling industry is one of the most revenue generating branches of the entertainment business, resulting in fierce competition and saturated markets. Therefore it is essential to efficiently retain gamblers. Churn prediction is a promising new alternative in customer relationship management (CRM) to analyze customer retention. It is the process of identifying gamblers with a high probability to leave the company based on their past behavior. This study investigates whether churn prediction is a valuable option in the CRM palette of the online gambling companies. Using real-life data of poker players at bwin, single algorithms, CART decision trees and generalized additive models are benchmarked to their ensemble counterparts, random forests and GAMens. The results show that churn prediction is a valuable strategy to identify and profile those customers at risk. Furthermore, the performance of the ensembles is more robust and better than the single models. (

Suggested Citation

  • K. Coussement & K.W. de Bock, 2013. "Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning," Post-Print hal-00788063, HAL.
    • Handle: RePEc:hal:journl:hal-00788063
    DOI: 10.1016/j.jbusres.2012.12.008
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    5. Petra Posedel v{S}imovi'c & Davor Horvatic & Edward W. Sun, 2021. "Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression," Papers 2105.07671, arXiv.org, revised Jul 2021.
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    7. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.
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    9. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
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    11. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
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    14. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    15. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    16. Boto Ferreira, Mário & Costa Pinto, Diego & Maurer Herter, Márcia & Soro, Jerônimo & Vanneschi, Leonardo & Castelli, Mauro & Peres, Fernando, 2021. "Using artificial intelligence to overcome over-indebtedness and fight poverty," Journal of Business Research, Elsevier, vol. 131(C), pages 411-425.
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    18. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    19. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    20. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    21. Wei Liu & Zongshui Wang & Hong Zhao, 2020. "Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 735-757, December.
    22. Wee How Khoh & Ying Han Pang & Shih Yin Ooi & Lillian-Yee-Kiaw Wang & Quan Wei Poh, 2023. "Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning," Sustainability, MDPI, vol. 15(11), pages 1-21, May.
    23. Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.
    24. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.

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