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Predicting the churn patterns of monetizers and non-monetizers: exploring the influence of behavioral variability in churn prediction

Ruei-Yan Wu (Department of Information Management, National Central University, Taoyuan, Taiwan)
Ya-Han Hu (Department of Information Management, National Central University, Taoyuan, Taiwan)
En-Yi Chou (Department of Information Management, National Central University, Taoyuan, Taiwan)

Internet Research

ISSN: 1066-2243

Article publication date: 14 January 2025

36

Abstract

Purpose

Although prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.

Design/methodology/approach

This study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).

Findings

The findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.

Originality/value

This study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

Keywords

Acknowledgements

Funding: This research was supported by the National Science and Technology Council, Taiwan (109-2410-H-008-059-MY2; 111-2410-H-008-026-MY2; 113-2410-H-008-063-MY3).

Citation

Wu, R.-Y., Hu, Y.-H. and Chou, E.-Y. (2025), "Predicting the churn patterns of monetizers and non-monetizers: exploring the influence of behavioral variability in churn prediction", Internet Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/INTR-05-2024-0747

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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