Predicting Player Churn in the Gaming Industry: A Machine Learning Framework for Enhanced Retention Strategies

Rahesha  Mulla; Saiprasad Potharaju; Swapnali N Tambe; Suvarna Joshi; Kalpana Kale; Pancham Bandishti; Ruchita Patre

doi:10.59796/jcst.V15N2.2025.103

Authors

Rahesha Mulla Department of Artificial Intelligence & Machine Learning, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
Saiprasad Potharaju Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
Swapnali N Tambe Department of Information Technology, K. K.Wagh Institute of Engineering Education & Research, Nashik, MH, India
Suvarna Joshi Department of Computer Science and Engineering, MIT ADT University, Pune, India
Kalpana Kale Department of Computer Science and Engineering, MIT ADT University, Pune, India
Pancham Bandishti Department of CSE, School of Computing, MIT Art, Design and Technology University, Pune, MH, India
Ruchita Patre Department of CSE, School of Computing, MIT Art, Design and Technology University, Pune, MH, India

DOI:

https://doi.org/10.59796/jcst.V15N2.2025.103

Keywords:

predictive modeling, machine-learning model, Power-BI for prediction, churn analysis, classification

Abstract

This research presents a prediction of gaming player churn along with a thorough analysis. It employs predictive modeling techniques utilizing machine learning approaches to predict player churn (customer attrition) on gaming platforms. Using real-world gaming data from player demographics, in-game purchases, social interactions, and historical gaming behavior, this study proposes a new framework that integrates data preprocessing, segmentation, and predictive modeling to determine which players will churn. Additionally, it uses Logistic Regression and Random Forest, a powerful ensemble learning algorithm, to estimate player churn within a limited time horizon. We found that this approach accurately identified potential churners through a thorough exploration and understanding of the dataset. This predictive model provides insight into the key factors influencing player attrition, allowing game developers to take countermeasures to prevent churn risks and improve player retention strategies. In addition, Power BI insights highlight the key factors influencing player churn. These findings provide actionable recommendations for game developers to mitigate churn risks and enhance player retention strategies. This study contributes to predicting player turnover in the gaming industry, providing a valuable tool for fostering sustainable growth and profitability.

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