Enhancing Game Customer Churn Prediction with a Stacked Ensemble Learning Model

Abstract With the rapid growth of mobile internet and smart devices, China's mobile game market has become highly competitive. Game companies face increasing costs in promoting games and acquiring new customers. In response, this paper proposes a stack-based model for game customer churn prediction. This model utilizes a comprehensive set of user features, including static, dynamic, and social network data extracted from game logs. To correct the imbalance between positive and negative examples in our dataset, we applied the Neighborhood Cleaning Rule (NCL). We then constructed the model using a stacking approach that integrates multiple machine learning models into a cohesive binary classification framework. This method systematically predicts customer churn by processing these features. Our approach was rigorously tested on a large-scale real-world dataset, comparing it against nine standard baseline methods. The results reveal that the stack-based model significantly outperforms these methods, achieving a prediction accuracy of 94%.