A Difficulty-Aware Framework for Churn Prediction and Intervention in Games

User's leaving from the system without further return, called user churn, is a severe negative signal in online games. Therefore, churn prediction and intervention are of great value for improving players' experiences and system performance. However, the problem has not been well-studied in the game scenario. Especially, some crucial factors, such as game difficulty, have not been considered for large-scale churn analysis. In this paper, a novel Difficulty-Aware Framework (DAF) for churn prediction and intervention is proposed. Firstly, a Difficulty Flow for each user is proposed, which is utilized to derive users' Personalized Perceived Difficulty during the game process. Then, a survival analysis model D-Cox-Time is designed to model the Dynamic Influence of Perceived Difficulty on player churn intention. Finally, the Personalized Perceived Difficulty (PPD) and Dynamic Difficulty Influence (DDI) are incorporated to churn prediction and intervention. The proposed DAF framework has been specified in a real-world puzzle game as an example for churn prediction and intervention. Extensive offline experiments show significant improvements in churn prediction by introducing difficulty-related features. Besides, we conduct an online intervention system to adjust difficulty dynamically in the online game. A/B test results verify that the proposed intervention system enhances user retention and engagement significantly. To the best of our knowledge, it is the first framework in games that illustrates an in-depth understanding and leveraging dynamic and personalized perceived difficulty during game playing, which is easy to be integrated with various churn prediction and intervention models.