Learning User Play-Then-Pay Behaviors in Digital Games: A Dynamic Perspective
提出双层隐马尔可夫模型，基于真实数据揭示用户游戏参与和付费意愿的动态转换规律，发现高参与状态导致付费意愿向上转换，而低参与状态则维持低付费意愿，并验证了流量补贴策略的有效性。
The gaming industry has emerged as a critical force in the digital content economy, yet managing user behavior to drive sustained activity and monetization remains a complex operational challenge. In this study, we propose a two-layer hidden Markov model to capture users’ gameplay and payment behaviors by constructing a play-then-pay chain that links user engagement to subsequent purchase intention dynamics. Drawing on a real-world dataset, we uncover three levels of engagement states measuring the degree of stickiness with the focal game, as well as two levels of purchase intention states describing one’s willingness to pay. We find that a higher engagement state is associated with a volatile transition pattern and leads to a higher upward transition tendency in purchase intention, while low and medium engagement states tend to maintain a low purchase intention state. We also examine several factors that affect the transitions of these psychological states. The analysis reveals that user activity in same-type games enhances upward transitions only among users in the medium engagement state, without affecting users in the high engagement state, and exhibits no significant effect on purchase intentions. In contrast, user activity in different types of games has a negative effect on users in both low and high engagement states. Our state-dependent outcomes suggest that the managers’ strategies are more effective when targeted toward users with low engagement and purchase intention states. Further experimental analysis supports the effectiveness of the proposed play-then-pay chain for predicting users’ behaviors. Our policy simulation demonstrates that traffic subsidization effectively redirects user attention to the focal game, with interventions targeting different-type games yielding greater improvements in propensities for both gameplay and payment behavior compared to same-type games. Our work provides managerial implications for platform managers.