Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning
This work addresses player engagement and monetization challenges for video game studios, but it is incremental as it builds on existing churn prediction methods with specific behavioral insights.
The study tackled player retention and in-game purchasing by analyzing churn and purchase churn behaviors, identifying profiles like false churners and zombies, and found that excluding certain churner types from training data significantly improved prediction models.
As video games attract more and more players, the major challenge for game studios is to retain them. We present a deep behavioral analysis of churn (game abandonment) and what we called "purchase churn" (the transition from paying to non-paying user). A series of churning behavior profiles are identified, which allows a classification of churners in terms of whether they eventually return to the game (false churners)--or start purchasing again (false purchase churners)--and their subsequent behavior. The impact of excluding some or all of these churners from the training sample is then explored in several churn and purchase churn prediction models. Our results suggest that discarding certain combinations of "zombies" (players whose activity is extremely sporadic) and false churners has a significant positive impact in all models considered.