Improved retention analysis in freemium role‐playing games by jointly modelling players’ motivation, progression and churn

We consider user retention analytics for online freemium role‐playing games (RPGs). RPGs constitute a very popular genre of computer‐based games that, along with a player's gaming actions, focus on the development of the player's in‐game virtual character through a persistent exploration of the gaming environment. Most RPGs follow the freemium business model in which the gamers can play for free but they are charged for premium add‐on amenities. As with other freemium products, RPGs suffer from the curse of high dropout rates. This makes retention analysis extremely important for successful operation and survival of their gaming portals. Here, we develop a disciplined statistical framework for retention analysis by modelling multiple in‐game player characteristics along with the dropout probabilities. We capture players’ motivations through engagement times, collaboration and achievement score at each level of the game, and jointly model them using a generalized linear mixed model (glmm) framework that further includes a time‐to‐event variable corresponding to churn. We capture the interdependencies in a player's level‐wise engagement, collaboration, achievement with dropout through a shared parameter model. We illustrate interesting changes in player behaviours as the gaming level progresses. The parameters in our joint model were estimated by a Hamiltonian Monte Carlo algorithm which incorporated a divide‐and‐recombine approach for increased scalability in glmm estimation that was needed to accommodate our large longitudinal gaming data‐set. By incorporating the level‐wise changes in a player's motivations and using them for dropout rate prediction, our method greatly improves on state‐of‐the‐art retention models. Based on data from a popular action based RPG, we demonstrate the competitive optimality of our proposed joint modelling approach by exhibiting its improved predictive performance over competitors. In particular, we outperform aggregate statistics based methods that ignore level‐wise progressions as well as progression tracking non‐joint model such as the Cox proportional hazards model. We also display improved predictions of popular marketing retention statistics and discuss how they can be used in managerial decision making.