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Retaining premium players is key to the success of free-to-play games, butmost of them do not start purchasing right after joining the game. Byexploiting the exceptionally rich datasets recorded by modern videogames--which provide information on the individual behavior of each and everyplayer--survival analysis techniques can be used to predict what players aremore likely to become paying (or even premium) users and when, both in terms oftime and game level, the conversion will take place. Here we show that atraditional semi-parametric model (Cox regression), a random survival forest(RSF) technique and a method based on conditional inference survival ensemblesall yield very promising results. However, the last approach has the advantageof being able to correct the inherent bias in RSF models by dividing theprocedure into two steps: first selecting the best predictor to perform thesplitting and then the best split point for that covariate. The proposedconditional inference survival ensembles method could be readily used inoperational environments for early identification of premium players and theparts of the game that may prompt them to become paying users. Such knowledgewould allow developers to induce their conversion and, more generally, tobetter understand the needs of their players and provide them with apersonalized experience, thereby increasing their engagement and paving the wayto higher monetization.

From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning