  Retaining premium players is key to the success of free-to-play games, but
most of them do not start purchasing right after joining the game. By
exploiting the exceptionally rich datasets recorded by modern video
games--which provide information on the individual behavior of each and every
player--survival analysis techniques can be used to predict what players are
more likely to become paying (or even premium) users and when, both in terms of
time and game level, the conversion will take place. Here we show that a
traditional semi-parametric model (Cox regression), a random survival forest
(RSF) technique and a method based on conditional inference survival ensembles
all yield very promising results. However, the last approach has the advantage
of being able to correct the inherent bias in RSF models by dividing the
procedure into two steps: first selecting the best predictor to perform the
splitting and then the best split point for that covariate. The proposed
conditional inference survival ensembles method could be readily used in
operational environments for early identification of premium players and the
parts of the game that may prompt them to become paying users. Such knowledge
would allow developers to induce their conversion and, more generally, to
better understand the needs of their players and provide them with a
personalized experience, thereby increasing their engagement and paving the way
to higher monetization.
