  Machine learning competitions such as those organized by Kaggle or KDD
represent a useful benchmark for data science research. In this work, we
present our winning solution to the Game Data Mining competition hosted at the
2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The
contest consisted of two tracks, and participants (more than 250, belonging to
both industry and academia) were to predict which players would stop playing
the game, as well as their remaining lifetime. The data were provided by a
major worldwide video game company, NCSoft, and came from their successful
massively multiplayer online game Blade and Soul. Here, we describe the long
short-term memory approach and conditional inference survival ensemble model
that made us win both tracks of the contest, as well as the validation
procedure that we followed in order to prevent overfitting. In particular,
choosing a survival method able to deal with censored data was crucial to
accurately predict the moment in which each player would leave the game, as
censoring is inherent in churn. The selected models proved to be robust against
evolving conditions---since there was a change in the business model of the
game (from subscription-based to free-to-play) between the two sample datasets
provided---and efficient in terms of time cost. Thanks to these features and
also to their a ability to scale to large datasets, our models could be readily
implemented in real business settings.
