  The possibility of using player engagement predictions to profile high
spending video game users is explored. In particular, individual-player
survival curves in terms of days after first login, game level reached and
accumulated playtime are used to classify players into different groups.
Lifetime value predictions for each player---generated using a deep learning
method based on long short-term memory---are also included in the analysis, and
the relations between all these variables are thoroughly investigated. Our
results suggest this constitutes a promising approach to user profiling.
