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.
@article{arxiv.1907.03870,
title = {Profiling Players with Engagement Predictions},
author = {Ana Fernández del Río and Pei Pei Chen and África Periáñez},
journal= {arXiv preprint arXiv:1907.03870},
year = {2020}
}