Before revision:  In this paper, we consider developing new universal features for churn predictions for long-term players based on player in-game time spending. In particular, we measure player time spending regularity using the notion of entropy and cross-entropy from information theory as the metric to characterize variance and change in the in-game time spent by a player, from data sets of six free online games of different types. We leverage information from players' in-game time spending regularity in the form of universal features for churn prediction. Experiments show that our developed features are better at predicting churners compared to baseline features. Thus, the experiment results imply that our proposed features could utilize the information extracted from players' in-game time spending more effectively than related baseline time spending features .
After revision:  In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime .