A Hidden Markov Model of Mobile Gameplay Volume and Reward Ads Watching Behavior.

Player engagement determines his gaming activities and is of great importance to publishers. This paper investigates two dimensions of players' activities - playing volume and reward ads watching. To be specific, we propose a hidden Markov model that captures the player engagement as a hidden state and calibrate it on a detailed clickstream data from a mobile gaming company. Our findings reveal that (1) Cumulative challenge level can motivate medium engagement players to move to a higher state. (2) Cumulative playing time deters low engagement players from transiting a higher state, but keeps high engagement players from leaving. (3) There is a complementary effect between free coins obtained from mission completion and reward ads watching behavior among high engagement players, and we do not find evidence of substitution between in-game purchases and reward ads watching. [ABSTRACT FROM AUTHOR]