Paper ID

3351

Description

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.

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A Hidden Markov Model of Mobile Game-play 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.