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Understanding repeat playing behavior in casual games using a Bayesian data augmentation approach

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  • Sam K. Hui

    (University of Houston)

Abstract

With an estimated market size of nearly $18 billion in 2016, casual games (games played over social networks or mobile devices) have become increasingly popular. Because most casual games are free to install, understanding repeat playing behavior is important for game developers as it directly drives advertising revenue. Game developers are keenly interested in benchmarking their game versus the market average, and understanding how genre and various game mechanics drive repeat playing behavior. Such cross-sectional analysis, however, is difficult to conduct because individual-level data on competitors’ games are not publicly available, and that the casual gaming industry is highly fragmented with each firm making only a handful of games. I develop a Bayesian approach, based on a parsimonious Hidden Markov Model at the individual level in conjunction with data augmentation, to study repeat playing behavior using only publicly available data. After applying the proposed approach to a sample of 379 casual games, I find that the average daily attrition rate across game is around 36.5%, with an average “play” rate of 47.9%, resulting in an average ARPU (average revenue per user) across games of around 20.5 cents. Certain genres are linked to higher attrition rates and play rates. In addition, giving out a “daily bonus” or limiting the amount of time that gamers can play each day are associated with a 17.7% and 16.4% higher ARPU, respectively.

Suggested Citation

  • Sam K. Hui, 2017. "Understanding repeat playing behavior in casual games using a Bayesian data augmentation approach," Quantitative Marketing and Economics (QME), Springer, vol. 15(1), pages 29-55, March.
    • Handle: RePEc:kap:qmktec:v:15:y:2017:i:1:d:10.1007_s11129-017-9180-2
    DOI: 10.1007/s11129-017-9180-2
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    References listed on IDEAS

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