IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v15y2017i1d10.1007_s11129-017-9180-2.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11129-017-9180-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11129-017-9180-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    2. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
    3. Joseph P. Redden, 2008. "Reducing Satiation: The Role of Categorization Level," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(5), pages 624-634, August.
    4. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    5. repec:cup:judgdm:v:6:y:2011:i:3:p:230-238 is not listed on IDEAS
    6. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    7. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    8. Leif D. Nelson & Tom Meyvis & Jeff Galak, 2009. "Enhancing the Television-Viewing Experience through Commercial Interruptions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 36(2), pages 160-172.
    9. Andrés Musalem & Eric T. Bradlow & Jagmohan S. Raju, 2009. "Bayesian estimation of random‐coefficients choice models using aggregate data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 490-516, April.
    10. Jeff Galak & Justin Kruger & George Loewenstein, 2013. "Slow Down! Insensitivity to Rate of Consumption Leads to Avoidable Satiation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 39(5), pages 993-1009.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Inwon Kang & Yiya Zhang & Sungjoon Yoo, 2020. "Elaboration of Social Media Performance Measures: From the Perspective of Social Media Discontinuance Behavior," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    2. Appel, Gil & Libai, Barak & Muller, Eitan & Shachar, Ron, 2020. "On the monetization of mobile apps," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 93-107.
    3. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    4. Haenlein, Michael & Libai, Barak & Muller, Eitan, 2023. "Satiation and cross promotion: Selling and swapping users in mobile games," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 342-361.
    5. Subodha Kumar & Xiaowei Mei & Liangfei Qiu & Lai Wei, 2020. "Watching Ads for Free Mobile Data: A Game-Theoretic Analysis of Sponsored Data with Reward Task," Working Papers 20-08, NET Institute.
    6. Numminen, Emil & Sällberg, Henrik & Wang, Shujun, 2022. "The impact of app revenue model choices for app revenues: A study of apps since their initial App Store launch," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 325-336.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
    2. Yanwen Wang & Chunhua Wu & Ting Zhu, 2019. "Mobile Hailing Technology and Taxi Driving Behaviors," Marketing Science, INFORMS, vol. 38(5), pages 734-755, September.
    3. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
    4. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
    5. Cammy Crolic & Chris Janiszewski, 2016. "Hedonic Escalation: When Food Just Tastes Better and Better," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 43(3), pages 388-406.
    6. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    7. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    8. Sha Yang & Yi Zhao & Ravi Dhar, 2010. "Modeling the Underreporting Bias in Panel Survey Data," Marketing Science, INFORMS, vol. 29(3), pages 525-539, 05-06.
    9. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    10. Reich, Taly & Fulmer, Alexander G. & Dhar, Ravi, 2022. "In the face of self-threat: Why ambivalence heightens people’s willingness to act," Organizational Behavior and Human Decision Processes, Elsevier, vol. 168(C).
    11. Sungho Park & Sachin Gupta, 2011. "A Regime-Switching Model of Cyclical Category Buying," Marketing Science, INFORMS, vol. 30(3), pages 469-480, 05-06.
    12. Christof Naumzik & Stefan Feuerriegel & Markus Weinmann, 2022. "I Will Survive: Predicting Business Failures from Customer Ratings," Marketing Science, INFORMS, vol. 41(1), pages 188-207, January.
    13. Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
    14. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    15. Verhoef, Peter C. & Venkatesan, Rajkumar & McAlister, Leigh & Malthouse, Edward C. & Krafft, Manfred & Ganesan, Shankar, 2010. "CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 121-137.
    16. Liye Ma & Baohong Sun & Sunder Kekre, 2015. "The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, INFORMS, vol. 34(5), pages 627-645, September.
    17. Durango-Cohen, Elizabeth J. & Torres, Ramón L. & Durango-Cohen, Pablo L., 2013. "Donor Segmentation: When Summary Statistics Don't Tell the Whole Story," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 172-184.
    18. German Zenetti & Thomas Otter, 2014. "Bayesian estimation of the random coefficients logit from aggregate count data," Quantitative Marketing and Economics (QME), Springer, vol. 12(1), pages 43-84, March.
    19. Lemmens, Aurélie & Croux, Christophe & Stremersch, Stefan, 2012. "Dynamics in the international market segmentation of new product growth," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 81-92.
    20. Peter Ebbes & Oded Netzer, 2022. "Using Social Network Activity Data to Identify and Target Job Seekers," Management Science, INFORMS, vol. 68(4), pages 3026-3046, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:qmktec:v:15:y:2017:i:1:d:10.1007_s11129-017-9180-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.