IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v185y2022i1p102-133.html
   My bibliography  Save this article

Improved retention analysis in freemium role‐playing games by jointly modelling players’ motivation, progression and churn

Author

Listed:
  • Bikram Karmakar
  • Peng Liu
  • Gourab Mukherjee
  • Hai Che
  • Shantanu Dutta

Abstract

We consider user retention analytics for online freemium role‐playing games (RPGs). RPGs constitute a very popular genre of computer‐based games that, along with a player's gaming actions, focus on the development of the player's in‐game virtual character through a persistent exploration of the gaming environment. Most RPGs follow the freemium business model in which the gamers can play for free but they are charged for premium add‐on amenities. As with other freemium products, RPGs suffer from the curse of high dropout rates. This makes retention analysis extremely important for successful operation and survival of their gaming portals. Here, we develop a disciplined statistical framework for retention analysis by modelling multiple in‐game player characteristics along with the dropout probabilities. We capture players’ motivations through engagement times, collaboration and achievement score at each level of the game, and jointly model them using a generalized linear mixed model (glmm) framework that further includes a time‐to‐event variable corresponding to churn. We capture the interdependencies in a player's level‐wise engagement, collaboration, achievement with dropout through a shared parameter model. We illustrate interesting changes in player behaviours as the gaming level progresses. The parameters in our joint model were estimated by a Hamiltonian Monte Carlo algorithm which incorporated a divide‐and‐recombine approach for increased scalability in glmm estimation that was needed to accommodate our large longitudinal gaming data‐set. By incorporating the level‐wise changes in a player's motivations and using them for dropout rate prediction, our method greatly improves on state‐of‐the‐art retention models. Based on data from a popular action based RPG, we demonstrate the competitive optimality of our proposed joint modelling approach by exhibiting its improved predictive performance over competitors. In particular, we outperform aggregate statistics based methods that ignore level‐wise progressions as well as progression tracking non‐joint model such as the Cox proportional hazards model. We also display improved predictions of popular marketing retention statistics and discuss how they can be used in managerial decision making.

Suggested Citation

  • Bikram Karmakar & Peng Liu & Gourab Mukherjee & Hai Che & Shantanu Dutta, 2022. "Improved retention analysis in freemium role‐playing games by jointly modelling players’ motivation, progression and churn," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 102-133, January.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:1:p:102-133
    DOI: 10.1111/rssa.12730
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12730
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12730?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
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Rizopoulos & Geert Verbeke & Geert Molenberghs, 2008. "Shared parameter models under random effects misspecification," Biometrika, Biometrika Trust, vol. 95(1), pages 63-74.
    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. Yan Huang & Stefanus Jasin & Puneet Manchanda, 2019. "“Level Up”: Leveraging Skill and Engagement to Maximize Player Game-Play in Online Video Games," Information Systems Research, INFORMS, vol. 30(3), pages 927-947, September.
    4. Michael I. Jordan & Jason D. Lee & Yun Yang, 2019. "Communication-Efficient Distributed Statistical Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 668-681, April.
    5. Nicholas Ross, 2018. "Customer retention in freemium applications," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(4), pages 127-137, December.
    6. Badrinarayanan, Vishag A. & Sierra, Jeremy J. & Martin, Kinnon M., 2015. "A dual identification framework of online multiplayer video games: The case of massively multiplayer online role playing games (MMORPGs)," Journal of Business Research, Elsevier, vol. 68(5), pages 1045-1052.
    7. Trambak Banerjee & Gourab Mukherjee & Shantanu Dutta & Pulak Ghosh, 2020. "A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 538-554, April.
    8. O Papaspiliopoulos & G O Roberts & G Zanella, 2020. "Scalable inference for crossed random effects models," Biometrika, Biometrika Trust, vol. 107(1), pages 25-40.
    9. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
    10. Dimitris Rizopoulos & Geert Verbeke & Geert Molenberghs, 2010. "Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes," Biometrics, The International Biometric Society, vol. 66(1), pages 20-29, March.
    11. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2009. "Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 637-654, June.
    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. Vinod Dumblekar & Suresh Paul Antony & Upinder Dhar, 2024. "Openness to Experience and Player Satisfaction in a Simulation Game," Simulation & Gaming, , vol. 55(3), pages 479-501, June.
    2. Philipp Brüggemann & Nina Lehmann-Zschunke, 2023. "How to reduce termination on freemium platforms—literature review and empirical analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 707-721, December.

    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. repec:jss:jstsof:35:i09 is not listed on IDEAS
    2. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    3. Francesco Bartolucci & Alessio Farcomeni, 2015. "A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates," Biometrics, The International Biometric Society, vol. 71(1), pages 80-89, March.
    4. Philipp Brüggemann & Nina Lehmann-Zschunke, 2023. "How to reduce termination on freemium platforms—literature review and empirical analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 707-721, December.
    5. Tang, Nian-Sheng & Tang, An-Min & Pan, Dong-Dong, 2014. "Semiparametric Bayesian joint models of multivariate longitudinal and survival data," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 113-129.
    6. Esra Kürüm & Brian Kwan & Qi Qian & Sudipto Banerjee & Connie M. Rhee & Danh V. Nguyen & Damla Şentürk, 2025. "A Bayesian Joint Model of Longitudinal Kidney Disease Progression, Recurrent Cardiovascular Events, and Terminal Event in Patients with Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(2), pages 528-554, July.
    7. Jean-Baptiste Débordès & Gilles Caporossi & Denis Larocque, 2021. "Is my cross-promotion profitable? Evaluation of game-to-game cannibalization in free-to-play mobile games," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 173-184, September.
    8. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    9. Baumann, Elias & Kern, Jana & Lessmann, Stefan, 2019. "Usage Continuance in Software-as-a-Service," IRTG 1792 Discussion Papers 2019-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Vinh Truong, 2024. "Optimizing mobile in-app advertising effectiveness using app publishers-controlled factors," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 925-943, December.
    11. Yen-Chun Chou & Howard Hao-Chun Chuang, 2018. "A predictive investigation of first-time customer retention in online reservation services," Service Business, Springer;Pan-Pacific Business Association, vol. 12(4), pages 685-699, December.
    12. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    13. Xiang, Pengcheng & Zhou, Ling & Tang, Lu, 2024. "Transfer learning via random forests: A one-shot federated approach," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    14. Esra Kürüm & Danh V. Nguyen & Qi Qian & Sudipto Banerjee & Connie M. Rhee & Damla Şentürk, 2024. "Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(4), pages 827-852, October.
    15. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    16. Slãvescu Ecaterina Oana & Panait Iulian, 2012. "Improving Customer Churn Models as one of Customer Relationship Management Business Solutions for the Telecommunication Industry," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 1156-1160, May.
    17. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    18. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    19. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    20. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    21. 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.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssa:v:185:y:2022:i:1:p:102-133. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.