Advertisement

Predicting Success of a Mobile Game: A Proposed Data Analytics-Based Prediction Model

  • Khaled Mohammad Alomari
  • Cornelius Ncube
  • Khaled Shaalan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

Even though billions of dollars in revenue have been generated from mobile game apps, there is still a knowledge gap with regard to mobile game user behavior and methodologies for predicting the likely success of mobile game apps during the development phase. This paper analyses game features and (Acquisition, Retention and Monetization) ARM strategies as primary drivers of mobile game application success. This study addresses these challenges through data driven research of the mobile gaming application market, mobile gaming application features, user acquisition and retention trends, and monetization strategies using the CRISP-DM model for data mining in order to prove a successful method for predictions of mobile game application success. A prediction model is developed then applied to 50 games. The prediction of successful mobile game application from a sample of 50 games is achieved by running a batch prediction for the game features dataset and a separate batch prediction for the user behavior dataset. The model produced a total of 9 titles from the sample with the highest probability of success. The significant outcomes for the comparisons included the predominance of the Social Networking Features, Offers, and (In App Purchase) IAP 90% to 100% of the sample. A model of mobile game app success prediction based upon the game features values that are created is proposed.

Keywords

Mobile games Prediction model Data analytics 

References

  1. 1.
    Mäyrä, F.: Mobile games. In: Mansell, R., Ang, P.H. (eds.) The International Encyclopedia of Digital Communication and Society. Wiley, Chichester (2015)Google Scholar
  2. 2.
    Lowood, H.: Videogames in computer space: the complex history of Pong. IEEE Ann. Hist. Comput. 31, 5–19 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Wright, C.: A Brief History of Mobile Games: In the beginning, there was Snake, Pocket Gamer, UK (2016)Google Scholar
  4. 4.
    MADAB: The Art of Automation. Mobile App Developers’ Advisory Board (2016)Google Scholar
  5. 5.
    Alomari, K.M., Soomro, T.R., Shaalan, K.: Mobile gaming trends and revenue models. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds.) IEA/AIE 2016. LNCS (LNAI), vol. 9799, pp. 671–683. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42007-3_58CrossRefGoogle Scholar
  6. 6.
    Sifa, R., Hadiji, F., Runge, J., Drachen, A., Kersting, K., Bauckhage, C.: Predicting purchase decisions in mobile free-to-play games. In: The Eleventh AAAI Conference on Artificia Intelligence and Interactive Digital Entertainment (AIIDE-15) (2015)Google Scholar
  7. 7.
    Unhelkar, B., Murugesan, S.: The enterprise mobile applications development framework. IT Prof. 12, 33–39 (2010)CrossRefGoogle Scholar
  8. 8.
    Law, F.L., Kasirun, Z.M., Gan, C.K.: Gamification towards sustainable mobile application. In: Harun, M.F. (ed.) 5th Malaysian Conference in Software Engineering (MySEC), 13–14 December 2011, Johor Bahru, Malaysia, pp. 349–353. IEEE, Piscataway, NJ (2011)Google Scholar
  9. 9.
    Gualtieri, M.: Forrester’s mobile app design context. location, locomotion, immediacy, intimacy, and device. Forrester. http://blogs.forrester.com/mike_gualtieri/11-04-13-forresters_mobile_app_design_context_location_locomotion_immediacy_intimacy_and_device
  10. 10.
    Constantino, A.: Developer Economics. The State of the Developer Nation, Q1 2015 (2015)Google Scholar
  11. 11.
  12. 12.
    Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step-by-step data mining guide (2000)Google Scholar
  13. 13.
    Mobileaction: Top charts for iPhone. ASO Intelligence. https://insights.mobileaction.co/appreport/?trackId=1095254858&store=ios
  14. 14.
  15. 15.
    BigML: Classification and Regression with the BigML Dashboard. https://static.bigml.com/pdf/BigML_Classification_and_Regression.pdf
  16. 16.
    Rogers, A.: How did electronic arts perform in Fiscal 2Q17? Market Realist. http://marketrealist.com/2016/11/mobile-gaming-key-driver-electronic-arts/
  17. 17.
    Barus, A.C., Tobing, R.D.H., Pratiwi, D.N., Damanik, S.A., Pasaribu, J. (eds.): Mobile game testing. Case study of a puzzle game genre. In: 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT) (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceAbu Dhabi UniversityAbu DhabiUAE
  2. 2.Faculty of Engineering and ITThe British University in DubaiDubaiUAE

Personalised recommendations