International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 14, No. 3, 8 Jun. 2024
Cover page and Table of Contents: PDF (size: 343KB)
Data Analysis and Success Prediction of Mobile Games before Publishing on Google Play Store
PDF (343KB), PP.13-21
Views: 0 Downloads: 0
Author(s)
Index Terms
Mobile games, Google Play Store, Game industries, Digital market, Downloads, User rating, Machine learning
Abstract
The popularity of mobile games has expanded among individuals of all ages, and the mobile gaming businesses are quickly expanding day by day. The Google Play Store, one of the most well-known platforms for the distribution of Android applications and games, sees a daily influx of thousands of new mobile games. One of the biggest problems in the gaming industry is predicting a mobile game's performance. Every day, thousands of new games are released. But just a couple of them are successful, while most of them fail. The study was done with the intention of analyzing any relationship between a mobile game's success and its distinctive features. Many of the mobile game developers work independently or work in the mobile game industries to make their games successful on the digital market. Before they are released, game makers can increase the quality of their games if they are confident in their products' commercial viability. For that reason, more than 17,000 games were taken into consideration. We show that the success of a mobile game is clearly influenced by its category, number of supported languages, developer profile, and release month. Furthermore, we show that specific aesthetic features of game symbols are more frequently linked to higher rating counts. We analyzed Google Play Store mobile games data and used a variety of machine learning algorithms for predicting the performance of mobile games based on the total number of downloads and the total user rating.
Cite This Paper
Muhammad Muhtasim, Md. Showrov Hossen, "Data Analysis and Success Prediction of Mobile Games before Publishing on Google Play Store ", International Journal of Education and Management Engineering (IJEME), Vol.14, No.3, pp. 13-21, 2024. DOI:10.5815/ijeme.2024.03.02
Reference
[1]S. Faralli, S. Rittinghaus, N. Samsami, D. Distante, and E. Rocha, “Emotional Intensity-based Success Prediction Model for Crowdfunded Campaigns,” Inf Process Manag, vol. 58, no. 1, Jan. 2021, doi: 10.1016/j.ipm.2020.102394.
[2]M. R. Dehkordi, H. Seifzadeh, G. Beydoun, and M. H. Nadimi- Shahraki, “Success prediction of android applications in a novel repository using neural networks,” Complex and Intelligent Systems, vol. 6, no. 3, pp. 573–590, Oct. 2020, doi: 10.1007/s40747-020-00154-3.
[3]R. M. Frey, R. Xu, and A. Ilic, “Mobile app adoption in different life stages: An empirical analysis,” Pervasive Mob Comput, vol. 40, pp. 512–527, Sep. 2017, doi: 10.1016/j.pmcj.2017.01.006.
[4]A. Kerim and B. Genç, “Mobile games success and failure: mining the hidden factors,” Neural Comput Appl, 2022, doi: 10.1007/s00521-022-07154-z.
[5]R. Xu, R. M. Frey, E. Fleisch, and A. Ilic, “Understanding the impact of personality traits on mobile app adoption - Insights from a large-scale field study,” Comput Human Behav, vol. 62, pp. 244–256, Sep. 2016, doi: 10.1016/j.chb.2016.04.011.
[6]M. Ben Hajhmida and O. Oueslati, “Predicting mobile application breakout using sentiment analysis of Facebook posts,” J Inf Sci, vol. 47, no. 4, pp. 502–516, Aug. 2021, doi: 10.1177/0165551520917099.
[7]M. Kajko-Mattsson, A. Hauzenberger, and R. Fredriksson, EM 3 : Software Retirement Process Model.
[8]T. Dinh Le and B.-T. Ho Nguyen, “ATTITUDES TOWARD MOBILE ADVERTISING: A STUDY OF MOBILE WEB DISPLAY AND MOBILE APP DISPLAY ADVERTISING,” 2014.
[9]K. L. Hsiao and C. C. Chen, “What drives in-app purchase intention for mobile games? An examination of perceived values and loyalty,” Electron Commer Res Appl, vol. 16, pp. 18–29, Mar. 2016, doi: 10.1016/j.elerap.2016.01.001.
[10]Y. Su, “Data-driven Analysis Platform for Indie Mobile Game Publishing,” 2022.
[11]Y. Su, P. Backlund, and H. Engström, “Comprehensive review and classification of game analytics,” in Service Oriented Computing and Applications, Springer Science and Business Media Deutschland GmbH, Jun. 2021, pp. 141–156. doi: 10.1007/s11761-020-00303-z.
[12]M. R. Dehkordi, H. Seifzadeh, G. Beydoun, and M. H. Nadimi- Shahraki, “Success prediction of android applications in a novel repository using neural networks,” Complex and Intelligent Systems, vol. 6, no. 3, pp. 573–590, Oct. 2020, doi: 10.1007/s40747-020-00154-3.
[13]B. Olabenjo, “Applying Naive Bayes Classification to Google Play Apps Categorization,” Aug. 2016, [Online]. Available: http://arxiv.org/abs/1608.08574
[14]O. Rutz, A. Aravindakshan, and O. Rubel, “Measuring and forecasting mobile game app engagement,” International Journal of Research in Marketing, vol. 36, no. 2, pp. 185–199, Jun. 2019, doi: 10.1016/j.ijresmar.2019.01.002.
[15]E. M. Ghazali, D. S. Mutum, and M. Y. Woon, “Multiple sequential mediation in an extended uses and gratifications model of augmented reality game Pokémon Go,” Internet Research, vol. 29, no. 3, pp. 504–528, Jun. 2019, doi: 10.1108/IntR-12-2017- 0505.
[16]S. C. Hung, H. Hsu, S. Y. Lien, and K. C. Chen, “Architecture harmonization between cloud radio access networks and fog networks,” IEEE Access, vol. 3, pp. 3019–3034, Dec. 2015, doi: 10.1109/ACCESS.2015.2509638.
[17]V. A. Burégio, E. S. De Almeida, D. Ludrédio, and S. L. Meira, “A reuse repository system: from specification to deployment,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, pp. 88–99. doi: 10.1007/978-3-540- 68073-4_8.