![]() ![]() Therefore, researchers often divide the market into multiple sectors and analyze each sector separately. ![]() As the number of published games increase exponentially, game market is becoming too complicated to understand as a single sector. The global game market continues to grow, and it is expected to grow 9.6 percent per year between 20 with a stable revenue stream. 490-20140016).Ĭompeting interests: The authors have declared that no competing interests exist. NRF-2016R1A2B1014734) and research grant for foreign professors through Seoul National University in 2014 (no. The third game’s data can be found in as well.įunding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All play log data files are available from the zenodo database ( ). Received: DecemAccepted: JPublished: July 5, 2017Ĭopyright: © 2017 Kim et al. PLoS ONE 12(7):Įditor: Wei-Xing Zhou, East China University of Science and Technology, CHINA While the analysis results provide useful insights, the overall results indicate that a small number of well-chosen features used as performance metrics might be sufficient for making important action decisions and that OP and CP should be properly chosen depending on the analysis goal.Ĭitation: Kim S, Choi D, Lee E, Rhee W (2017) Churn prediction of mobile and online casual games using play log data. ![]() ![]() Play log data of three different casual games are considered by analyzing a total of 193,443 unique player records and 10,874,958 play log records. We cover essential topics such as pre-processing of raw data, feature engineering including feature analysis, churn prediction modeling using traditional machine learning algorithms (logistic regression, gradient boosting, and random forests) and two deep learning algorithms (CNN and LSTM), and sensitivity analysis for OP and CP. Using the definition, we develop a standard churn analysis process for casual games. Therefore, we focus on the new players and formally define churn using observation period ( OP) and churn prediction period ( CP). Most of the existing methods cannot be applied to casual games because casual game players tend to churn very quickly and they do not pay periodic subscription fees. While churn prediction and analysis can provide important insights and action cues on retention, its application using play log data has been primitive or very limited in the casual game area. In this study, we focus on churn prediction of mobile and online casual games. With the resulting fierce competition, now both acquisition and retention of users are the prime concerns in the field. On the other hand, development of casual games has become easier than ever as a result of the advancement of development tools. Interaction with such devices has evolved into frequent and short-duration usage, and this phenomenon has resulted in a pervasive popularity of casual games in the game sector. Internet-connected devices, especially mobile devices such as smartphones, have become widely accessible in the past decade. ![]()
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