| Title | Mobile Games Success and Failure: Mining the Hidden Factors |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Kerim, A., Genc, B. |
| Conference Name | 2020 7th International Conference on Soft Computing Machine Intelligence (ISCMI) |
| Keywords | data mining, game design, game features, Games, Industries, land mobile radio, mobile games, Predictive models, pubcrawl, Resiliency, Scalability, Support vector machines, Uniform resource locators, work factor metrics |
| Abstract | Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released each day. However, a few of them succeed while the majority fail. Towards the goal of investigating the potential correlation between the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousands games were considered for that reason. We show that specific game attributes, such as number of IAPs (In-App Purchases), belonging to the puzzle genre, supporting different languages and being produced by a mature developer highly and positively affect the success of the game in the future. Moreover, we show that releasing the game in July and not including any IAPs seems to be highly associated with the game's failure. Our second main contribution, is the proposal of a novel success score metric that reflects multiple objectives, in contrast to evaluating only revenue, average rating or rating count. We also employ different machine learning models, namely, SVM (Support Vector Machine), RF (Random Forest) and Deep Learning (DL) to predict this success score metric of a mobile game given its attributes. The trained models were able to predict this score, as well as the rating average and rating count of a mobile game with more than 70% accuracy. This prediction can help developers before releasing their game to the market to avoid any potential disappointments. |
| DOI | 10.1109/ISCMI51676.2020.9311587 |
| Citation Key | kerim_mobile_2020 |
Mobile Games Success and Failure: Mining the Hidden Factors