What Makes a Mobile Game Successful: A Data-driven Analysis of the Ios Game App Market

Document Type


Degree Name

Master of Science (MS)


Computer Science

Date of Award

Summer 2016


The global mobile application market is a billion dollar market and one of the fastest growing global markets in terms of revenue year by year. The iOS app store and Google Play are considered to be the top players of the mobile application market where users can find millions of mobile applications (also known as apps) under different categories to serve their purpose or to keep them occupied and entertained. As such, the mobile app markets, albeit among those that are most lucrative, are extremely competitive, where developers and design studios strive to create apps that can attract as many customers as possible (success) and avoid being swamped in the wealth of other apps (failure). Unfortunately, for the iOS App Store, one of the most prominent app markets nowadays, such information is not available to developers. In this project, our goal was to shed some light on the secrets behind a mobile game app’s success by analyzing the metadata of more than 130,000 iOS game apps to identify the factors that influence the following features: (1) the average user rating and (2) the user rating count. We suspected that an app’s success can be roughly approximated by the significance (i.e., rating count) of its good ratings. If an app has a lot of good ratings, it can be deemed successful. By estimating the performance of an app based on these selected indicators, we hoped to help developers predict their apps’ prospected success in advance, so that they can build a game that has a higher chance at succeeding in the market. We first constructed predictive models, which include multiple linear regression, clustering, logistic regression, multi-layer perceptron, and Bayesian network models, that take descriptive features of a game as input (e.g., game genre, number of supported devices, developers’ name, price, its description, etc.) and return estimates of its average user rating. Finally, we compared the accuracy of these models and examined their advantages and disadvantages to users when interpreting the results. We found out that Bayesian Network models outperform all other models with an accuracy of 74% when predicting the average user rating of a game app.


Truong-Huy Nguyen

Subject Categories

Computer Sciences | Data Science | Physical Sciences and Mathematics