PRADO: Predicting App Adoption by Learning the Correlation between Developer-Controllable Properties and User Behaviors

To survive and stand out from the fierce market competition nowadays, it is critical for app developers to know (desirably ahead of time) whether, how well, and why their apps would be adopted by users. Ideally, the adoption of an app could be predicted by factors that can be controlled by app developers in the development process, and factors that app developers are able to take actions on and improve according to the predictions. To this end, this paper proposes PRADO, an approach to measuring various aspects of user adoption, including app download and installation, uninstallation, and user ratings. PRADO employs advanced machine learning algorithms to predict user adoption based on how these metrics correlate to a comprehensive taxonomy of 108 developer-controllable features of the app. To evaluate PRADO, we use 9,824 free apps along with their behavioral data from 12.57 million Android users, demonstrating that user adoption of a new app can be accurately predicted. We also derive insights on which factors are statistically significant to user adoption, and suggest what kinds of actions can be possibly performed by developers in practice.