Abstract
Mobile applications (or apps) are present on every portable device and have become the center of tremendous attention from developers and software vendors. Some apps meet significant success with high profitability, but most of them tend to remain anonymous, with weak returns on investment. The risk incurred when launching a new app is therefore significant. In this article, we introduce the concept of Publication Strategy, resulting from the numerous decisions made by an app designer on all the variables which are publicly visible on the stores (screenshots, description, title, etc.). This paper studies the extent to which the success of an app may be predicted using such Publication Strategy. To do this, we use metadata about more than 40,000 apps from both the Google Play Store and the Apple App Store and adopt a machine learning research strategy by training and testing a number of classification models. We observe that in about 50% of the cases, it is possible to predict the rating of an app based solely on its Publication Strategy. The results are very similar between the 2 stores. These results bring us to the definition of a number of research avenues to further explore the notion of App Publication Strategy which can be used to support apps designers in their decisions.
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Lega, M., Burnay, C., Faulkner, S. (2022). Predicting the Rating of an App Beyond Its Functionalities: Introducing the App Publication Strategy. In: Cabral Seixas Costa, A.P., Papathanasiou, J., Jayawickrama, U., Kamissoko, D. (eds) Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs. ICDSST 2022. Lecture Notes in Business Information Processing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-031-06530-9_2
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