Designing for Visibility: The Case of Mobile Apps

Abstract

With a large number of mobile apps in both iOS and Android platforms, app developers face a significant challenge in generating market demand. Apps can incorporate social features to share information on social media platforms and gain visibility. Classifying features of an app as intrinsic or social, we focus on the impact of these features in the head, body, and tail of the demand distribution. We posit that owing to the lack of visibility, apps in the tail may benefit more from social features than those in the head or body sections. Using a panel of version release notes from the iOS platform, we develop a new hierarchical deep learning model to extract intrinsic and social features. Our results suggest that social features increase the relative demand only for tail apps. For apps in the head of the distribution, social and intrinsic features together help increase the relative demand. However, the combined effect of social and intrinsic is negative in the tail for low-quality apps. The results underscore the heterogeneity in the effect of social features on app demand, an important consideration in the design phase. We demonstrate that there is no one-size-fits-all approach that works in all parts of the demand distribution. Our study provides managerial guidance to app developers in making their products visible through design choices and presents a novel deep learning approach for product feature identification.