While apps on the whole increasingly drive and shape present-day Web usage, they individually thrive and fall based on rich patterns of user adoption and long-term engagement. In studying these patterns at the population level, researchers remain curtailed by practical difficulties in accessing consistently detailed data covering large, representative cross-sections of apps. We address this challenge by proposing an empirical framework for analyzing an app’s patterns of adoption and engagement. Importantly, our approach requires obtaining only an app’s daily, weekly, and monthly usership time-series data, which are popular metrics tracked and made available for many apps. Modeling how the nuanced co-dynamics of an app’s daily, weekly, and monthly usership measures (DAU, WAU, and MAU) reliably reveal user adoption and repeat engagement, we extend and demonstrably improve on the predictive performance of prior install- or DAU-based methods. To further study the mechanisms by which successful apps cultivate their userbases, we position apps along the dual axes of viral adoption and retentive engagement as mechanisms to potentially explain success. Applying our approach to data on Facebook apps, we show that these apps over time became less viral and more engaging. Interestingly, despite their diminished virality, their improved user retention subtly and counter-intuitively boosted these apps’ rates of new user adoptions through user-activity-based word of mouth, because an engaged user contributes to word of mouth over a longer period of time. In a final case application, we introduce evidence that developers of apps that learn to successfully retain their users carry this valuable experience over into their new apps.
Recommended citation: Mendelson, H., and K. Moon. (2018). "Modeling Success and Engagement for the App Economy" Proceedings of ACM The Web Conference (WWW’18), Research track: Social Network Analysis and Graph Algorithms for the Web, 569-578.