Preference, Profitability, and Ranking in Mobile App Monetization
Posted: 18 Jun 2019
Date Written: May 29, 2019
For more effective monetization of cost per action (CPA) in mobile app distribution, the platform should balance its app revenue margins with consumer utility from click and conversion (i.e., install) decisions. In this paper, we develop an empirical framework to estimate consumer click and conversion utility using mobile clickstream data from a large mobile service operator. Our data set uniquely includes actual CPA margins for every app. Consumer click and conversion utilities are jointly estimated as a function of a comprehensive set of quantitative (screen rank, quality, and popularity) and qualitative (textual data of app titles, descriptions, and reviews) covariates. We conduct policy experiments on our structural model estimates to examine the effectiveness of alternative screen ranking strategies, finding that a personalized ranking scheme accounting for both utility and margins outperforms the methods based on margins alone (by 24%) or utility alone (by 11%). Overall, our analyses shed light on consumer click and conversion behavior and on how app monetization can be optimized through a personalized ranking that balances consumer revealed preferences with app CPA margins.
Keywords: Apps, Ranking, Platform Profitability, Hierarchical Bayes, Text Analytics
JEL Classification: M15
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