Data-Driven Promotion Planning for Paid Mobile Applications

In this paper, we propose a two-step data analytic approach to the promotion planning for paid mobile applications (apps). In the first step, we use historical sales data to empirically estimate the app demand model and quantify the effect of price promotions on download volume. The estimation results reveal two interesting characteristics of the relationship between price promotion and download volume of mobile apps: (1) the magnitude of the direct immediate promotion effect is changing within a multiday promotion; and (2) due to the visibility effect (i.e., apps ranked high on the download chart are more visible to consumers), a price promotion also has an indirect effect on download volume by affecting app rank, and this effect can persist after the promotion ends. Based on the empirically estimated demand model, we formulate the app promotion optimization problem into a longest path problem, which takes into account the direct and indirect effects of promotions. To deal with the tractability of the longest path problem, we propose a moving planning window heuristic, which sequentially solves a series of subproblems with a shorter time horizon, to construct a promotion policy. Our heuristic promotion policy consists of shorter and more frequent promotions. We show that the proposed policy can increase the app lifetime revenue by around 10%.