Marketing Seminar（2015-11）

Title:Buying Downloads: Paying to Take off by Mobile Apps

Speaker:Xing Li, Stanford University

Time:10.Dec. 13:30-15:00pm

Location:Room217, Guanghua Building 2

Abstract:

Success breeds success in many mass-market industries, as well known products gain further consumer acceptance because of their visibility. However, new products must struggle to gain consumer's scarce attention and initiate that virtuous cycle. The newest mass-market industry, mobile apps, has these features. Success among apps is highly concentrated, in part because the "top apps lists" recommend apps based on past success as measured by downloads. Consequently, in order to introduce themselves to users, new app developers attempt to gain a position on the "top app lists" by "buying downloads", i.e., paying a user to download the app onto her device. We leverage a private dataset of one platform for buying downloads and identify the return from this investment. $100 invested will improve the ranking by 2.2%. To capture the rationale of investment in buying downloads as to enter the virtuous cycle, we further build a model that accommodates (1) the impact of buying downloads on top list rank, (2) optimal investment in buying downloads, (3) an empirical distinction between organic virtuous cycle diffusion and spurious diffusion caused by buying downloads, and (4) a rich set of app-specific heterogeneities. We quantify the app-specific structural coefficients, including the value per organic download, top-list effect, word-of-mouth effect and market size, by estimating the model using time-series ranking positions of 2,755 free apps. We find the median value of one organic download is 65% of the cost of buying one download, implying a huge marginal cost of buying downloads. App developers lose money during the initial days after release and before taking off. These coefficients are correlated with ex-post quality, measured by users' rating but uncorrelated with ex-ante categories of apps, suggesting that developers face a great deal of ex-ante uncertainty about the outcome for their apps when they enter the market. A model based measure of values for top-list appearance is proposed, and we find that value is associated with app quality and app categories.

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