Matching Mobile Applications for Cross-Promotion

Gene Moo Lee (), Shu He (), Joowon Lee () and Andrew B. Whinston ()
Additional contact information
Gene Moo Lee: Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
Shu He: Department of Operations and Information Management, University of Connecticut, Storrs, Connecticut 06269
Joowon Lee: College of Social Science, Hansung University, Seoul 136-172, Korea
Andrew B. Whinston: Department of Information, Risks, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712

Information Systems Research, 2020, vol. 31, issue 3, 865-891

Abstract: The mobile applications (apps) market is one of the most successful software markets. As the platform grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. The challenge is how app developers can target the right users with their apps and how consumers can find the apps that fit their needs. Cross-promotion, advertising a mobile app (target app) in another app (source app), is introduced as a new app-promotion framework to alleviate the issue of search costs. In this paper, we model source app user behaviors (downloads and postdownload usages) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using latent Dirichlet allocation topic modeling on apps’ production descriptions and then analyze how the similarity between the source and target apps influences users’ app download and usage decisions. To estimate the model, we use a unique data set from a large-scale random matching experiment conducted by a major mobile advertising company in Korea. The empirical results show that consumers prefer more diversified apps when they are making download decisions compared with their usage decisions, which is supported by the psychology literature on people’s variety-seeking behavior. Lastly, we propose an app-matching system based on machine-learning models (on app download and usage prediction) and generalized deferred acceptance algorithms. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of cross-promotion campaigns and that, at the expense of privacy, individual user data can further improve the matching performance. This paper has implications on the trade-off between utility and privacy in the growing mobile economy.

Keywords: mobile applications; cross-promotion; matching; search cost; two-sided platform; topic modeling; machine learning; deferred acceptance; algorithm; mobile analytics (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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