Matching Mobile Applications for Cross Promotion

Gene Moo Lee, Shu He, Joowon Lee, Andrew B. Whinston (2020) Matching Mobile Applications for Cross-Promotion. Information Systems Research 31(3):865-891.

63 Pages Posted: 6 Jan 2017 Last revised: 27 Oct 2021

See all articles by Gene Moo Lee

Gene Moo Lee

University of British Columbia (UBC) - Sauder School of Business

Shu He

University of Florida - Warrington College of Business Administration

Joowon Lee

Hansung University

Andrew B. Whinston

University of Texas at Austin - Department of Information, Risk and Operations Management

Date Written: August 26, 2020

Abstract

The mobile app 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 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 post-download usage) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using LDA 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. The paper has implications on the tradeoff 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

Suggested Citation

Lee, Gene Moo and He, Shu and Lee, Joowon and Whinston, Andrew B., Matching Mobile Applications for Cross Promotion (August 26, 2020). Gene Moo Lee, Shu He, Joowon Lee, Andrew B. Whinston (2020) Matching Mobile Applications for Cross-Promotion. Information Systems Research 31(3):865-891., Available at SSRN: https://ssrn.com/abstract=2893338 or http://dx.doi.org/10.2139/ssrn.2893338

Gene Moo Lee (Contact Author)

University of British Columbia (UBC) - Sauder School of Business ( email )

2053 Main Mall
Vancouver, BC V6T 1Z2
Canada

Shu He

University of Florida - Warrington College of Business Administration ( email )

PO Box 117165, 201 Stuzin Hall
Gainesville, FL
United States

Joowon Lee

Hansung University ( email )

Seoul, 136-792

Andrew B. Whinston

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States
512-471-8879 (Phone)

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