Targeting and Privacy in Mobile Advertising

89 Pages Posted: 3 May 2018 Last revised: 2 Jun 2020

See all articles by Omid Rafieian

Omid Rafieian

University of Washington

Hema Yoganarasimhan

University of Washington

Date Written: June 2nd, 2020

Abstract

Mobile in-app advertising is now the dominant form of digital advertising. While these ads have excellent user-tracking properties, they have raised concerns among privacy advocates. This has resulted in an ongoing debate on the value of different types of targeting information, the incentives of ad-networks to engage in behavioral targeting, and the role of regulation. To answer these questions, we propose a unified modeling framework that consists of two components -- a machine learning framework for targeting and an analytical auction model for examining market outcomes under counterfactual targeting regimes. We apply our framework to large-scale data from the leading in-app ad-network of an Asian country. We find that an efficient targeting policy based on our machine learning framework improves the average click-through rate by 66.80\% over the current system. These gains mainly stem from behavioral information compared to contextual information. Theoretical and empirical counterfactuals show that while total surplus grows with more granular targeting, ad-network's revenues are non-monotonic, i.e., the most efficient targeting does not maximize ad-network revenues. Rather, it is maximized when the ad-network does not allow advertisers to engage in behavioral targeting. Our results suggest that ad-networks may have economic incentives to preserve users' privacy without external regulation.

Keywords: mobile, advertising, targeting, machine learning, behavioral targeting, privacy, auctions, regulation, public policy

JEL Classification: D44, D82, M37, L51

Suggested Citation

Rafieian, Omid and Yoganarasimhan, Hema, Targeting and Privacy in Mobile Advertising (June 2nd, 2020). Available at SSRN: https://ssrn.com/abstract=3163806 or http://dx.doi.org/10.2139/ssrn.3163806

Omid Rafieian

University of Washington ( email )

Seattle, WA 98195
United States

Hema Yoganarasimhan (Contact Author)

University of Washington ( email )

481 Paccar Hall
Seattle, WA 98195
United States

HOME PAGE: http://faculty.washington.edu/hemay/

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