Targeting and Privacy in Mobile Advertising Omid Rafieian∗ Hema Yoganarasimhan∗ University of Washington University of Washington June 2, 2020 Forthcoming, Marketing Science ∗We are grateful to an anonymous firm for providing the data and to the UW-Foster High Performance Computing Lab for providing us with computing resources. We thank Daria Dzyabura, Avi Goldfarb, Clarence Lee, Simha Mummalaneni, Puneet Manchanda, Sridhar Narayanan, Amin Sayedi, K. Sudhir, and Daniel Zantedeschi for detailed comments that have improved the paper. We also thank the participants of the 2016 Invitational Choice Symposium, 2016 FTC and Marketing Science Conference, 2016 Big Data and Marketing Analytics Conference at the University of Chicago, 2017 Ph.D. Students Workshop at the University of Washington, 2017 Adobe Data Science Symposium, 2017 SICS, the 2018 MSI-Wharton Conference on New Perspectives in Analytics, 2018 UW-UBC conference, the 2019 UT Dallas FORMS conference, and MIT, CMU, HBS, and Wharton marketing seminars for their feedback. Please address all correspondence to: rafi
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[email protected]. 1 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.