Targeting and Privacy in Mobile Advertising
Omid Rafieian and Hema Yoganarasimhan , 2018, 18-131-10
Mobile in-app advertising is growing in popularity. Key to this growth is excellent user-tracking properties through mobile device IDs which enables advertisers to target consumers’ marketing appeals. While the advertising industry has lauded the trackability of in-app ads, consumers and privacy advocates have derided them, citing privacy concerns.
As such, consumers, businesses, and regulators are trying to find the right balance between consumer protection and business interests. Nevertheless, we do not have a good understanding of the key issues at the core of targeting and privacy. Omid Rafieian and Hema Yoganarasimhan seek to address this gap by providing answers to the following key questions:
- Value of targeting information: How do different pieces of information contribute towards improving targeting ability? What is the relative value of contextual vs. behavioral information?
- Impact of stronger privacy regulations: If the ad-network were to lose the ability to do user tracking through device IDs (e.g., through blanket adoption of LAT [limit ad tracking] or government regulation), to what extent would its targeting ability suffer?
- Incentives to preserve user privacy: Do ad-networks have an incentive to share targeting data with advertisers and thereby enable targeted bidding? What is the optimal level of data-sharing from the perspective of different players?
They propose a modeling framework that consists of two components - a machine learning framework for targeting and click-through rate predictions and a stylized analytical framework for conducting data-sharing counterfactuals and examining economic incentives in this marketplace. They apply their framework to data from the leading in-app ad-network of an Asian country.
They find that machine learning model improves targeting ability by 17.95% over the baseline. These gains mainly stem from behavioral information and the value of contextual information is relatively small.
Next, they examine how stronger privacy regulations that ban user tracking through device IDs would affect the ad-network’s ability to target. In this case, the ad-network would have to resort to using IP addresses as the user-identifier. They find that this substantially shrinks the value of behavioral targeting, suggesting that behavioral targeting without a reliable user-identifier is not very effective. They show that it is necessary for a reliable identifier not to assign multiple users to one ID as opposed to assigning one user to multiple IDs.
Finally, they address the question of an ad-network’s incentives to share data with advertisers by considering four data-sharing scenarios: full, behavioral, contextual, and no data-sharing. They show that the total value created in the market grows with more granular data-sharing between the ad-network and advertisers. However, this increase in created value does not necessarily translate into higher revenues for the ad-network. Their empirical analysis reveals that ad-network revenues are maximized when it restricts data-sharing to the contextual level, because sharing behavioral information thins out the market, thereby reducing ad-network revenues. Thus, the ad-network has an incentive to preserve users’ privacy without external regulation.
Omid Rafieian is a Ph.D. student in Marketing and Hema Yoganarasimhan is Associate Professor of Marketing, both at the Foster School of Business, University of Washington.
The authors are grateful to an anonymous firm for providing the data and to the UW-Foster High Performance Computing Lab for providing them with computing resources. They thank Avi Goldfarb, Daria Dzyabura, Clarence Lee, Simha Mummalaneni, Sridhar Narayanan, Amin Sayedi, and K. Sudhir for detailed comments that have improved the paper. They 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, SICS 2017, and the 2018 MSI Wharton Conference on New Perspectives in Analytics, for their feedback.
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