Should Ad Exchanges Subsidize Advertisers to Acquire Targeting Data?

Wangsheng Zhu (), Shaojie Tang () and Vijay Mookerjee ()
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Wangsheng Zhu: Department of Information Systems, Business Statistics, and Operations Management, School of Business and Management, Hong Kong University of Science and Technology, Hong Kong
Shaojie Tang: Center for AI Business Innovation, Department of Management Science and Systems, University at Buffalo, Buffalo, New York 14260
Vijay Mookerjee: Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, Texas 75080

Information Systems Research, 2025, vol. 36, issue 3, 1502-1521

Abstract: Large volumes of online impressions are sold daily via real-time auctions to deliver targeted advertisements to consumers. Advertisers use data to learn about user preferences and select the most appropriate ad for each user, which also helps them optimize their bids in an ad auction. Although ad exchanges may provide some user data to advertisers, they are usually limited, and advertisers often acquire data from various sources to improve targeting performance. The acquisition of such data can significantly influence the revenue of the ad exchange, which motivates ad exchanges to take actions that reduce advertisers’ data acquisition costs and encourage them to buy data. Previous studies have examined the impact of ad exchanges revealing their data to advertisers, but little attention has been paid to the impact of ad exchanges subsidizing advertisers to acquire data from third parties. To address this gap, we propose three subsidy frameworks to increase ad exchange revenue by inducing more advertisers to acquire data: all subsidized (AS), winner subsidized (WS), and loser subsidized. Using a stylized model, we analyze the impact of subsidy provisions on the platform’s net revenue. Our results show that WS can be better or worse than AS depending on the cost of data acquisition, its beneficial impact on ad selection, and the distribution of impression values.

Keywords: targeted advertising; ad auctions; targeting data acquisition; subsidy frameworks (search for similar items in EconPapers)
Date: 2025
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