业务
补贴
广告
互联网隐私
营销
计算机科学
经济
市场经济
作者
Wangsheng Zhu,Shaojie Tang,Vijay Mookerjee
标识
DOI:10.1287/isre.2023.0126
摘要
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 help 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. In this study, we propose three subsidy frameworks to increase ad exchange revenue by inducing more advertisers to acquire data: all subsidized, winner subsidized, and loser subsidized. Using a stylized model, we analyze the impact of subsidy provisions on the platform’s net revenue. Our results show that winner subsidized can be better or worse than all subsidized depending on the cost of data acquisition, its beneficial impact on ad selection, and the distribution of impression values.
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