To match or not to match: Economics of cookie matching in online advertising

Arpita Ghosh, Mohammad Mahdian, R. Preston McAfee, Sergei Vassilvitskii

Modern online advertising increasingly relies on the availability of user tracking technology called cookiematching to increase efficiency in ad allocations. Web publishers today use this technology to share information about the websites a user has visited, making it possible to target advertisements to users based on their prior history. This begs the question: do publishers (who are competitors for advertising money) always have the incentive to share online information? Intuitive arguments as well as anecdotal evidence suggest that sometimes a premium publisher might suffer from information sharing through an effect called information leakage: by sharing user information with the advertiser, the advertiser will be able to target the same user elsewhere on cheaper publishers, leading to a dilution of the value of the supply on the premium publishers. The goal of this paper is to explore this aspect of online information sharing. We show that when advertisers are homogeneous (i.e., they value the users similarly, up to a constant multiple), in equilibrium, the publishers always agree about the benefits of cookie-matching (i.e., either they all benefit, or they all suffer from it). We also analyze a simple model that exhibits how information leakage can help one publisher and harm the other when the advertisers are not homogeneous.


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