Chapter

Algorithmic Game Theory

Volume 8768 of the series Lecture Notes in Computer Science pp 194-205

Value of Targeting

  • Kshipra BhawalkarAffiliated withGoogle, Inc.
  • , Patrick HummelAffiliated withGoogle, Inc.
  • , Sergei VassilvitskiiAffiliated withGoogle, Inc.

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Abstract

We undertake a formal study of the value of targeting data to an advertiser. As expected, this value is increasing in the utility difference between realizations of the targeting data and the accuracy of the data, and depends on the distribution of competing bids. However, this value may vary non-monotonically with an advertiser’s budget. Similarly, modeling the values as either private or correlated, or allowing other advertisers to also make use of the data, leads to unpredictable changes in the value of data. We address questions related to multiple data sources, show that utility of additional data may be non-monotonic, and provide tradeoffs between the quality and the price of data sources. In a game-theoretic setting, we show that advertisers may be worse off than if the data had not been available at all. We also ask whether a publisher can infer the value an advertiser would place on targeting data from the advertiser’s bidding behavior and illustrate that this is impossible.