Value of Targeting

TL;DR

This paper provides a formal analysis of the value of targeting data for advertisers, revealing complex, non-monotonic relationships with budget, data quality, and competition, and highlights strategic and inferential limitations.

Contribution

It introduces a formal framework for evaluating targeting data value, explores non-monotonic effects, and examines strategic implications in competitive and game-theoretic settings.

Findings

Value of data increases with utility difference and accuracy

Data value can vary non-monotonically with budget

Advertisers may be worse off with data in competitive settings

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.