Modeling the value of information granularity in targeted advertising
Authors: 
Swati Rallapalli, Qiang Ma, Han Hee Song, Mario Baldi, S. Muthukrishnan, Lili QiuAuthors Info & Claims
Swati Rallapalli, Qiang Ma, Han Hee Song, Mario Baldi, S. Muthukrishnan, Lili QiuAuthors Info & ClaimsPages 42 - 45
Abstract
Behavioral Targeting (BT) in the past few years has seen a great upsurge in commercial as well as research interest. To make advertising campaigns more effective, advertisers look to target more relevant users. Ad-networks and other data collectors, such as, Cellular Service Providers (CSPs), hold a treasure trove of user information that is extremely valuable to advertisers. Moreover, these players may have complimentary sets of data. Combining and using data from different collectors can be very useful for advertising. However, in the trade of data among the various players, it is currently unclear how a price can be attached to a certain piece of information. This work contributes (i) a MOdel of the Value of INformation Granularity (MoVInG) that captures the impact of additional information on the revenue from targeted ads in case of uniform bidding and (ii) an expression that is applicable in more general scenarios. We apply MoVInG to a user data-set from a large CSP to evaluate the financial benefit of precise user data.
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Published In
March 2014
104 pages
Copyright © 2014 Authors.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 April 2014
Published in SIGMETRICS Volume 41, Issue 4
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