Modeling the value of information granularity in targeted advertising

Swati Rallapalli, Qiang Ma, Han Hee Song, Mario Baldi, S. Muthukrishnan, Lili Qiu
<span title="2014-04-17">2014</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pkal2zplnrarzkkzugv4u5ithy" style="color: black;">Performance Evaluation Review</a> </i> &nbsp;
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
more &raquo; ... ry 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2627534.2627547">doi:10.1145/2627534.2627547</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qgo4ri63kbefdijb63djwmqqte">fatcat:qgo4ri63kbefdijb63djwmqqte</a> </span>
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