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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.<\/jats:p>","DOI":"10.1145\/2627534.2627547","type":"journal-article","created":{"date-parts":[[2014,5,27]],"date-time":"2014-05-27T12:56:59Z","timestamp":1401195419000},"page":"42-45","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling the value of information granularity in targeted advertising"],"prefix":"10.1145","volume":"41","author":[{"given":"Swati","family":"Rallapalli","sequence":"first","affiliation":[{"name":"The University of Texas at Austin"}]},{"given":"Qiang","family":"Ma","sequence":"additional","affiliation":[{"name":"Rutgers University"}]},{"given":"Han Hee","family":"Song","sequence":"additional","affiliation":[{"name":"Narus Inc."}]},{"given":"Mario","family":"Baldi","sequence":"additional","affiliation":[{"name":"Narus Inc."}]},{"given":"S.","family":"Muthukrishnan","sequence":"additional","affiliation":[{"name":"Rutgers University"}]},{"given":"Lili","family":"Qiu","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin"}]}],"member":"320","published-online":{"date-parts":[[2014,4,17]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"A method for large-scale l1-regularized least squares. https:\/\/linproxy.fan.workers.dev:443\/http\/www.stanford.edu\/\u00bfboyd\/l1_ls\/.  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Behavioral targeting conversion rate. https:\/\/linproxy.fan.workers.dev:443\/http\/www.networkadvertising.org\/pdfs\/Beales_NAI_Study.pdf."},{"key":"e_1_2_1_6_1","unstructured":"Behavioral targeting spending. https:\/\/linproxy.fan.workers.dev:443\/http\/www.emarketer.com\/Article.aspx?R=1006384.  Behavioral targeting spending. https:\/\/linproxy.fan.workers.dev:443\/http\/www.emarketer.com\/Article.aspx?R=1006384."},{"key":"e_1_2_1_7_1","unstructured":"Behavioral targeting usage. https:\/\/linproxy.fan.workers.dev:443\/http\/www.internetretailer.com\/2009\/05\/04\/24-of-online-marketers-use-behavioral-\\\\targeting-up-50-from-a.  Behavioral targeting usage. https:\/\/linproxy.fan.workers.dev:443\/http\/www.internetretailer.com\/2009\/05\/04\/24-of-online-marketers-use-behavioral-\\\\targeting-up-50-from-a."},{"key":"e_1_2_1_8_1","unstructured":"Data trade. https:\/\/linproxy.fan.workers.dev:443\/http\/www.networkadvertising.org\/pdfs\/Beales_NAI_Study.pdf.  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Expected second maximum. https:\/\/linproxy.fan.workers.dev:443\/http\/www.econ.ucsb.edu\/\u00bftedb\/Courses\/Ec100C\/Readings\/MatthewsAuctionPrimer.pdf."},{"key":"e_1_2_1_12_1","volume-title":"Bidding for representative allocations for display advertising. CoRR, abs\/0910.0880","author":"Ghosh A.","year":"2009","unstructured":"A. Ghosh , R. P. McAfee , K. Papineni , and S. Vassilvitskii . Bidding for representative allocations for display advertising. CoRR, abs\/0910.0880 , 2009 . A. Ghosh, R. P. McAfee, K. Papineni, and S. Vassilvitskii. Bidding for representative allocations for display advertising. CoRR, abs\/0910.0880, 2009."},{"key":"e_1_2_1_13_1","volume-title":"Auction theory","author":"Krishna V.","year":"2002","unstructured":"V. Krishna . Auction theory . Academic Press , 2002 . V. Krishna. Auction theory. Academic Press, 2002."},{"key":"e_1_2_1_14_1","unstructured":"S. Muthukrishnan. How to auction data. https:\/\/linproxy.fan.workers.dev:443\/https\/sites.google.com\/site\/adauctions2011\/program-and-schedule.  S. Muthukrishnan. How to auction data. https:\/\/linproxy.fan.workers.dev:443\/https\/sites.google.com\/site\/adauctions2011\/program-and-schedule."},{"key":"e_1_2_1_15_1","author":"Vickrey W.","year":"1961","unstructured":"W. Vickrey . Counterspeculation, Auctions and Competitive Sealed Tenders . Journal of Finance, pages 8--37 , 1961 . W. Vickrey. Counterspeculation, Auctions and Competitive Sealed Tenders. 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