Social Networks, Personalized Advertising, and Privacy Controls
This paper investigates how internet users' perception of control over their personal information affects how likely they are to click on online advertising. The paper uses data from a randomized field experiment that examined the relative effectiveness of personalizing ad copy to mesh with existing personal information on a social networking website. The website gave users more control over their personally identifiable information in the middle of the field test. The website did not change how advertisers used anonymous data to target ads. After this policy change, users were twice as likely to click on personalized ads. There was no comparable change in the effectiveness of ads that did not signal that they used private information when targeting. The increase in effectiveness was larger for ads that used less commonly available private information to personalize their message. This suggests that giving users the perception of more control over their private information can be an effective strategy for advertising-supported websites.
References listed on IDEAS
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- Bharat Anand & Ron Shachar, 2009. "Targeted advertising as a signal," Quantitative Marketing and Economics (QME), Springer, vol. 7(3), pages 237-266, September.
- Kihlstrom, Richard E & Riordan, Michael H, 1984. "Advertising as a Signal," Journal of Political Economy, University of Chicago Press, vol. 92(3), pages 427-450, June.
- Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
- Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
- Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
- Meghan Busse & Jorge Silva-Risso & Florian Zettelmeyer, 2006. "$1,000 Cash Back: The Pass-Through of Auto Manufacturer Promotions," American Economic Review, American Economic Association, vol. 96(4), pages 1253-1270, September.
- Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004.
"How Much Should We Trust Differences-In-Differences Estimates?,"
The Quarterly Journal of Economics,
Oxford University Press, vol. 119(1), pages 249-275.
- Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2002. "How Much Should We Trust Differences-in-Differences Estimates?," NBER Working Papers 8841, National Bureau of Economic Research, Inc.
- McAfee, R Preston & McMillan, John, 1987. "Auctions and Bidding," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 699-738, June.
- Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
- N. Lesca, 2010. "Introduction," Post-Print halshs-00640602, HAL. Full references (including those not matched with items on IDEAS)