Xianghua Lu
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Abstract
Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of this study is to examine how behavioral targeting (BT) impacts users’ responses to online ads and particularly whether BT works better in combination with contextual targeting (CT). Using a large, individual-level clickstream data set of an automobile advertising campaign from an Internet advertising intermediary, this study examines the impact of BT and CT strategies on users’ click behavior. The results show that (1) targeting a user with behavioral characteristics that are closely related to ads does not necessarily increase the click through rates (CTRs); whereas, targeting a user with behavioral characteristics that are loosely related to ads leads to a higher CTR, and (2) BT and CT work better in combination. Our study contributes to online advertising design literature and provides important managerial implications for advertising intermediaries and advertisers on targeting individual users.
- J. Angwin. 2010. The Web's New Gold Mine: Your Secrets. The Wall Street Journal—What They Know. Retrieved from http://online.wsj.com/news/articles/SB10001424052748703940904575395073512989404/.Google Scholar
- H. Beales. 2010. The Value of Behavioral Targeting. Network Advertising Initiative (NAI). Retrieved from http://www.turn.com/sites/default/files/wp-content/uploads/2010/06/Beales_NAI_Study.pdf.Google Scholar
- M. Braun and W. Moe. 2013. Modeling the effects of multiple creatives and individual impression histories. Marketing Science 32, 5, 753--767. Google ScholarDigital Library
- D. Breznitz and V. Palermo. 2013. A strategic advantage with behavioral targeting? How can (and what) firms benefit from personal data-based online marketing strategies. In Proceedings of the 35th DRUID Celebration Conference.Google Scholar
- M. C. Campbell. 1995. When attention-getting tactics elicit consumer inferences of manipulative intent: The importance of balancing benefits and investments. Journal of Consumer Psychology 4, 3, 225--254.Google ScholarCross Ref
- P. Chatterjee, D. Hoffman, and T. Novak. 2003. Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science 22, 4, 520--541. Google ScholarDigital Library
- China Internet Network Information Center (CNNIC). 2014. Annual report of online shopping behavior analysis, Retrieved from http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/dzswbg/201301 at 2014/04/05.Google Scholar
- C.-H. Cho and H. J. Cheon. 2004. Why do people avoid advertising on the Internet? Journal of Advertising 33, 4, 89--97.Google ScholarCross Ref
- Consumer Union. 2008. Consumer reports poll: Americans extremely concerned about Internet privacy. Retrieved from http://www.consumersunion.org/pub/core_telecom_and_utilities/006189.html.Google Scholar
- J. Dubin and D. Rivers. 1989. Selection bias in linear regression, logit and probit models. Sociological Methods Research 18, 2--3, 360--390.Google ScholarCross Ref
- A. Edgcomb and F. Vahid. 2013. Accurate and efficient algorithms that adapt to privacy-enhanced video for improved assistive monitoring. ACM Transactions on Management Information Systems 4, 3, Article No. 14. Google ScholarDigital Library
- A. Farahat and M. C. Bailey. 2012. How effective is targeted advertising? In Proceedings of the 21st International Conference on the World Wide Web. ACM, 111--120. Google ScholarDigital Library
- A. Ghose, P. G. Ipeirotis, and B. Li. 2014. Examining the impact of ranking on consumer behavior and search engine revenue. Management Science 60, 7, 1632--1654. Google ScholarDigital Library
- A. Ghose and S. Yang. 2009. An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Science 55, 10, 1605--1623. Google ScholarDigital Library
- A. Goldfarb and C. Tucker. 2011a. Online display advertising: Targeting and obtrusiveness. Marketing Science 30, 3, 389--404. Google ScholarDigital Library
- A. Goldfarb and C. Tucker. 2011b. Privacy regulation and online advertising. Management Science 57, 1, 57--71. Google ScholarDigital Library
- A. Goldfarb and C. Tucker. 2011c. Search engine advertising: Channel substitution when pricing ads to context. Management Science 57, 3, 458--470. Google ScholarDigital Library
- IAB. 2010. Targeting Local Markets: An IAB Interactive Advertising Guide. Interactive Advertising Bureau. Retrieved from http://www.iab.net/media/file/IAB_Local_Targeting_Guide_0922_FINAL.pdf.Google Scholar
- H. E. Krugman. 1983. Television program interest and commercial interruption: Are commercials on interesting programs less effective? Journal of Advertising Research 23, 21--23.Google Scholar
- A. Lambrecht and C. Tucker. 2013. When does retargeting work? Information specificity in online advertising. Journal of Marketing Research 50, 5, 561--576.Google ScholarCross Ref
- H. Lebo. 2014. The 2008 digital future report: Surveying the digital future. USC Annenberg School Center for the Digital Future. Retrieved from http://www.digitalcenter.org/wp-content/uploads/2014/12/2014-Digital-Future-Report.pdf.Google Scholar
- H. Li, S. M. Edward, and J. Lee. 2002. Measuring the intrusiveness of advertisements: Scale development and validation. Journal of Advertising 31, 37--47.Google ScholarCross Ref
- X. Ma, S. H. Kim, and S. S. Kim. 2014. Online gambling behavior: The impacts of cumulative outcomes, recent outcomes, and prior use. Information Systems Research 25, 3, 511--527. Google ScholarDigital Library
- G. Mandler. 1982. The structure of value: Accounting for taste. In Affect and Cognition: The 17th Annual Carnegie Symposium, M. S. Clark and S. T. Fisk (Eds.). Lawrence Erlbaum Associates, Hillsdale, NJ, 203--230.Google Scholar
- P. Manchanda, J.-P. Dube, K. Y. Goh, and P. K. Chintagunta. 2006. The effect of banner advertising on Internet purchasing. Journal of Marketing Research XLIII, 98--108.Google ScholarCross Ref
- N. K. Malhotra, S. S. Kim, and Agarwal. 2004. Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research 15, 4, 336--355. Google ScholarDigital Library
- G. Mathew and Z. Obradovic. 2013. Distributed privacy-preserving decision support system for highly imbalanced clinical data. ACM Transactions on Management Information Systems 4, 3, Article 12. Google ScholarDigital Library
- S. Mithas, N. Ramasubbu, M. S. Krishnan, and C. Fornell. 2007. Designing web sites for customer loyalty across business domains: A multilevel analysis. Journal of Management Information Systems 23, 3, 97--127. Google ScholarDigital Library
- R. S. Moore, C. A. Stammerjohan, and R. A. Coulter. 2005. Banner advertiser-web site congruity and color effects on attention and attitudes. Journal of Advertising 34, 2, 71--84.Google ScholarCross Ref
- J. Pancras and K. Sudhir. 2007. Optimal marketing strategies for a customer data intermediary. Journal of Marketing Research XLIV, 560--578.Google ScholarCross Ref
- S. Rodgers. 2003/2004. The effects of sponsor relevance on consumer reactions to Internet sponsorships. Journal of Advertising 32, 4, 67--76.Google ScholarCross Ref
- E. Smith. 2014. Targeted Display to Drive Online Ad Growth. Retrieved from http://www.netnewscheck.com/article/35766/targeted-display-to-drive-online-ad-growth.Google Scholar
- C. E. Tucker. 2014. Social networks, personalized advertising, and privacy controls. Journal of Marketing Research 51, 5, 546--562.Google ScholarCross Ref
- J. Turow, J. King, C. J. Hoofnagle, A. Bleakley, and M. Hennessy. 2009. Americans reject tailored advertising and three activities that enable it. SSRN Working Paper. Retrieved from http://ssrn.com/abstract = 1478214.Google Scholar
- I. Weber and A. Jaimes. 2011. Who uses web search for what: and how. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 15--24. Google ScholarDigital Library
- J. M. Woodridge. 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed. MIT Press Cambridge, Cambridge.Google Scholar
- J. Yan, G. Wang, E. Zhang, Y. Jiang, and Z. Chen. 2009. How much can behavioral targeting help online advertising? In Proceedings of WWW 2009 MADRID. Retrieved from http://www2009.eprints.org. Google ScholarDigital Library
- K. Zhang and Z. Katona. 2012. Contextual advertising. Marketing Science 31, 6, 980--994. Google ScholarDigital Library
- X. Zhao. 2012. Service design of a customer data intermediary for competitive target promotions. Decision Support Systems 54, 1, 699--718. Google ScholarDigital Library
- X. Zhao and L. Xue. 2013. Competitive target advertising and consumer data sharing. Journal of Management Information Systems 29, 3, 189--222.Google ScholarCross Ref
Index Terms
- Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?
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