ABSTRACT
Advertisers are demanding more accurate estimates of the impact of targeted advertisements, yet no study proposes an appropriate methodology to analyze the effectiveness of a targeted advertising campaign, and there is a dearth of empirical evidence on the effectiveness of targeted advertising as a whole. The targeted population is more likely to convert from advertising so the response lift between the targeted and untargeted group to the advertising is likely an overestimate of the impact of targeted advertising. We propose a difference-in-differences estimator to account for this selection bias by decomposing the impact of targeting into selection bias and treatment effects components. Using several large-scale online advertising campaigns, we test the effectiveness of targeted advertising on brand-related searches and clickthrough rates. We find that the treatment effect on the targeted group is about twice as large for brand-related searches, but naively estimating this effect without taking into account selection bias leads to an overestimation of the lift from targeting on brand-related searches by almost 1,000%.
Supplemental Material
Available for Download
== AUXILIARY AND SUPPLEMENTARY MATERIAL FOR FARAHAT AND BAILEY == HOW EFFECTIVE IS TARGETED ADVERTISING? == PUBLISHED IN THE PROCEEDINGS OF WWW'2012, April 16-20, 2012, Lyon, France == The online appendix of all data, tables, and results are found in the pdf file fp1272.pdf.
- B. Anand and R. Shachar. Targeted advertising as a signal. Quantitative Marketing and Economics, 7(3):237--266, 2009.Google ScholarCross Ref
- J. Angrist and G. Imbens. Two-Stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association, 90(430):431--442, 1995.Google ScholarCross Ref
- J. Angrist, G. Imbens, and D. Rubin. Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434):444--455, June 1996.Google ScholarCross Ref
- K. Bagwell. Chapter 28 the economic analysis of advertising. Handbook of Industrial Organization, 3:1701--1844, 2007.Google Scholar
- H. Beales. The value of behavioral targeting. Network Advertising Initiative, 2010.Google Scholar
- G. Box and D. Cox. An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), page 211--252, 1964.Google Scholar
- K. L. Chang and V. K. Narayanan. Performance analysis of behavioral targeting at yahoo! Technical report, Advertising Sciences, Yahoo! Labs, 2010.Google Scholar
- J. Chen and J. Stallaert. An economic analysis of online advertising using behavioral targeting. Technical report, University of Connecticut, 2010.Google Scholar
- T. Chen, J. Yan, G. Xue, and Z. Chen. Transfer learning for behavioral targeting. In Proceedings of the 19th international conference on World wide web, pages 1077--1078, 2010. Google ScholarDigital Library
- Y. Chen, D. Pavlov, and J. F. Canny. Large-scale behavioral targeting. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 209--218, 2009. Google ScholarDigital Library
- E. Gal-Or, M. Gal-Or, J. May, and W. Spangler. Targeted advertising strategies on television. Management Science, 52(5):713--725, May 2006. Google ScholarDigital Library
- G. Imbens and J. Angrist. Identification and estimation of local average treatment effects. Econometrica, 62(2):467--475, 1994.Google ScholarCross Ref
- G. Iyer, D. Soberman, and M. V. Boas. The targeting of advertising. Marketing Science, 24(3), 2005. Google ScholarDigital Library
- M. Joo, K. Wilbur, and Y. Zhu. Television advertising and online search. Available at SSRN: http://papers. ssrn. com/sol3/papers. cfm, 2010.Google Scholar
- P. Kazienko and M. Adamski. AdROSA-Adaptive personalization of web advertising. Information Sciences, 177(11):2269--2295, June 2007. Google ScholarDigital Library
- A. Lambrecht and C. Tucker. When does retargeting work? timing information specificity. SSRN eLibrary, July 2011.Google Scholar
- J. Lecinski. Zero Moment of Truth. http://www.zeromomentoftruth.com/Google Scholar
- R. A. Lewis, J. M. Rao, and D. H. Reiley. Here, there, and everywhere: correlated online behaviors can lead to overestimates of the effects of advertising. In Proceedings of the 20th international conference on World wide web, page 157--166, 2011. Google ScholarDigital Library
- P. Melville, S. Rosset, and R. D. Lawrence. Customer targeting models using actively-selected web content. Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 946--953, 2008. Google ScholarDigital Library
- D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5):688--701, 1974.Google ScholarCross Ref
- D. Vakratsas, F. Feinberg, F. Bass, and G. Kalyanaram. The shape of advertising response functions revisited: A model of dynamic probabilistic thresholds. Marketing Science, 23(1):109--119, Jan. 2004. Google ScholarDigital Library
- T. Wiesel, K. Pauwels, and J. Arts. Practice prize Paper-Marketing's profit impact: Quantifying online and off-line funnel progression. Marketing Science, 30(4):604--611, 2011. Google ScholarDigital Library
- J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. How much can behavioral targeting help online advertising? In Proceedings of the 18th international conference on World wide web, pages 261--270, 2009. Google ScholarDigital Library
Index Terms
- How effective is targeted advertising?
Recommendations
Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?
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 ...
How much can behavioral targeting help online advertising?
WWW '09: Proceedings of the 18th international conference on World wide webBehavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking ...
An economic analysis of online advertising using behavioral targeting
Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and ...
Comments