ABSTRACT
In many online advertising campaigns, multiple vendors, publishers or search engines (herein called channels) are contracted to serve advertisements to internet users on behalf of a client seeking specific types of conversion. In such campaigns, individual users are often served advertisements by more than one channel. The process of assigning conversion credit to the various channels is called "attribution," and is a subject of intense interest in the industry. This paper presents a causally motivated methodology for conversion attribution in online advertising campaigns. We discuss the need for the standardization of attribution measurement and offer three guiding principles to contribute to this standardization. Stemming from these principles, we position attribution as a causal estimation problem and then propose two approximation methods as alternatives for when the full causal estimation can not be done. These approximate methods derive from our causal approach and incorporate prior attribution work in cooperative game theory. We argue that in cases where causal assumptions are violated, these approximate methods can be interpreted as variable importance measures. Finally, we show examples of attribution measurement on several online advertising campaign data sets.
- C. Achen. Interpreting and using regression. Number 29. Sage Publications, Inc., 1982.Google Scholar
- L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001. Google ScholarDigital Library
- D. Budescu. Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3):542, 1993.Google ScholarCross Ref
- D. Chan, R. Ge, O. Gershony, T. Hesterberg, and D. Lambert. Evaluating online ad campaigns in a pipeline: causal models at scale. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 7--16. ACM, 2010. Google ScholarDigital Library
- eMarkerter. Tbd. http://www.emarketer.com/Report.aspx?code=emarketer_2000774.Google Scholar
- eMarketer. Us online ad spend to close in on $40 billion. http://www.emarketer.com/Article.aspx?R=1008783.Google Scholar
- I. Finlo. Removing the barriers to growing online media spend: Transparency. http://www.admonsters.com/blog/removingbarriersgrowingonlinemediaspendtransparency, Nov. 2010.Google Scholar
- U. Grömping. Estimators of relative importance in linear regression based on variance decomposition. The American Statistician, 61(2):139--147, 2007.Google Scholar
- A. Hunter, M. Jacobsen, R. Talens, and T. Winders. When money moves to digital, where should it go? http://www.comscore.com/Press_Events/Presentations_Whitepapers/2010/When_Money_Moves_to_Digital_Where_Should_It_Go, Sept. 2010.Google Scholar
- C. Inc. Media attribution. http://www.clearsaleing.com/product/media-attribution/.Google Scholar
- C. M. Inc. What is c3 metrics? http://c3metrics.com/executive-summary/.Google Scholar
- E. Inc. http://www.encoremetrics.com/solution.Google Scholar
- J. Johnson and J. LeBreton. History and use of relative importance indices in organizational research. Organizational Research Methods, 7(3):238, 2004.Google Scholar
- R. Lewis, J. Rao, and D. 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, pages 157--166. ACM, 2011. Google ScholarDigital Library
- S. Lipovetsky and M. Conklin. Analysis of regression in game theory approach. Applied Stochastic Models in Business and Industry, 17(4):319--330, 2001.Google Scholar
- M. Osborne and A. Rubinstein. A course in game theory. The MIT press, 1994.Google Scholar
- J. Pearl. Causality: models, reasoning, and inference, volume 47. Cambridge Univ Press, 2000. Google ScholarDigital Library
- F. Provost, B. Dalessandro, R. Hook, X. Zhang, and A. Murray. Audience selection for on-line brand advertising: privacy-friendly social network targeting. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 707--716. ACM, 2009. Google ScholarDigital Library
- D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 66(5):688, 1974.Google Scholar
- X. Shao and L. Li. Data-driven multi-touch attribution models. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 258--264. ACM, 2011. Google ScholarDigital Library
- L. Shapley. A value for n-person games. The Shapley value, pages 31--40, 1953.Google Scholar
- O. Stitelman, B. Dalessandro, C. Perlich, and F. Provost. Estimating the effect of online display advertising on browser conversion. Data Mining and Audience Intelligence for Advertising (ADKDD 2011), page 8, 2011.Google Scholar
- C. Strobl, A. Boulesteix, A. Zeileis, and T. Hothorn. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, 8(1):25, 2007.Google Scholar
- A. Tsiatis. Semiparametric theory and missing data. Springer Verlag, 2006.Google Scholar
- M. Van Der Laan. Statistical inference for variable importance. The International Journal of Biostatistics, 2(1):2, 2006.Google Scholar
- M. Van Der Laan, S. Dudoit, and S. Keles. Asymptotic optimality of likelihood-based cross-validation. Statistical Applications in Genetics and Molecular Biology, 3(1):4, 2004.Google Scholar
Index Terms
- Causally motivated attribution for online advertising
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