Skip to main content

Attribution Modeling

  • Living reference work entry
  • First Online:
Handbook of Market Research

Abstract

Marketing attribution is the process of allocating appropriate credit to each marketing touchpoint a customer has encountered before conducting the desired customer action, e.g., a purchase. Ideally, this credit should be capturing the incremental effect of the touchpoint on the customer action. Finding this incremental effect is relevant for marketers to decide on budget allocations and to decide how, when, and where to target which customer. This chapter introduces and discusses various marketing attribution techniques. The techniques range from basic attribution techniques, like touch-based attribution and Shapley values, to advanced attribution techniques, like randomized field experiments and Markov chains. The chapter discusses the up- and downsides of each attribution technique, discusses alternative methods if one method is inappropriate, and links this to the concept of incrementality and causality, i.e., to which degree the technique gives proper credits to the different channels or touchpoints the customer has encountered. This chapter is accompanied by the necessary R-scripts to generate the datasets and estimate the attribution techniques, which can also be downloaded at http://www.evertdehaan.com.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  • Anderl, E., Becker, I., Von Wangenheim, F., & Schumann, J. H. (2016). Mapping the customer journey: Lessons learned from graph-based online attribution modeling. International Journal of Research in Marketing, 33(3), 457–474.

    Article  Google Scholar 

  • Artz, M., & Doering, H. (2021). Exploiting Data from Field Experiments. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Blake, T., Nosko, C., & Tadelis, S. (2015). Consumer heterogeneity and paid search effectiveness: A large-scale field experiment. Econometrica, 83(1), 155–174.

    Article  Google Scholar 

  • Bornemann, T., & Hattula, S. (2018). Experiments in Market Research. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Braun, M., & Moe, W. W. (2013). Online display advertising: Modeling the effects of multiple creatives and individual impression histories. Marketing Science, 32(5), 753–767.

    Article  Google Scholar 

  • Brodersen, K. H., & Hauser, A. (2021). CausalImpact. Available at https://cran.r-project.org/web/packages/CausalImpact/vignettes/CausalImpact.html

  • De Haan, E., Wiesel, T., & Pauwels, K. (2016). The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. International Journal of Research in Marketing, 33(3), 491–507.

    Article  Google Scholar 

  • De Haan, E., Kannan, P. K., Verhoef, P. C., & Wiesel, T. (2018). Device switching in online purchasing: Examining the strategic contingencies. Journal of Marketing, 82(5), 1–19.

    Article  Google Scholar 

  • De Haan, E., Verhoef, P. C., & Wiesel, T. (2021). Customer feedback metrics for marketing accountability. Review of Marketing Research, forthcoming.

    Google Scholar 

  • Ebbes, P., Papies, D., & Van Heerde, H. J. (2016). Dealing with Endogeneity: A Nontechnical Guide for Marketing Researchers. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Försch, S., & de Haan, E. (2018). Targeting online display ads: Choosing their frequency and spacing. International Journal of Research in Marketing, 35(4), 661–672.

    Article  Google Scholar 

  • Gupta, S., & Zeithaml, V. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718–739.

    Article  Google Scholar 

  • Gupta, S., Lehmann, D. R., & Stuart, J. A. (2004). Valuing customers. Journal of Marketing Research, 41(1), 7–18.

    Article  Google Scholar 

  • Hanssens, D. M. (2021). Return on Media Models. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Hitsch, G. J., & Misra, S. (2018). Heterogeneous treatment effects and optimal targeting policy evaluation. Available at SSRN, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3111957

  • Ho, D., Imai, K., King, G., Stuart, E., Whitworth, A., & Greifer, N. (2021). Package ‘MatchIt’. Available at https://cran.r-project.org/web/packages/MatchIt/MatchIt.pdf

  • Hoban, P. R., & Bucklin, R. E. (2015). Effects of internet display advertising in the purchase funnel: Model-based insights from a randomized field experiment. Journal of Marketing Research, 52(3), 375–393.

    Google Scholar 

  • Jackson, C. (2019). Package ‘msm’. Available at https://cran.r-project.org/web/packages/msm/msm.pdf

  • Johnson, L. (2018). When Procter & Gamble Cut $200 Million in Digital Ad Spend, It Increased Its Reach 10%. Available at https://www.adweek.com/brand-marketing/when-procter-gamble-cut-200-million-in-digital-ad-spend-its-marketing-became-10-more-effective/

  • Kannan, P. K., Reinartz, W., & Verhoef, P. C. (2016). The path to purchase and attribution modeling: Introduction to special section.

    Google Scholar 

  • Landwehr, J. R. (2019). Analysis of Variance. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Leeflang, P. S. H., Wieringa, J. E., Bijmolt, T. H. A., & Pauwels, K. (2015). Modeling markets: Analyzing marketing phenomena and improving marketing decision making (Ser. International series in quantitative marketing). Springer.

    Book  Google Scholar 

  • Leeflang, P. S. H., Wieringa, J. E., Bijmolt, T. H. A., & Pauwels, K. (2017). Advanced methods for modeling markets (Ser. International series in quantitative marketing). Springer.

    Book  Google Scholar 

  • Lesscher, L., Lobschat, L., & Verhoef, P. C. (2021). Do offline and online go hand in hand? Cross-channel and synergy effects of direct mailing and display advertising. International Journal of Research in Marketing, forthcoming.

    Google Scholar 

  • Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40–56.

    Article  Google Scholar 

  • Li, J., Luo, X., Lu, X., & Moriguchi, T. (2021). The double-edged effects of E-commerce cart retargeting: Does retargeting too early backfire? Journal of Marketing, forthcoming.

    Google Scholar 

  • Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1–2), 29–39.

    Article  Google Scholar 

  • Pan, W., & Bai, H. (Eds.). (2015). Propensity score analysis: Fundamentals and developments. Guilford Publications.

    Google Scholar 

  • Pauwels, K., Demirci, C., Yildirim, G., & Srinivasan, S. (2016a). The impact of brand familiarity on online and offline media synergy. International Journal of Research in Marketing, 33(4), 739–753.

    Article  Google Scholar 

  • Pauwels, K., Aksehirli, Z., & Lackman, A. (2016b). Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance. International Journal of Research in Marketing, 33(3), 639–655.

    Article  Google Scholar 

  • Rubin, D. B. (2001). Using propensity scores to help design observational studies: application to the tobacco litigation. Health Services and Outcomes Research Methodology, 2(3), 169–188.

    Google Scholar 

  • Rutz, O. J., & Bucklin, R. E. (2011). From generic to branded: A model of spillover in paid search advertising. Journal of Marketing Research, 48(1), 87–102.

    Article  Google Scholar 

  • Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games, 2(28), 307–317.

    Google Scholar 

  • Skiera, B., Reiner, J., & Albers, S. (2018). Regression Analysis. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Spedicato, G. A. (2021). Package ‘markovchain’. Available at https://cran.r-project.org/web/packages/markovchain/markovchain.pdf

  • Spedicato, G. A., Kang, T. S., Yalamanchi, S. B., Yadav, D., & Cordón, I. (2016). The markovchain package: A package for easily handling Discrete Markov Chains in R. https://cran.r-project.org/web/packages/markovchain/vignettes/an_introduction_to_markovchain_package.pdf

  • Srinivasan, S. (2021). Modeling Marketing Dynamics Using Vector Autoregressive (VAR) Models. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Srinivasan, S., Vanhuele, M., & Pauwels, K. (2010). Mind-set metrics in market response models: An integrative approach. Journal of Marketing Research, 47(4), 672–684.

    Article  Google Scholar 

  • Srinivasan, S., Rutz, O. J., & Pauwels, K. (2016). Paths to and off purchase: Quantifying the impact of traditional marketing and online consumer activity. Journal of the Academy of Marketing Science, 44(4), 440–453.

    Article  Google Scholar 

  • Tillmanns, S., & Krafft, M. (2017). Logistic Regression and Discriminant Analysis. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90–102.

    Article  Google Scholar 

  • WARC. (2021). Uber’s former performance chief charts extent of ad fraud. Available at https://www.warc.com/newsandopinion/news/ubers-former-performance-chief-charts-extent-of-ad-fraud/44527

  • Wiesel, T., Pauwels, K., & Arts, J. (2011). Practice prize paper – Marketing’s profit impact: Quantifying online and off-line funnel progression. Marketing Science, 30(4), 604–611.

    Article  Google Scholar 

  • Valli, V., Stahl, F., & Feit, E. M. (2017). Field Experiments. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

  • Wang, W., & Yildirim, G. (2021). Applied Time-Series Analysis in Marketing. In C. Homburg, M. Klarmann, A. E. Vomberg (Eds.). Handbook of Market Research. Springer, Cham.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Evert de Haan .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

de Haan, E. (2022). Attribution Modeling. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-05542-8_39-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05542-8_39-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05542-8

  • Online ISBN: 978-3-319-05542-8

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics