A Non-parametric Approach to the Multi-channel Attribution Problem

  • Meghanath Macha YadagiriEmail author
  • Shiv Kumar Saini
  • Ritwik Sinha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9418)


Multi-channel marketing attribution modeling is a two-stage process. First, the value of exposure from different marketing channel needs to be estimated. Next, the total surplus achieved needs to be assigned to individual marketing channels by using the exposure effects from the first stage. There has been limited work in exploring possible choices and effects of determining the value of exposure to different marketing channels in the first stage. This paper proposes novel non-parametric and semi-parametric approaches to estimate the value function and compares it with other natural choices. We build a simulation engine that captures important behavioral phenomenon known to affect a customer’s purchase decision; and compare the performance of five attribution approaches in their ability to closely approximate the known ground truth. Our proposed method works well when marketing channels have high levels of synergy. We apply the proposed approaches on two real-world datasets and present the results.


Multi-channel attribution Shapley Value Simulation study 


  1. 1.
    Abhishek, V., Fader, P.S., Hosanagar, K.: Media exposure through the funnel: a model of multi-stage attribution. Technical report, Working paper. Carnegie Mellon University, Pittsburgh (2013)Google Scholar
  2. 2.
    Armstrong, J.S., Collopy, F.: Error measures for generalizing about forecasting methods: empirical comparisons. Int. J. Forecast. 8(1), 69–80 (1992)CrossRefGoogle Scholar
  3. 3.
    Binmore, K., Rubinstein, A., Wolinsky, A.: The nash bargaining solution in economic modelling. RAND J. Econ. 17, 176–188 (1986)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Dalessandro, B., Perlich, C., Stitelman, O., Provost, F.: Causally motivated attribution for online advertising. In: Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy, p. 7. ACM (2012)Google Scholar
  6. 6.
    Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., Tibshirani, R.: The Elements of Statistical Learning. Springer, New York (2009)zbMATHCrossRefGoogle Scholar
  7. 7.
    Shao, X., Li, L.: Data-driven multi-touch attribution models. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 258–264. ACM (2011)Google Scholar
  8. 8.
    Shapley, L.S.: A value for n-person games. Technical report, DTIC Document (1952)Google Scholar
  9. 9.
    Sinha, R., Saini, S., Anadhavelu, N.: Estimating the incremental effects of interactions for marketing attribution. In: 2014 International Conference on Behavior, Economic and Social Computing (BESC), pp. 1–6. IEEE (2014)Google Scholar
  10. 10.
    Skidmore, A.K.: A comparison of techniques for calculating gradient and aspect from a gridded digital elevation model. Int. J. Geog. Infom. Syst. 3(4), 323–334 (1989)CrossRefGoogle Scholar
  11. 11.
    Xu, L., Duan, J.A., Whinston, A.: Path to purchase: a mutually exciting point process model for online advertising and conversion. Manage. Sci. 60(6), 1392–1412 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Meghanath Macha Yadagiri
    • 1
    Email author
  • Shiv Kumar Saini
    • 1
  • Ritwik Sinha
    • 1
  1. 1.Adobe ResearchBangaloreIndia

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