Time-Weighted Multi-Touch Attribution and Channel Relevance in the Customer Journey to Online Purchase
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We address statistical issues in attributing revenue to marketing channels and inferring the importance of individual channels in customer journeys toward an online purchase. We describe the relevant data structures and introduce an example. We suggest an asymmetric bathtub shape as appropriate for time-weighted revenue attribution to the customer journey, provide an algorithm, and illustrate the method. We suggest a modification to this method when there is independent information available on the relative values of the channels. To infer channel importance, we employ sequential data analysis ideas and restrict to data which ends in a purchase. We propose metrics for source, intermediary, and destination channels based on two- and three-step transitions in fragments of the customer journey. We comment on the practicalities of formal hypothesis testing. We illustrate the ideas and computations using data from a major UK online retailer. Finally, we compare the revenue attributions suggested by the methods in this article with several common attribution methods.
KeywordsSequential analysis Metrics Clickstream Digital marketing E-commerce Path to conversion
AMS Subject Classification62L10 90B60
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- Abhishek, V., P. Fader, and K. Hosanagar. 2012. The long road to online conversion: A model of multi-channel attribution. https://doi.org/10.2139/ssrn.2158421.
- Agrawal, R., and R. Srikant. 1995. Mining sequential patterns. Technical report, IBM Research Division, Almaden Research Center.Google Scholar
- Dalessandro, B., C. Perlich, O. Stitelman, and F. Provost. 2012. Causally motivated attribution for online advertising. In Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy, ADKDD’ 12, 7: 1–7:9. ACM, New York, NY. doi:10.1145/2351356.Google Scholar
- Gunduz, S., and M. T. Ozsu. 2003. A web page prediction model based on click-stream tree representation of user behavior. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, August 24–27, 2003, 535–540.Google Scholar
- Gunduz-Oguducu, S., and M. T. Ozsu. 2006. Incremental click-stream tree model: Learning from new users for web page prediction. Distrib. Parallel Databases, 19, 1–5.Google Scholar
- Internet Advertising Bureau UK. 2013. 2012 Online Adspend full year results. https://doi.org/www.iabuk.net/research/library/2012-full-year-digital-adspend-results
- Jamalzadeh, A. 2012. Analysis of clickstream data. PhD thesis, Durham University, Durham, UK.Google Scholar
- Osur, A., E. Riley, T. Moffett, S. Glass, and E. Komar. 2012. The Forrester Wave Interactive Attribution Vendors Q2 2012. Technical report, Forrester Research, Inc.Google Scholar
- Shao, X., and L. Li. 2011. Data-driven multi-touch attribution models. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 258–264.Google Scholar
- Xu, L., J. A. Duan, and A. B. Whinston. 2012. Path to purchase: A mutually exciting point process model for online advertising and conversion. https://doi.org/10.2139/ssrn.2149920.
- Zaki, M. J. 2000b. Sequence mining in categorical domains: Incorporating constraints. In Proceedings of the 9th International Conference on Information and Knowledge Management, Washington DC, 422–429.Google Scholar