Journal of Marketing Analytics

, Volume 3, Issue 2, pp 79–95 | Cite as

From predictive uplift modeling to prescriptive uplift analytics: A practical approach to treatment optimization while accounting for estimation risk

Original Article
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Abstract

Uplift modeling, a predictive modeling technique, empowers marketers or other researchers to identify the ‘true’ treatment responders who would be most positively influenced by the treatment or intervention through uncovering their characteristics separately from the characteristics of baseline or control responders (that is, those who would have responded anyway). This article briefly reviews the concept of uplift modeling and extends the current work to multiple treatment situations (where at least two treatments are available as options). It discusses the mathematical problem of optimizing treatment at the individual level, and proposes a practical heuristic solution. Finally, it presents a framework accounting for the variability in estimates when handling multiple assignments. An example from an online retailer is used to illustrate the methodologies.

Keywords

uplift modeling prescriptive analytics prescriptive uplift analytics marketing treatment optimization estimation risk mean-variance optimization 
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Notes

Acknowledgements

The authors would like to thank Florence H. Yong and Kathleen Kane for reviewing an earlier version of this paper.

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Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2015

Authors and Affiliations

  1. 1.Division of Mathematics and SciencesBabson CollegeWellesleyUSA

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