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Uplift Modeling Application and Methodology in Database Marketing

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 55)

Abstract

While there is a broad consensus that incrementality is the accurate measurement of database marketing impact, few marketing activities today are focused on uplift effect; because most of the target campaigns are selected by leveraging propensity models which maximize the gross response or demand. In this paper, we will introduce a tree-based uplift modeling methodology, which optimizes true marketing profitability. We will also discuss the major stages involved in this approach, with a real-life example from analytic services in the specialty retail industry.

Keywords

Double Difference Market Treatment Good Split Marketing Treatment Propensity Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    Victor S. Y. Lo (2002). The true lift model—A Novel Data Mining Approach to Response Modeling in Database Marketing. ACM SIGKDD Explorations Newsletter, 4(2):78–86Google Scholar
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    Nicholas J. Radcliffe and Patrick D. Surry (2011). Real-World Uplift Modeling with Significance-Based Uplift Trees. Stochastic Solutions White Paper Google Scholar
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    Optimal Targeting through Uplift Modeling: Generating higher demand and increasing customer retention while reducing marketing costs. A white paper by Portrait Software (2006)Google Scholar
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    Behram Hansotia and Brad Rukstales (2002). Incremental Value Modeling. Journal of Interactive Marketing, 16(3):35–46Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Merkle Inc.ColumbiaUSA

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