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2013 | OriginalPaper | Chapter

Uplift Modeling Application and Methodology in Database Marketing

Author : Junjun Yue

Published in: Topics in Applied Statistics

Publisher: Springer New York

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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.

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previous chapter Mixed Modeling for Physician-Direct Campaigns
next chapter Designing Studies for Assessing Efficacy in Mixture Populations
Literature
51.
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):78–86
52.
Nicholas J. Radcliffe and Patrick D. Surry (2011). Real-World Uplift Modeling with Significance-Based Uplift Trees. Stochastic Solutions White Paper
53.
Optimal Targeting through Uplift Modeling: Generating higher demand and increasing customer retention while reducing marketing costs. A white paper by Portrait Software (2006)
54.
Behram Hansotia and Brad Rukstales (2002). Incremental Value Modeling. Journal of Interactive Marketing, 16(3):35–46
Metadata
Title
Uplift Modeling Application and Methodology in Database Marketing
Author
Junjun Yue
Copyright Year
2013
Publisher
Springer New York
Book
Topics in Applied Statistics

Print ISBN: 978-1-4614-7845-4

Electronic ISBN: 978-1-4614-7846-1

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-7846-1_5

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