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Identifying Causal Marketing-Mix Effects Using a Regression Discontinuity Design


Wesley R. Hartmann


Stanford University - Graduate School of Business

Harikesh Nair


Stanford University - Graduate School of Business

Sridhar Narayanan


Stanford Graduate School of Business

May 1, 2011

Stanford University Graduate School of Business Research Paper No. 2039 (R)

Abstract:     
We discuss how regression discontinuity designs arise naturally in settings where firms target marketing activity at consumers, and discuss how this aspect may be exploited for econometric inference of causal effects of marketing effort. Our main insight is to use commonly observed discreteness and kinks in the heuristics by which firms target such marketing activity to consumers for nonparametric identification. Such kinks, along with continuity restrictions that are typically satisfied in marketing and industrial organization applications, are sufficient for identification of local treatment effects. We review the theory of regression discontinuity estimation in the context of targeting, and explore its applicability to several marketing settings. We discuss identifiability of causal marketing effects using the design, and illustrate theoretically the conditions under which the RD estimator may be valid. Specifically, we argue that consideration of an underlying model of strategic consumer behavior reveals how identification hinges on model features such as the specification and value of structural parameters as well as belief structures. We present two empirical applications: the first, to measuring the effect of casino e-mail promotions targeted to customers based on ranges of their expected profitability; and the second, to measuring the effect of direct mail targeted by a B2C company to zip-codes based on thresholds of expected response. In both cases, we illustrate that exploiting the regression discontinuity design reveals negative effects of the marketing campaigns that would not have been uncovered using other approaches. Our results are nonparameteric, easy to compute, and fully control for the endogeneity induced by the targeting rule.

Number of Pages in PDF File: 39

Keywords: endogeneity, discontinuity, treatment effects, selection, direct-mail, regression, nonparametric identification, targeted marketing, casinos

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Date posted: December 21, 2009 ; Last revised: June 4, 2011

Suggested Citation

Hartmann, Wesley R., Nair, Harikesh and Narayanan, Sridhar, Identifying Causal Marketing-Mix Effects Using a Regression Discontinuity Design (May 1, 2011). Stanford University Graduate School of Business Research Paper No. 2039 (R). Available at SSRN: http://ssrn.com/abstract=1525113 or http://dx.doi.org/10.2139/ssrn.1525113

Contact Information

Wesley R. Hartmann (Contact Author)
Stanford University - Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
United States

Harikesh Nair
Stanford University - Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
United States
650-736-4256 (Phone)
HOME PAGE: http://faculty-gsb.stanford.edu/nair/index.html

Sridhar Narayanan
Stanford Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
United States
650-723-9675 (Phone)
HOME PAGE: http://https://gsbapps.stanford.edu/facultybios/bio.asp?ID=409

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