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Identifying Causal Marketing Mix Effects Using a Regression Discontinuity Design


  • Wesley Hartmann

    () (Stanford Graduate School of Business, Stanford University, Stanford, California 94305)

  • Harikesh S. Nair

    () (Stanford Graduate School of Business, Stanford University, Stanford, California 94305)

  • Sridhar Narayanan

    () (Stanford Graduate School of Business, Stanford University, Stanford, California 94305)


We discuss how regression discontinuity designs arise naturally in settings where firms target marketing activity at consumers, and we illustrate how this aspect may be exploited for econometric inference of causal effects of marketing effort. Our main insight is to use commonly observed discontinuities 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 show 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 emphasize the role of selection for identification. We present two empirical applications: the first measures the effect of casino e-mail promotions targeted to customers based on ranges of their expected profitability, and the second measures the effect of direct mail targeted by a business-to-consumer company to zip codes based on cutoffs 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 nonparametric, easy to compute, and control for the endogeneity induced by the targeting rule.

Suggested Citation

  • Wesley Hartmann & Harikesh S. Nair & Sridhar Narayanan, 2011. "Identifying Causal Marketing Mix Effects Using a Regression Discontinuity Design," Marketing Science, INFORMS, vol. 30(6), pages 1079-1097, November.
  • Handle: RePEc:inm:ormksc:v:30:y:2011:i:6:p:1079-1097
    DOI: 10.1287/mksc.1110.0670

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    Cited by:

    1. Sungho Park & Sachin Gupta, 2012. "Handling Endogenous Regressors by Joint Estimation Using Copulas," Marketing Science, INFORMS, vol. 31(4), pages 567-586, July.
    2. Wooyong Jo & Sarang Sunder & Jeonghye Choi & Minakshi Trivedi, 2020. "Protecting Consumers from Themselves: Assessing Consequences of Usage Restriction Laws on Online Game Usage and Spending," Marketing Science, INFORMS, vol. 39(1), pages 117-133, January.
    3. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
    4. Harikesh S. Nair & Sanjog Misra & William J. Hornbuckle IV & Ranjan Mishra & Anand Acharya, 2017. "Big Data and Marketing Analytics in Gaming: Combining Empirical Models and Field Experimentation," Marketing Science, INFORMS, vol. 36(5), pages 699-725, September.
    5. Arun Gopalakrishnan & Eric T. Bradlow & Peter S. Fader, 2017. "A Cross-Cohort Changepoint Model for Customer-Base Analysis," Marketing Science, INFORMS, vol. 36(2), pages 195-213, March.
    6. Sridhar Narayanan & Kirthi Kalyanam, 2015. "Position Effects in Search Advertising and their Moderators: A Regression Discontinuity Approach," Marketing Science, INFORMS, vol. 34(3), pages 388-407, May.
    7. Jura Liaukonyte & Thales Teixeira & Kenneth C. Wilbur, 2015. "Television Advertising and Online Shopping," Marketing Science, INFORMS, vol. 34(3), pages 311-330, May.
    8. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    9. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    10. Makoto Mizuno & Hideaki Aoyama & Yoshi Fujiwara, 2020. "Untangling the complexity of market competition in consumer goods -A complex Hilbert PCA analysis," Papers 2008.11327,
    11. Puneet Manchanda & Grant Packard & Adithya Pattabhiramaiah, 2015. "Social Dollars: The Economic Impact of Customer Participation in a Firm-Sponsored Online Customer Community," Marketing Science, INFORMS, vol. 34(3), pages 367-387, May.
    12. MIZUNO Makoto & AOYAMA Hideaki & FUJIWARA Yoshi, 2020. "Constructing the Customer Journey Map of Competitive Brands: A Complex Time-series Analysis," Discussion papers 20070, Research Institute of Economy, Trade and Industry (RIETI).


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