IDEAS home Printed from https://ideas.repec.org/p/ecl/stabus/2039.html
   My bibliography  Save this paper

Nonparametric Estimation of Marketing-Mix Effects Using a Regression Discontinuity Design

Author

Listed:
  • Hartmann, Wesley

    (Stanford University)

  • Nair, Harikesh

    (Stanford University)

  • Narayanan, Sridhar

    (Stanford University)

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.

Suggested Citation

  • Hartmann, Wesley & Nair, Harikesh & Narayanan, Sridhar, 2009. "Nonparametric Estimation of Marketing-Mix Effects Using a Regression Discontinuity Design," Research Papers 2039, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:2039
    as

    Download full text from publisher

    File URL: http://gsbapps.stanford.edu/researchpapers/library/RP2039.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ke-Wei Huang, 2009. "Optimal criteria for selecting price discrimination metrics when buyers have log-normally distributed willingness-to-pay," Quantitative Marketing and Economics (QME), Springer, vol. 7(3), pages 321-341, September.
    2. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    3. Kenneth Y. Chay & Michael Greenstone, 2005. "Does Air Quality Matter? Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 376-424, April.
    4. Meghan Busse & Jorge Silva-Risso & Florian Zettelmeyer, 2006. "$1,000 Cash Back: The Pass-Through of Auto Manufacturer Promotions," American Economic Review, American Economic Association, vol. 96(4), pages 1253-1270, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Jorge Silva-Risso & Irina Ionova, 2008. "—A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions," Marketing Science, INFORMS, vol. 27(4), pages 545-566, 07-08.
    3. David R. Bell & Jeongwen Chiang & V. Padmanabhan, 1999. "The Decomposition of Promotional Response: An Empirical Generalization," Marketing Science, INFORMS, vol. 18(4), pages 504-526.
    4. Douglas Almond & Yuyu Chen & Michael Greenstone & Hongbin Li, 2009. "Winter Heating or Clean Air? Unintended Impacts of China's Huai River Policy," American Economic Review, American Economic Association, vol. 99(2), pages 184-190, May.
    5. repec:ces:ifodic:v:14:y:2016:i:1:p:19204333 is not listed on IDEAS
    6. Leenheer, J. & Bijmolt, T.H.A. & van Heerde, H.J. & Smidts, A., 2002. "Do Loyalty Programs Enhance Behavioral Loyalty : An Empirical Analysis Accounting for Program Design and Competitive Effects," Discussion Paper 2002-65, Tilburg University, Center for Economic Research.
    7. Nishitateno, Shuhei & Burke, Paul J., 2021. "Willingness to pay for clean air: Evidence from diesel vehicle registration restrictions in Japan," Regional Science and Urban Economics, Elsevier, vol. 88(C).
    8. Chadwick J. Miller & Daniel C. Brannon & Jim Salas & Martha Troncoza, 2021. "Advertising, incentives, and the upsell: how advertising differentially moderates customer- vs. retailer-directed price incentives’ impact on consumers’ preferences for premium products," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1043-1064, November.
    9. Guignet, Dennis & Jenkins, Robin R. & Belke, James & Mason, Henry, 2023. "The property value impacts of industrial chemical accidents," Journal of Environmental Economics and Management, Elsevier, vol. 120(C).
    10. Waights, Sevrin, 2018. "Does the law of one price hold for hedonic prices?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 55(15), pages 3299-3317.
    11. Kopalle, Praveen K. & Pauwels, Koen & Akella, Laxminarayana Yashaswy & Gangwar, Manish, 2023. "Dynamic pricing: Definition, implications for managers, and future research directions," Journal of Retailing, Elsevier, vol. 99(4), pages 580-593.
    12. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    13. Helen Tauchen & Ann Dryden Witte, 2001. "Estimating Hedonic Models: Implications of the Theory," NBER Technical Working Papers 0271, National Bureau of Economic Research, Inc.
    14. Aaron Sojourner, "undated". "Partial identification of willingness-to-pay using shape restrictions with an application to the value of a statistical life," Working Papers 0110, Human Resources and Labor Studies, University of Minnesota (Twin Cities Campus).
    15. Nicolai V. Kuminoff & Jaren C. Pope, 2014. "Do “Capitalization Effects” For Public Goods Reveal The Public'S Willingness To Pay?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(4), pages 1227-1250, November.
    16. Janet Currie & Reed Walker, 2011. "Traffic Congestion and Infant Health: Evidence from E-ZPass," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 65-90, January.
    17. Banzhaf, H. Spencer & Farooque, Omar, 2013. "Interjurisdictional housing prices and spatial amenities: Which measures of housing prices reflect local public goods?," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 635-648.
    18. Dröes, Martijn I. & Koster, Hans R.A., 2016. "Renewable energy and negative externalities: The effect of wind turbines on house prices," Journal of Urban Economics, Elsevier, vol. 96(C), pages 121-141.
    19. Koichiro Ito & Shuang Zhang, 2020. "Willingness to Pay for Clean Air: Evidence from Air Purifier Markets in China," Journal of Political Economy, University of Chicago Press, vol. 128(5), pages 1627-1672.
    20. Xu Xu & Kevin Sylwester, 2016. "Environmental Quality and International Migration," Kyklos, Wiley Blackwell, vol. 69(1), pages 157-180, February.
    21. Gabriel Ahlfeldt & Pantelis Koutroumpis & Tommaso Valletti, 2017. "Speed 2.0: Evaluating Access to Universal Digital Highways," Journal of the European Economic Association, European Economic Association, vol. 15(3), pages 586-625.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecl:stabus:2039. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/gsstaus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.