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Deriving dynamic marketing effectiveness from econometric time series models

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  • Horváth, C.
  • Franses, Ph.H.B.F.

Abstract

To understand the relevance of marketing efforts, it has become standard practice to estimate the long-run and short-run effects of the marketing-mix, using, say, weekly scanner data. A common vehicle for this purpose is an econometric time series model. Issues that are addressed in the literature are unit roots, cointegration, structural breaks and impulse response functions. In this paper we summarize the most important concepts by reviewing all possible empirical cases that can be encountered in practice using a prototypical model. We provide guidelines for practitioners, and illustrate these for a detailed workedout example.

Suggested Citation

  • Horváth, C. & Franses, Ph.H.B.F., 2003. "Deriving dynamic marketing effectiveness from econometric time series models," ERIM Report Series Research in Management ERS-2003-079-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:1016
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    References listed on IDEAS

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    1. G. Dekimpe, Marnik & Hanssens, Dominique M. & Silva-Risso, Jorge M., 1998. "Long-run effects of price promotions in scanner markets," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 269-291, November.
    2. Vincent R. Nijs & Marnik G. Dekimpe & Jan-Benedict E.M. Steenkamps & Dominique M. Hanssens, 2001. "The Category-Demand Effects of Price Promotions," Marketing Science, INFORMS, vol. 20(1), pages 1-22, September.
    3. Rajiv Lal & V. Padmanabhan, 1995. "Competitive Response and Equilibria," Marketing Science, INFORMS, vol. 14(3_supplem), pages 101-108.
    4. Robert C. Blattberg & Kenneth J. Wisniewski, 1989. "Price-Induced Patterns of Competition," Marketing Science, INFORMS, vol. 8(4), pages 291-309.
    5. Grewal, Rajdeep & Mills, Jeffrey A. & Mehta, Raj & Mujumdar, Sudesh, 2001. "Using cointegration analysis for modeling marketing interactions in dynamic environments: methodological issues and an empirical illustration," Journal of Business Research, Elsevier, vol. 51(2), pages 127-144, February.
    6. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "Empirical Generalizations About Market Evolution and Stationarity," Marketing Science, INFORMS, vol. 14(3_supplem), pages 109-121.
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    Cited by:

    1. Sismeiro, Catarina & Mizik, Natalie & Bucklin, Randolph E., 2012. "Modeling coexisting business scenarios with time-series panel data: A dynamics-based segmentation approach," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 134-147.

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    More about this item

    Keywords

    dynamic effects; econometric time series models; marketing mix;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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