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Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models

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  • Kristiaan Helsen

    (University of Chicago)

  • David C. Schmittlein

    (University of Pennsylvania)

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    Abstract

    Some statistical methods developed recently in the biometrics and econometrics literature show great promise for improving the analysis of duration times in marketing. They incorporate the right censoring that is prevalent in duration times data, and can be used to make a wide variety of useful predictions. Both of these features make these methods preferable to the regression, logit, and discriminant analyses that marketers have typically used in analyzing durations. This paper is intended to fulfill three objectives. First, we demonstrate how decision situations that involve durations differ from other marketing phenomena. Second, we show how standard modeling approaches to handle duration times can break down because of the peculiarities inherent in durations. It has been suggested in recent marketing articles that an alternative to these conventional procedures, i.e., hazard rate models and proportional hazard regression, can more effectively handle duration type data. Third, to investigate whether these proposed benefits are in fact delivered for marketing durations data, we estimate and validate both conventional and hazard rate models for household interpurchase times of saltine crackers. Our findings indicate the superiority of proportional hazard regression methods vis-à-vis common procedures in terms of stability and face validity of the estimates and in predictive accuracy.

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    File URL: http://dx.doi.org/10.1287/mksc.12.4.395
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 12 (1993)
    Issue (Month): 4 ()
    Pages: 395-414

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    Handle: RePEc:inm:ormksc:v:12:y:1993:i:4:p:395-414

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    Related research

    Keywords: econometric modeling; estimation and other statistical techniques; pricing research; promotion; regression and other statistical techniques;

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    Cited by:
    1. Kim, Namwoon & Srivastava, Rajendra K. & Han, Jin K., 2001. "Consumer decision-making in a multi-generational choice set context," Journal of Business Research, Elsevier, vol. 53(3), pages 123-136, September.
    2. Fok, D. & Paap, R. & Franses, Ph.H.B.F., 2002. "Modeling dynamic effects of promotion on interpurchase times," Econometric Institute Research Papers EI 2002-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Uwe Cantner & Kristina Dreßler & Jens J. Krüger, 2006. "Firm survival in the German automobile industry," Empirica, Springer, vol. 33(1), pages 49-60, March.
    4. Bolton, R.N. & Lemo, K.N. & Verhoef, P.C., 2002. "The Theoretical Underpinnings of Customer Asset Management," ERIM Report Series Research in Management ERS-2002-80-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 Uni.
    5. Islam, Towhidul & Meade, Nigel, 2000. "Modelling diffusion and replacement," European Journal of Operational Research, Elsevier, vol. 125(3), pages 551-570, September.
    6. Brian W. Gould, 1996. "Consumer Promotion and Purchase Timing: The Case of Cheese," Wisconsin-Madison Agricultural and Applied Economics Staff Papers 396, Wisconsin-Madison Agricultural and Applied Economics Department.
    7. Van de Gucht, Linda M. & Moore, William T., 1998. "Predicting the duration and reversal probability of leveraged buyouts," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 299-315, October.
    8. Jeongwen Chiang & Ching-Fan Chung & Emily Cremers, 2001. "Promotions and the pattern of grocery shopping time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(7), pages 801-819.
    9. Jolley, William & Lee, Alvin & Mizerski, Richard & Sadeque, Saalem, 2013. "Permission email messages significantly increase gambler retention," Journal of Business Research, Elsevier, vol. 66(9), pages 1617-1622.
    10. Govert Bijwaard, 2010. "Regularity in individual shopping trips: implications for duration models in marketing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1931-1945.
    11. Brian GOULD, 1996. "Consumer Promotion And Purchase Timing: The Case Of Cheese," Staff Papers 396, University of Wisconsin Madison, AAE.
    12. Agarwal, Rajshree & Bayus, Barry L., 2002. "The Market Evolution and Sales Take-Off of Product Innovations," Working Papers 02-0104, University of Illinois at Urbana-Champaign, College of Business.
    13. Rust, Roland T. & Metters, Richard, 1996. "Mathematical models of service," European Journal of Operational Research, Elsevier, vol. 91(3), pages 427-439, June.
    14. Andrew Ching & Tülin Erdem & Michael Keane, 2009. "The price consideration model of brand choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 393-420, 04.
    15. Mercedes Esteban-Bravo & Jose M. Mugica & Jose M. Vidal-Sanz, 2004. "Optimal Duration Of Magazine Promotions," Business Economics Working Papers wb045417, Universidad Carlos III, Departamento de Economía de la Empresa.
    16. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics, Springer, vol. 5(2), pages 163-190, June.
    17. Vardit Landsman & Moshe Givon, 2010. "The diffusion of a new service: Combining service consideration and brand choice," Quantitative Marketing and Economics, Springer, vol. 8(1), pages 91-121, March.

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