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A Probablistic Market Model of Purchase Timing and Brand Selection

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  • Jerome Herniter

    (Boston University)

Abstract

A stochastic model of consumer purchase behavior for frequently purchased, low cost products is developed. Both brand selection and purchase timing are incorporated in the model; a first-order Markov process is used to describe brand selection, and Erlang density functions are used to describe time between purchases. The market's behavior is obtained by describing the individual consumer's behavior and then aggregating over consumers. The model's predictions of various aggregate purchase timing statistics and repeat purchase sequences are empirically verified.

Suggested Citation

  • Jerome Herniter, 1971. "A Probablistic Market Model of Purchase Timing and Brand Selection," Management Science, INFORMS, vol. 18(4-Part-II), pages 102-113, December.
    • Handle: RePEc:inm:ormnsc:v:18:y:1971:i:4-part-ii:p:p102-p113
    DOI: 10.1287/mnsc.18.4.P102
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    Cited by:

    1. Trinh, Giang & Wright, Malcolm J., 2022. "Predicting future consumer purchases in grocery retailing with the condensed Poisson lognormal model," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    2. Gould, Brian W., 1996. "Consumer Promotion And Purchase Timing: The Case Of Cheese," Staff Papers 12664, University of Wisconsin-Madison, Department of Agricultural and Applied Economics.
    3. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
    4. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    5. Peter S. Fader & Bruce G. S. Hardie & Chun-Yao Huang, 2004. "A Dynamic Changepoint Model for New Product Sales Forecasting," Marketing Science, INFORMS, vol. 23(1), pages 50-65, October.
    6. Juha Karvanen & Ari Rantanen & Lasse Luoma, 2014. "Survey data and Bayesian analysis: a cost-efficient way to estimate customer equity," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 305-329, September.
    7. S Reader, 1993. "Unobserved Heterogeneity in Dynamic Discrete Choice Models," Environment and Planning A, , vol. 25(4), pages 495-519, April.
    8. Bruce G. S. Hardie & Peter S. Fader & Robert Zeithammer, 2003. "Forecasting new product trial in a controlled test market environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 391-410.
    9. Schweidel, David A. & Fader, Peter S., 2009. "Dynamic changepoints revisited: An evolving process model of new product sales," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 119-124.
    10. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    11. Boylan, I. E. & Johnston, F. R., 1996. "Variance laws for inventory management," International Journal of Production Economics, Elsevier, vol. 45(1-3), pages 343-352, August.

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