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A consumer purchasing model with learning and departure behaviour

Author

Listed:
  • C Wu

    (National Taiwan University of Science and Technology)

  • H-L Chen

    (Ming-Chuan University)

Abstract

The purpose of this paper is to extend the model of negative binominal distribution used in consumer purchasing models so as to incorporate the consumer's learning and departure behaviours. The regularity of interpurchase time and its unobserved heterogeneity are also included. Due to these extensions, this model can be used to determine during a given period how many purchases are made by an experienced or an inexperienced customer. This model also allows the determination of the probability that a customer with a given pattern of purchasing behaviour still remains, or has departed, at any time after k≥1 purchases are made. An illustration of the approach is conducted using consumer purchase data for tea. As assessed by comparing results with Theil's U, the integrated model developed gives the best results and shows that learning and departure are important factors which influence consumer's purchase behaviour, especially, when evaluating the behaviour of inexperienced customers.

Suggested Citation

  • C Wu & H-L Chen, 2000. "A consumer purchasing model with learning and departure behaviour," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 583-591, May.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:5:d:10.1057_palgrave.jors.2600903
    DOI: 10.1057/palgrave.jors.2600903
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    Citations

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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. Meade, Nigel & Islam, Towhidul, 2010. "Using copulas to model repeat purchase behaviour - An exploratory analysis via a case study," European Journal of Operational Research, Elsevier, vol. 200(3), pages 908-917, February.
    3. Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    4. Trinh, Giang, 2014. "Predicting variation in repertoire size with the NBD model," Australasian marketing journal, Elsevier, vol. 22(2), pages 111-116.
    5. Giang Trinh & Cam Rungie & Malcolm Wright & Carl Driesener & John Dawes, 2014. "Predicting future purchases with the Poisson log-normal model," Marketing Letters, Springer, vol. 25(2), pages 219-234, June.
    6. Ira Gerhardt & Barry L. Nelson, 2009. "Transforming Renewal Processes for Simulation of Nonstationary Arrival Processes," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 630-640, November.
    7. 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.
    8. Guang Yang & Xinwang Liu, 2018. "A commuter departure-time model based on cumulative prospect theory," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(2), pages 285-307, April.
    9. 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.

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