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Modelling Retail Customer Behavior at Merrill Lynch

Author

Listed:
  • Donald G. Morrison

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Richard D. H. Chen

    (Marketing Evaluation Department, Merrill Lynch, One Liberty Plaza, New York, New York 10080)

  • Sandra L. Karpis

    (Marketing Evaluation Department, Merrill Lynch, One Liberty Plaza, New York, New York 10080)

  • Kathryn E. A. Britney

    (Marketing Evaluation Department, Merrill Lynch, One Liberty Plaza, New York, New York 10080)

Abstract

A two state Markov chain model is used to describe and forecast the over time behavior of the best retail customers at Merrill Lynch. This model has 4 behaviorally meaningful parameters which capture the effect of recently being a prime customer, the differing average commissions generated across customers and the exiting of some of these customers from the Merrill Lynch system. This model helps management to understand the dynamics of the prime customers' behavior. In particular, the forecasts generated by the model allow for better analyses of possible strategies for providing special services for these very good customers. The model which was developed with 1976–1979 data is validated against the actual 1980 behavior of Merrill Lynch customers.

Suggested Citation

  • Donald G. Morrison & Richard D. H. Chen & Sandra L. Karpis & Kathryn E. A. Britney, 1982. "Modelling Retail Customer Behavior at Merrill Lynch," Marketing Science, INFORMS, vol. 1(2), pages 123-141.
    • Handle: RePEc:inm:ormksc:v:1:y:1982:i:2:p:123-141
    DOI: 10.1287/mksc.1.2.123
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    Citations

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    Cited by:

    1. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    2. Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Ülengin, Burç, 2014. "Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model," European Journal of Operational Research, Elsevier, vol. 237(1), pages 278-288.
    3. Yeliz Ekinci & Füsun Ulengin & Nimet Uray, 2014. "Using customer lifetime value to plan optimal promotions," The Service Industries Journal, Taylor & Francis Journals, vol. 34(2), pages 103-122, January.
    4. Hans Buhl & Robert Klein & Johannes Kolb & Andrea Landherr, 2011. "CR 2 M—an approach for capacity control considering long-term effects on the value of a customer for the company," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 22(2), pages 187-204, December.
    5. Hans Buhl & Martin Gneiser & Julia Heidemann, 2009. "Ein modelltheoretischer Ansatz zur Planung von Investitionen in Kundenbeziehungen," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 20(2), pages 175-195, October.
    6. Audzeyeva, Alena & Summers, Barbara & Schenk-Hoppé, Klaus Reiner, 2012. "Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective," International Journal of Forecasting, Elsevier, vol. 28(2), pages 507-518.
    7. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.

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