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The impact of customer behavior models on revenue management systems

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  • Shadi Azadeh
  • M. Hosseinalifam
  • G. Savard

Abstract

Revenue management (RM) can be considered an application of operations research in the transportation industry. For these service companies, it is a difficult task to adjust supply and demand. In order to maximize revenue, RM systems display demand behavior by using historical data. Usually, parametric methods are applied to estimate the probability of choosing a product at a given time. However, parameter estimation becomes challenging when we need to deal with constrained data. In this research, we evaluate the performance of a revenue management system when a non-parametric method for choice probability estimation is chosen. The outcomes of this method have been compared to the total expected revenue using synthetic data. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Shadi Azadeh & M. Hosseinalifam & G. Savard, 2015. "The impact of customer behavior models on revenue management systems," Computational Management Science, Springer, vol. 12(1), pages 99-109, January.
    • Handle: RePEc:spr:comgts:v:12:y:2015:i:1:p:99-109
    DOI: 10.1007/s10287-014-0204-z
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    References listed on IDEAS

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    1. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
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    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
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    5. Juan M. Chaneton & Gustavo Vulcano, 2011. "Computing Bid Prices for Revenue Management Under Customer Choice Behavior," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 452-470, October.
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    Cited by:

    1. Una McMahon-Beattie & Mairead McEntee & Robert McKenna & Ian Yeoman & Lynsey Hollywood, 2016. "Revenue management, pricing and the consumer," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 299-305, July.
    2. Catherine Cleophas & Daniel Kadatz & Sebastian Vock, 2017. "Resilient revenue management: a literature survey of recent theoretical advances," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(5), pages 483-498, October.
    3. Johannes F. Jörg & Catherine Cleophas, 2022. "Nonparametric estimation of customer segments from censored sales panel data," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(4), pages 393-417, August.

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