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Revenue management under general discrete choice model of consumer behavior

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  • Kalyan Talluri
  • Garrett van Ryzin

Abstract

Customer choice behavior, such as 'buy-up' and 'buy-down', is an important phe-nomenon in a wide range of industries. Yet there are few models or methodologies available to exploit this phenomenon within yield management systems. We make some progress on filling this void. Specifically, we develop a model of yield management in which the buyers' behavior is modeled explicitly using a multi-nomial logit model of demand. The control problem is to decide which subset of fare classes to offer at each point in time. The set of open fare classes then affects the purchase probabilities for each class. We formulate a dynamic program to determine the optimal control policy and show that it reduces to a dynamic nested allocation policy. Thus, the optimal choice-based policy can easily be implemented in reservation systems that use nested allocation controls. We also develop an estimation procedure for our model based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results show that this combined optimization -estimation approach may significantly improve revenue performance relative to traditional leg-based models that do not account for choice behavior.

Suggested Citation

  • Kalyan Talluri & Garrett van Ryzin, 2000. "Revenue management under general discrete choice model of consumer behavior," Economics Working Papers 533, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2001.
    • Handle: RePEc:upf:upfgen:533
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    References listed on IDEAS

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    1. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
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    Cited by:

    1. Kalyan Talluri, 2003. "On equilibria in duopolies with finite strategy spaces," Economics Working Papers 701, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    3. Uzma Mushtaque & Jennifer A. Pazour, 2020. "Random Utility Models with Cardinality Context Effects for Online Subscription Service Platforms," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(4), pages 276-290, August.

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    More about this item

    Keywords

    yield management; revenue management; discrete choice theory; airlines; dynamic programming;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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