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Demand estimation from sales transaction data: practical extensions

Author

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  • Norbert Remenyi

    (Sabre)

  • Xiaodong Luo

    (Shenzhen Research Institute of Big Data)

Abstract

In this paper, we discuss practical limitations of the standard choice-based demand models used in the literature to estimate demand from sales transaction data. We present modifications and extensions of the models and discuss data preprocessing and solution techniques which are useful for practitioners dealing with sales transaction data. Among these, we present an algorithm to split sales transaction data observed under partial availability, we extend a popular Expectation Maximization (EM) algorithm for non-homogeneous product sets, and we develop two iterative optimization algorithms which can handle much of the extensions discussed in the paper.

Suggested Citation

  • Norbert Remenyi & Xiaodong Luo, 2021. "Demand estimation from sales transaction data: practical extensions," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 276-300, June.
    • Handle: RePEc:pal:jorapm:v:20:y:2021:i:3:d:10.1057_s41272-021-00312-3
    DOI: 10.1057/s41272-021-00312-3
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    References listed on IDEAS

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    1. Gustavo Vulcano & Garrett van Ryzin & Richard Ratliff, 2012. "Estimating Primary Demand for Substitutable Products from Sales Transaction Data," Operations Research, INFORMS, vol. 60(2), pages 313-334, April.
    2. Hunter D.R. & Lange K., 2004. "A Tutorial on MM Algorithms," The American Statistician, American Statistical Association, vol. 58, pages 30-37, February.
    3. Guillermo Gallego & Richard Ratliff & Sergey Shebalov, 2015. "A General Attraction Model and Sales-Based Linear Program for Network Revenue Management Under Customer Choice," Operations Research, INFORMS, vol. 63(1), pages 212-232, February.
    4. 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.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    6. Marguerite Frank & Philip Wolfe, 1956. "An algorithm for quadratic programming," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 95-110, March.
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    Cited by:

    1. Resul Aydemir & Mehmet Melih Değirmenci & Abdullah Bilgin, 2023. "Estimation of passenger sell-up rates in airline revenue management by considering the effect of fare class availability," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 501-513, December.

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