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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.
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    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
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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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