Demand estimation from sales transaction data: practical extensions

Norbert Remenyi () and Xiaodong Luo ()
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Norbert Remenyi: Sabre, 3150 Sabre Dr.
Xiaodong Luo: Shenzhen Research Institute of Big Data, Shenzhen

A chapter in Artificial Intelligence and Machine Learning in the Travel Industry, 2023, pp 67-91 from Springer

Abstract: 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.

Keywords: Demand estimation; Demand untruncation; Multinomial logit model; EM algorithm; MM algorithm; Frank–; Wolfe method; Revenue management (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-25456-7_6

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DOI: 10.1007/978-3-031-25456-7_6

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