Demand Estimation from Sales Transaction Data – Practical Extensions
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In this paper we discuss some of the practical limitations of the standard choice-based demand models used in the literature to estimate demand from sales transaction data. We present interesting modifications and extensions of the models and discuss possible data-preprocessing and solution techniques which can be 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 (Vulcano et al. (2012)) for non-homogeneous product sets, and we develop two iterative optimization algorithms which can handle much of the extensions discussed in the paper.
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