Work

Demand Estimation from Sales Transaction Data -- Practical Extensions

Year: 2020

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2006.11450

Authors: Remenyi, Norbert, Luo, Xiaodong

Institutions: Sabre (United States), Shenzhen Research Institute of Big Data

Language: English

Topic: Consumer Market Behavior and Pricing

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W3036617659

Cites: 13

Cited by:

Related to: 10