Bayesian inference of arrival rate and substitution behavior from sales transaction data with stockouts

Published

Conference Paper

© 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. When an item goes out of stock, sales transaction data no longer reflect the original customer demand, since some customers leave with no purchase while others substitute alternative products for the one that was out of stock. Here we develop a Bayesian hierarchical model for inferring the underlying customer arrival rate and choice model from sales transaction data and the corresponding stock levels. The model uses a nonhomogeneous Poisson process to allow the arrival rate to vary throughout the day, and allows for a variety of choice models. Model parameters are inferred using a stochastic gradient MCMC algorithm that can scale to large transaction databases. We fit the model to data from a local bakery and show that it is able to make accurate out-of-sample predictions, and to provide actionable insight into lost cookie sales.

Full Text

Duke Authors

Cited Authors

  • Letham, B; Letham, LM; Rudin, C

Published Date

  • August 13, 2016

Published In

  • Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining

Volume / Issue

  • 13-17-August-2016 /

Start / End Page

  • 1695 - 1704

International Standard Book Number 13 (ISBN-13)

  • 9781450342322

Digital Object Identifier (DOI)

  • 10.1145/2939672.2939810

Citation Source

  • Scopus