Triplet Embeddings for Demand Estimation

55 Pages Posted: 20 May 2022 Last revised: 30 Oct 2023

See all articles by Lorenzo Magnolfi

Lorenzo Magnolfi

University of Wisconsin-Madison

Jonathon McClure

University of Wisconsin - Madison

Alan T. Sorensen

University of Wisconsin - Madison

Date Written: October 27, 2023

Abstract

We propose a method to augment conventional demand estimation approaches with crowd-sourced data on the product space. Our method obtains triplets data (“product A is closer to B than it is to C”) from an online survey to compute an embedding—i.e., a low-dimensional representation of the latent product space. The embedding can either (i) replace data on observed characteristics in mixed logit models, or (ii) provide pairwise product distances to discipline cross-elasticities in log-linear models. We illustrate both approaches by estimating demand for ready-to-eat cereals; the information contained in the embedding leads to more plausible substitution patterns and better fit.

Suggested Citation

Magnolfi, Lorenzo and McClure, Jonathon and Sorensen, Alan T., Triplet Embeddings for Demand Estimation (October 27, 2023). Available at SSRN: https://ssrn.com/abstract=4113399 or http://dx.doi.org/10.2139/ssrn.4113399

Lorenzo Magnolfi (Contact Author)

University of Wisconsin-Madison ( email )

1180 Observatory Drive
Madison, WI Wisconsin 53706
United States
6082628789 (Phone)

HOME PAGE: http://lorenzomagnolfi.com

Jonathon McClure

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Alan T. Sorensen

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

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