Triplet Embeddings for Demand Estimation

Magnolfi, Lorenzo; McClure, Jonathon; Sorensen, Alan

doi:10.1257/mic.20220248

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 loglinear 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.

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Triplet Embeddings for Demand Estimation

Abstract

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