SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements

TL;DR

SHOPPER is a probabilistic model that captures customer choice behavior, including interactions between products, enabling accurate predictions and insights into product substitutes and complements from large-scale shopping data.

Contribution

It introduces a novel interpretable probabilistic model with an efficient inference algorithm for analyzing product interactions in shopping data.

Findings

Accurately predicts customer choices under price changes.

Identifies complementary and substitutable product pairs.

Analyzes large-scale grocery store data effectively.

Abstract

We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimate these forces from large-scale data, and we analyze a large dataset from a major chain grocery store. We are interested in answering counterfactual queries about changes in prices. We found that SHOPPER provides accurate predictions even under price interventions, and that it helps identify complementary and substitutable pairs of products.