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

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  • Francisco J. R. Ruiz
  • Susan Athey
  • David M. Blei

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.

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  • Francisco J. R. Ruiz & Susan Athey & David M. Blei, 2017. "SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements," Papers 1711.03560, arXiv.org, revised Jun 2019.
    • Handle: RePEc:arx:papers:1711.03560
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    Cited by:

    1. Martin O'Connell & Pierre Dubois & Rachel Griffith, 2022. "The Use of Scanner Data for Economics Research," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 723-745, August.
    2. Yu Xia & Ali Arian & Sriram Narayanamoorthy & Joshua Mabry, 2023. "RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation," Papers 2312.14095, arXiv.org.
    3. Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 64-67, May.

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