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Nonparametric Joint Assortment and Price Choice Model

Author

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  • Srikanth Jagabathula

    (Stern School of Business, New York University, New York, New York 10012)

  • Paat Rusmevichientong

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

Abstract

The selection of products and prices offered by a firm significantly impacts its profits. Existing approaches do not provide flexible models that capture the joint effect of assortment and price. We propose a nonparametric framework in which each customer is represented by a particular price threshold and a particular preference list over the alternatives. The customers follow a two-stage choice process; they consider the set of products with prices less than the threshold and choose the most preferred product from the set considered. We develop a tractable nonparametric expectation maximization (EM) algorithm to fit the model to the aggregate transaction data and design an efficient algorithm to determine the profit-maximizing combination of offer set and price. We also identify classes of pricing structures of increasing complexity, which determine the computational complexity of the estimation and decision problems. Our pricing structures are naturally expressed as business constraints, allowing a manager to trade off pricing flexibility with computational burden.

Suggested Citation

  • Srikanth Jagabathula & Paat Rusmevichientong, 2017. "Nonparametric Joint Assortment and Price Choice Model," Management Science, INFORMS, vol. 63(9), pages 3128-3145, September.
    • Handle: RePEc:inm:ormnsc:v:63:y:2017:i:9:p:3128-3145
    DOI: 10.1287/mnsc.2016.2491
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    References listed on IDEAS

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    18. Meng Qi & Ho‐Yin Mak & Zuo‐Jun Max Shen, 2020. "Data‐driven research in retail operations—A review," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 595-616, December.
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    20. Frank Huettner & Tamer Boyacı & Yalçın Akçay, 2019. "Consumer Choice Under Limited Attention When Alternatives Have Different Information Costs," Operations Research, INFORMS, vol. 67(3), pages 671-699, May.
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    22. Barbier, Thibault & Anjos, Miguel F. & Cirinei, Fabien & Savard, Gilles, 2020. "Product-closing approximation for ranking-based choice network revenue management," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1002-1017.
    23. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.

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