Nonparametric Joint Assortment and Price Choice Model

Srikanth Jagabathula () and Paat Rusmevichientong ()
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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

Management Science, 2017, vol. 63, issue 9, 3128-3145

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.

Keywords: nonparametric choice models; joint assortment and price optimization; EM algorithm; transaction data (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (25)

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