Price Optimization Under the Finite-Mixture Logit Model

Ruben van de Geer () and Arnoud V. den Boer ()
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Ruben van de Geer: Beat Research B.V., 1012 WX Amsterdam, Netherlands
Arnoud V. den Boer: Korteweg-de Vries Institute for Mathematics, University of Amsterdam, 1098 XG Amsterdam, Netherlands; Amsterdam Business School, University of Amsterdam, 1018 TV Amsterdam, Netherlands

Management Science, 2022, vol. 68, issue 10, 7480-7496

Abstract: We consider price optimization under the finite-mixture logit model. This model assumes that customers belong to one of a number of customer segments, where each customer segment chooses according to a multinomial logit model with segment-specific parameters. We reformulate the corresponding price optimization problem and develop a novel characterization. Leveraging this new characterization, we construct an algorithm that obtains prices at which the revenue is guaranteed to be at least ( 1 − ϵ ) times the maximum attainable revenue for any prespecified ϵ > 0 . Existing global optimization methods require exponential time in the number of products to obtain such a result, which practically means that the prices of only a handful of products can be optimized. The running time of our algorithm, however, is exponential in the number of customer segments and only polynomial in the number of products. This is of great practical value, because in applications, the number of products can be very large, whereas it has been found in various contexts that a low number of segments is sufficient to capture customer heterogeneity appropriately. The results of our numerical study show that (i) ignoring customer segmentation can be detrimental for the obtained revenue, (ii) heuristics for optimization can get stuck in local optima, and (iii) our algorithm runs fast on a broad range of problem instances.

Keywords: price optimization; finite-mixture logit model; fully polynomial-time approximation scheme (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:10:p:7480-7496

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