A Dynamic Clustering Approach to Data-Driven Assortment Personalization

Fernando Bernstein, Sajad Modaresi, Denis Sauré (2019) A Dynamic Clustering Approach to Data-Driven Assortment Personalization. Management Science 65 (5) 2095-2115

44 Pages Posted: 9 Jun 2017 Last revised: 28 Apr 2021

See all articles by Fernando Bernstein

Fernando Bernstein

Duke University

Sajad Modaresi

University of North Carolina at Chapel Hill - Kenan-Flagler Business School

Denis Saure

University of Chile - Industrial Engineering

Date Written: August 3, 2018

Abstract

We consider an online retailer facing heterogeneous customers with initially unknown product preferences. Customers are characterized by a diverse set of demographic and transactional attributes. The retailer can personalize the customers' assortment offerings based on available profile information to maximize cumulative revenue. To that end, the retailer must estimate customer preferences by observing transaction data. This, however, may require a considerable amount of data and time given the broad range of customer profiles and large number of products available. At the same time, the retailer can aggregate (pool) purchasing information among customers with similar product preferences to expedite the learning process. We propose a dynamic clustering policy that estimates customer preferences by adaptively adjusting customer segments (clusters of customers with similar preferences) as more transaction information becomes available. We test the proposed approach with a case study based on a dataset from a large Chilean retailer. The case study suggests that the benefits of the dynamic clustering policy under the MNL model can be substantial and result (on average) in more than 37% additional transactions compared to a data-intensive policy that treats customers independently and in more than 27% additional transactions compared to a linear-utility policy that assumes that product mean utilities are linear functions of available customer attributes. We support the insights derived from the numerical experiments by analytically characterizing settings in which pooling transaction information is beneficial for the retailer, in a simplified version of the problem. We also show that there are diminishing marginal returns to pooling information from an increasing number of customers.

Keywords: Data-Driven Assortment Planning, Personalization, Dynamic Clustering, Multi-Armed Bandit

Suggested Citation

Bernstein, Fernando and Modaresi, Sajad and Saure, Denis, A Dynamic Clustering Approach to Data-Driven Assortment Personalization (August 3, 2018). Fernando Bernstein, Sajad Modaresi, Denis Sauré (2019) A Dynamic Clustering Approach to Data-Driven Assortment Personalization. Management Science 65 (5) 2095-2115, Available at SSRN: https://ssrn.com/abstract=2983207 or http://dx.doi.org/10.2139/ssrn.2983207

Fernando Bernstein

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Sajad Modaresi (Contact Author)

University of North Carolina at Chapel Hill - Kenan-Flagler Business School ( email )

300 Kenan Drive
Chapel Hill, NC 27599
United States

Denis Saure

University of Chile - Industrial Engineering ( email )

República 701, Santiago
Chile

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