Personalized Retail Promotions Through a Directed Acyclic Graph–Based Representation of Customer Preferences

Srikanth Jagabathula (), Dmitry Mitrofanov () and Gustavo Vulcano
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Srikanth Jagabathula: Department of Technology, Operations, and Statistics, Leonard N. Stern School of Business, New York University, New York, New York 10012
Dmitry Mitrofanov: Business Analytics Department, Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467

Operations Research, 2022, vol. 70, issue 2, 641-665

Abstract: We propose a back-to-back procedure for running personalized promotions in retail operations contexts, from the construction of a nonparametric choice model where customer preferences are represented by directed acyclic graphs (DAGs) to the design of such promotions. The source data include a history of purchases tagged by customer ID jointly with product availability and promotion data for a category of products. In each customer DAG, nodes represent products and directed edges represent the relative preference order between two products. Upon arrival to the store, a customer samples a full ranking of products within the category consistent with her DAG and purchases the most preferred option among the available ones. We describe the construction process to obtain the DAGs and explain how to mount a parametric, multinomial logit model (MNL) over them. We provide new bounds for the likelihood of a DAG and show how to conduct the MNL estimation. We test our model to predict purchases at the individual level on real retail data and characterize conditions under which it outperforms state-of-the-art benchmarks. Finally, we illustrate how to use the model to run personalized promotions. Our framework leads to significant revenue gains that make it an attractive candidate to be pursued in practice.

Keywords: Operations and Supply Chains; retailing; choice models; multinomial logit; promotion optimization; rank-based choice model; customized promotions (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:70:y:2022:i:2:p:641-665

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