Personalized Retail Promotions through a DAG-based Representation of Customer Preferences

85 Pages Posted: 4 Oct 2018 Last revised: 16 Sep 2020

See all articles by Srikanth Jagabathula

Srikanth Jagabathula

New York University (NYU) - Department of Information, Operations, and Management Sciences

Dmitry Mitrofanov

Boston College, Carroll School of Management

Gustavo Vulcano

Universidad Torcuato Di Tella - School of Business

Date Written: September 15, 2020

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 includes a history of purchases tagged by customer id, and 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 DAG construction process and explain how to mount a parametric, multinomial logit model (MNL) over it. 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 verify that it outperforms state-of-the-art benchmarks. Finally, we illustrate how to use it to run personalized promotions. Our framework leads to significant revenue gains over the sample data that make it an attractive candidate to be tested in practice.

Keywords: retailing, choice models, multinomial logit, promotion optimization, rank-based choice model

Suggested Citation

Jagabathula, Srikanth and Mitrofanov, Dmitry and Vulcano, Gustavo, Personalized Retail Promotions through a DAG-based Representation of Customer Preferences (September 15, 2020). Available at SSRN: https://ssrn.com/abstract=3258700 or http://dx.doi.org/10.2139/ssrn.3258700

Srikanth Jagabathula

New York University (NYU) - Department of Information, Operations, and Management Sciences ( email )

44 West Fourth Street
New York, NY 10012
United States

Dmitry Mitrofanov (Contact Author)

Boston College, Carroll School of Management

257 Beacon Street
Chestnut Hill, MA 02467
United States

Gustavo Vulcano

Universidad Torcuato Di Tella - School of Business ( email )

Avda Figueroa Alcorta 7350
Buenos Aires, CABA 1428
Argentina

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