Detecting Customer Trends for Optimal Promotion Targeting

48 Pages Posted: 12 Sep 2018 Last revised: 13 Aug 2019

See all articles by Lennart Baardman

Lennart Baardman

University of Michigan, Stephen M. Ross School of Business

Setareh Borjian Boroujeni

Oracle Retail Science

Tamar Cohen-Hillel

Massachusetts Institute of Technology (MIT) - Operations Research Center

Kiran Panchamgam

Oracle Retail Science

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Date Written: August 30, 2018

Abstract

Problem Definition: Retailers have become increasingly interested in personalizing their products and services such as promotions. For this, we need new personalized demand models. Unfortunately, social data is not available to many retailers for cost and/or privacy issues. Thus, we focus on the problem of detecting customer relationships from transactional data, and using them to target promotions to the right customers.

Academic/Practical Relevance: From an academic point of view, this paper solves the novel problem of jointly detecting customer trends and using them for optimal promotion targeting. Notably, we estimate the causal customer-to-customer trend effect solely from transactional data, and target promotions for multiple items and time periods. In practice, we provide a new tool for Oracle Retail clients that personalizes promotions.

Methodology: We develop a novel probabilistic demand model distinguishing between a base purchase probability, capturing factors such as price and seasonality, and a customer trend probability, capturing customer-to-customer trend effects. The estimation procedure is based on regularized bounded variables least squares and instrumental variable methods. The resulting customer trend estimates feed into the dynamic promotion targeting optimization problem, formulated as a non-linear mixed-integer optimization model. Though it is NP-hard, we propose an adaptive greedy algorithm.

Results: We prove our customer-to-customer trend estimates are statistically consistent, and the adaptive greedy algorithm is provably good. Having access to Oracle Retail fashion client data, we show that our demand model reduces the WMAPE by 11% on average. Also, we provide evidence of the causality of our estimates. Finally, we demonstrate that the optimal policy increases profits by 3-11%.

Managerial Implications: The demand model with customer trend and the optimization model for targeted promotions form a decision support tool for promotion planning. Next to general planning, it also helps to find important customers and target them to generate additional sales.

Keywords: Retail Operations, Demand Modeling, Instrumental Variables, Promotion Optimization, Promotion Targeting, Approximation Algorithms

Suggested Citation

Baardman, Lennart and Borjian Boroujeni, Setareh and Cohen-Hillel, Tamar and Panchamgam, Kiran and Perakis, Georgia, Detecting Customer Trends for Optimal Promotion Targeting (August 30, 2018). Available at SSRN: https://ssrn.com/abstract=3242529 or http://dx.doi.org/10.2139/ssrn.3242529

Lennart Baardman

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Setareh Borjian Boroujeni

Oracle Retail Science ( email )

Burlington, MA 01803
United States

Tamar Cohen-Hillel

Massachusetts Institute of Technology (MIT) - Operations Research Center ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Kiran Panchamgam

Oracle Retail Science ( email )

Burlington, MA Massachusetts 01803
United States

Georgia Perakis (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-565
Cambridge, MA 02142
United States

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