Data Aggregation and Demand Prediction

42 Pages Posted: 30 Jun 2019 Last revised: 14 May 2021

See all articles by Maxime C. Cohen

Maxime C. Cohen

Desautels Faculty of Management, McGill University

Renyu (Philip) Zhang

New York University Shanghai; the Chinese University of Hong Kong

Kevin Jiao

New York University (NYU) - Leonard N. Stern School of Business

Date Written: June 28, 2019

Abstract

We study how retailers can use data aggregation and clustering to improve demand prediction. High accuracy in demand prediction allows retailers to more effectively manage their inventory and mitigate stock-outs and excess supply. A typical retail setting involves predicting demand for hundreds of items simultaneously. Although some items have a large amount of historical data, others were recently introduced, and thus, transaction data could be scarce. A common approach is to cluster several items and estimate a joint model at the cluster level. In this vein, one can estimate some model parameters by aggregating the data from several items and other parameters at the individual-item level. We propose a practical method referred to as data aggregation with clustering (DAC), which balances the tradeoff between data aggregation and model flexibility. DAC allows us to predict demand while optimally identifying the features that should be estimated at the (i) item, (ii) cluster, and (iii) aggregate levels. We show that the DAC algorithm yields a consistent estimate, along with improved asymptotic properties relative to the decentralized method, which estimates a different model for each item. Using both simulated and real data, we illustrate DAC's improvement in prediction accuracy relative to common benchmarks. Interestingly, the DAC algorithm has theoretical and practical advantages and helps retailers uncover meaningful managerial insights.

Keywords: Retail analytics, demand prediction, data aggregation, clustering

Suggested Citation

Cohen, Maxime C. and Zhang, Renyu and Jiao, Kevin, Data Aggregation and Demand Prediction (June 28, 2019). Available at SSRN: https://ssrn.com/abstract=3411653 or http://dx.doi.org/10.2139/ssrn.3411653

Maxime C. Cohen (Contact Author)

Desautels Faculty of Management, McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Renyu Zhang

New York University Shanghai ( email )

1555 Century Avenue
Shanghai, 200122
China
86-21-20595135 (Phone)

HOME PAGE: http://rphilipzhang.github.io/rphilipzhang/index.html

the Chinese University of Hong Kong ( email )

Shatin, N.T.
Hong Kong, Hong Kong
China

HOME PAGE: http://rphilipzhang.github.io/rphilipzhang/index.html

Kevin Jiao

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 W. 4th St
Suite 9-160
New York, NY 10012
United States

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