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Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Ma, Shaohui, Fildes, Robert, Huang, Tao (2016) Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information. European Journal of Operational Research (ABS 4), 249 (1). pp. 245-257. ISSN 0377-2217. (doi:10.1016/j.ejor.2015.08.029) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/j.ejor.2015.08.029

Abstract

In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts. But no research has yet put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra- and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 12.6 percent over the model using only the SKU's own predictors. But of the improvements achieved, 95 percent of it comes from the intra-category information, and only 5 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.

Item Type: Article
DOI/Identification number: 10.1016/j.ejor.2015.08.029
Uncontrolled keywords: KW - Analytics KW - OR in marketing KW - Forecasting KW - Retailing KW - Promotions
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD29 Operational Research - Applications
H Social Sciences > HF Commerce > HF5351 Business
H Social Sciences > HF Commerce > HF5415 Marketing
Divisions: Faculties > Social Sciences > Kent Business School
Faculties > Social Sciences > Kent Business School > Marketing
Depositing User: Tao Huang
Date Deposited: 02 Oct 2015 11:25 UTC
Last Modified: 29 May 2019 16:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50753 (The current URI for this page, for reference purposes)
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