Skip to main content

Incorporating Competitive Promotional Information in Forecasting SKU Product Sales for Retailers”

Huang, T., Fildes, R., Soopramanien, D. (2012) Incorporating Competitive Promotional Information in Forecasting SKU Product Sales for Retailers”. In: 34th ISMS Marketing Science Conference, 7th - 9th June 2012, Boston, USA. (Unpublished) (doi:N/A) (The full text of this publication is not currently available from this repository.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://www.bu.edu/marketingscience2012/

Abstract

Sales forecasting at the UPC level is important for retailers to manage inventory. In this paper, we propose more effective methods to forecast retail UPC sales by incorporating competitive information including prices and promotions. The impact of these competitive marketing activities on the sales of the focal product has been extensively documented. However, competitive information has been surprisingly overlooked by previous studies in forecasting UPC sales, probably because of the high-dimensionality problem associated with the selection of variables. That is, each FMCG product category typically contains a large number of UPCs and is consequently associated with a large number of competitive explanatory variables. Under such a circumstance, time series models can easily become over-fitted and thus generate poor forecasting results. Our forecasting methods consist of two stages. At the first stage, we refine the competitive information. We identify the most relevant explanatory variables using variable selection methods, or alternatively, pool information across all variables using factor analysis to construct a small number of diffusion indexes. At the second stage, we specify the Autoregressive Distributed Lag (ADL) model following a general to specific modelling strategy with the identified most relevant competitive explanatory variables and the constructed diffusion indexes. We compare the forecasting performance of our proposed methods with the industrial practice method (benchmark model) and the ADL model specified exclusively with the price and promotion information of the focal product. The results show that our proposed methods generate substantially more accurate forecasts across a range of product categories. 1Corresponding

Item Type: Conference or workshop item (Paper)
DOI/Identification number: N/A
Uncontrolled keywords: Forecasting; Business analytics; OR in marketing; Retailing; Promotions; Competitive information
Subjects: H Social Sciences
Divisions: Faculties > Social Sciences > Kent Business School > Marketing
Depositing User: Tracey Pemble
Date Deposited: 08 Sep 2014 13:56 UTC
Last Modified: 29 May 2019 13:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/42851 (The current URI for this page, for reference purposes)
  • Depositors only (login required):