Retail forecasting: research and practice

Robert Fildes, Shaohui Ma and Stephan Kolassa

MPRA Paper from University Library of Munich, Germany

Abstract: This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

Keywords: retail forecasting; product hierarchies; big data; marketing analytics; user-generated web content; new products; comparative accuracy; forecasting practice (search for similar items in EconPapers)
JEL-codes: L81 M20 M30 (search for similar items in EconPapers)
Date: 2019-10
New Economics Papers: this item is included in nep-big, nep-for and nep-mkt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

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