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Forecast Analysis for Sales in Large-Scale Retail Trade

Forecast Analysis for Sales in Large-Scale Retail Trade
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Author(s): Mirco Nanni (ISTI institute of CNR, Italy) and Laura Spinsanti (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
Copyright: 2010
Pages: 26
Source title: Data Mining in Public and Private Sectors: Organizational and Government Applications
Source Author(s)/Editor(s): Antti Syvajarvi (University of Lapland, Finland) and Jari Stenvall (Tampere University, Finland)
DOI: 10.4018/978-1-60566-906-9.ch012


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In large-scale retail trade, a very significant problem consists in analyzing the response of clients to product promotions. The aim of the project described in this work is the extraction of forecasting models able to estimate the volume of sales involving a product under promotion, together with a prediction of the risk of out of stock events, in which case the sales forecast should be considered potentially underestimated. Our approach consists in developing a multi-class classifier with ordinal classes (lower classes represent smaller numbers of items sold) as opposed to more traditional approaches that translate the problem to a binary-class classification. In order to do that, a proper discretization of sales values is studied, and ad hoc quality measures are provided in order to evaluate the accuracy of forecast models taking into consideration the order of classes. Finally, an overall system for end users is sketched, where the forecasting functionalities are organized in an integrated dashboard.

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