Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression

Ali Fallah Tehrani and Diane Ahrens

Journal of Retailing and Consumer Services, 2016, vol. 32, issue C, 131-138

Abstract: Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write off and increases the revenue. In fact, beyond of sales forecasting in general a crucial question remains whether a product may sell well or not. Assuming three classes as substantial, middle and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. Thereafter, we combine kernel machines with a probabilistic approach to empower the performance of kernel machines and eventually to make use of it to predicting the number of sales. The proposed approach is more robust to outliers (in the case of highly sold products) and in addition uses prior knowledge, hence it serves more reliable results. In order to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.

Keywords: Ordinal logistic regression; Fashion products; Sales forecasting; Kernel machines (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:32:y:2016:i:c:p:131-138

DOI: 10.1016/j.jretconser.2016.05.008

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