Title: Knowledge discovery in databases: an application to market segmentation in retail supermarkets

Authors: Kellen Dayelle Endler; Cassius Tadeu Scarpin; Maria Teresinha Arns Steiner; Tamires Almeida Sfeir; Claudimar Pereira da Veiga

Addresses: Universidade Federal do Paraná, Av. Pref. Lothário Meissner, 3400, Jardim Botânico, Curitiba, Brazil ' Universidade Federal do Paraná, Av. Pref. Lothário Meissner, 3400, Jardim Botânico, Curitiba, Brazil ' Pontifical Catholic University of Paraná, R. Imac. Conceição, 1155 – Prado Velho, Curitiba – PR, 80215-901, Brazil ' Federal University of Paraná – UFPR, Av. Pref. Lothário Meissner, 3400, Jardim Botânico, Curitiba, Brazil ' Federal University of Paraná – UFPR, Av. Pref. Lothário Meissner, 3400, Jardim Botânico, Curitiba, Brazil

Abstract: The purpose of this article is to present a methodology based on the extraction process of knowledge discovery in databases (KDD) to predict the expenditure of different customer profiles, considering their characteristics, and the type of store they would buy from, in one of the largest retail chains in the Brazilian supermarket and hypermarket segment. These stores have different characteristics, such as physical size, product assortment and customer profile. This heterogeneity in terms of commercial offers implies a desire for consumption by customers that differs from store to store, depending on how their preferences are met. The proposed methodology was applied to a real marketing case based in a business-to-consumer (B2C) environment to aid retailers during the segmentation process. The results show that it is possible to highlight relationships between the data that enabled the prediction of customers' consumption, which can contribute towards generating useful information to retail businesses.

Keywords: knowledge discovery in databases; KDD; data mining; market segmentation; retail; principal component analysis; PCA; cluster analysis; multiple linear regression; MLR; artificial neural network; ANN.

DOI: 10.1504/IJBIDM.2023.129878

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.3, pp.310 - 332

Received: 18 May 2021
Accepted: 10 Aug 2021
Published online: 03 Apr 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article