Customer segmentation in a large database of an online customized fashion business

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Pedro Quelhas Brito (Autor) FEP; Carlos Soares (Autor) FEUP; Almeida, S (Autor) Outra; Monte, A (Autor) Outra; Byvoet, M (Autor) Outra

Revista

Título: Robotics and Computer-Integrated Manufacturing

Vol. 36

Páginas: 93-100

ISSN: 0736-5845

Editora: Elsevier

Indexação

ISI Web of Knowledge - 44 Citações

Scopus - 70 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

Outras Informações

ID Authenticus: P-00G-DHA

DOI: 10.1016/j.rcim.2014.12.014

Abstract (EN): Data mining (DM) techniques have been used to solve marketing and manufacturing problems in the fashion industry. These approaches are expected to be particularly important for highly customized industries because the diversity of products sold makes it harder to find clear patterns of customer preferences. The goal of this project was to investigate two different data mining approaches for customer segmentation: clustering and subgroup discovery. The models obtained produced six market segments and 49 rules that allowed a better understanding of customer preferences in a highly customized fashion manufacturer/e-tailor. The scope and limitations of these clustering DM techniques will lead to further methodological refinements.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8