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

• Type: Article in International Scientific Journal
• Date: 2015

Authors

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

Journal

Title: Robotics and Computer-Integrated Manufacturing

Vol. 36

Pages: 93-100

ISSN: 0736-5845

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 44 Citations

Scopus - 70 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8