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

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. We investigate customer segmentation in highly customized fashion industries.Two data mining methods are used, clustering and subgroup discovery.The segments obtained enabled a better understanding of customer preferences.Different approaches provide complementary perspectives.Lines for further methodological refinements were identified.