Using data mining to analyse fashion consumers’ preferences from a cross-national perspective

Abstract

The purpose of this study is twofold: (1) to cluster the respondents into three consumer groups – fashion innovator, fashion follower and laggard and (2) to extract association rules from the data set in order to understand consumers’ preferences. A data-mining method was employed to analyse considerable amount of data collected from four cities as well as to understand the complexity of the diffusion process of multiple apparel products. According to the results of the present study, style was not an important factor for the fashion leaders to purchase socks in Toronto, Hangzhou and Johor Bahru. In terms of t-shirts and evening dresses/suits, 53% and 51% of fashion laggards in China had shown their strong preferences for fit and comfort, respectively. Additionally, 60% of the fashion leaders in Canada had shown a strong preference for fit and style of t-shirts. Although this study is exploratory in nature, we believe that data mining has great potential for investigating fashion diffusion of innovativeness, and more replication of this type of research will be worthwhile and meaningful.

• data mining
• diffusion of fashion innovation
• cross-national study
• product attributes
• consumer preferences