• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.7
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• pp.1321-1331
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• 2001

As the society becomes industrialized and urbanized, men are changed and the speed of change becomes faster and faster. The purpose of this study was to identify the consumer's value systems and clarify how it influence on clothing involvements and shopping orientations. The data were collected via a questionnaire from 423 students of Semyung University in Checheon and data were analyzed by frequency, Crobach's alpha, factor analysis, custer analysis, ANOVA, Duncan test, t-test and multiple regression. The results of this study were as follows: According to the value factors students were classified into four groups. Among four groups success pursuit group was the biggest(58.4%). The four groups showed different clothing involvements and shopping orientations. Shopping orientations were influenced by the demorgraphic factors, value systems and clothing involvements. Among seven sopping orientation, entertainment pursuit was the most influenced factor y three factors. Additionally value system, clothing involvements and shopping orientations were influenced by the demographic variables.

• Journal of Industrial Technology
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• v.44 no.1
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• pp.25-34
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• 2024

Customer identification for company is very valuable for direct marketing and increase of profit to target the population who are to become most profitable customer to the company based on target customer analysis and customer segmentation. Customer value analysis involves seeking the profitable groups of customers through analysis of customer's attributes. Data mining techniques can help to accomplish to extract or detect hidden customer values and behaviors from big data. The objective of this paper is to propose customer value analysis based on RFM (R: Recency, F: Frequency, M: Monetary) model to identify the profitable segments (top target customer) of customer based on customer' underlying characteristics. We use the case study of S-company (122 customers with 6639 transactions from 2017/09/01 to 2018/08/31) to show the procedure of customer value analysis based on RFM model. We show how we can make the scores of RFM attributes and segment customers. K-means is one of the most important technique in data mining. K-means is used for five group market segmentations based on valid index intra-cluster distance which is a popular and efficient data clustering method. Our experiments and simulation results show the 26 top target customers out of 122 customers. We also propose the product recommend system based on RFM model for efficient marketing strategy with high priority.