Applying Data Mining Technology to Retail Industry – A Case Study on Home Furnishing Stores

• Main Authors: WU, TAI-HSUN, 吳岱壎
• Other Authors: WU, TAI-HSI
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/15038341439499567249

碩士 === 國立臺北大學 === 企業管理學系碩士在職專班 === 100 === With the development of computer hardware, data storage capacity and capability are fast-growing; organizations not only be able to effectively store, manage data, it also need to find out valuable information in the big data, and to help assist in making the next critical decision. Retail industries usually have contact with the end users, through data mining techniques, will be able to gather a lot of customer’s information, transaction records, buying patterns, rules, or trends, and these information will provide more convincing marketing strategy recommendations. In this study, a home furnishing store, for example, through its joint co-branded cards, it will provide customer’s information and transaction records, using standardized the RFM consumer behavior variables and weights of the method of assessment of RFM, to create customer value in line with the retail characteristics of quantitative analysis through the AHP model. Through the establishment of quantitative models of customer value to the customer cluster on the basis of customer grading system to help businesses master a loyal customer base, the resources to do the most effective configuration, and a higher response rate. In the study, the K-Means method, the customer cluster analysis standardized the RFM variables to the case company is divided into three major groups; "loyal customers", "target customers", "lost customers," By the quantitative model of customer value to take the same proportion the number of cross-analysis, we found that up to 92% similarity with the two methods. In this study, based on the retail industry characteristics, through data mining of association rules and sequential patterns analysis, to explore suitable product mix for the customer, find out which product mix most frequently purchased together, which goods exist to purchase the order of relations, and apply the results of cross-selling and up-selling planning. Due to diverse numbers of product items in retail sales, to effectively find out the valuable association rules greatly depends on the appropriate category structure. In this study, according to the case company being classified, classification by product association rule mining, and to increase the fine classification association rules. In the selection of association rules, probability, and importance, the indicators for analysis, to provide reference to the product mix and marketing activities planning.