Applying Data Mining to Customer Relationship Management -A study on Construction Materials Retail Business

• Main Authors: Min-Hui Shih, 施旻慧
• Other Authors: Jau-Shin Hon
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/9ex4a5

碩士 === 東海大學 === 工業工程與經營資訊學系 === 101 === As retailers face dynamic comptetion and continuous consumer’s demands for products consumption modes of diversification. Therefore, Customer Relationship Management (CRM) is gaining more and more attention in the enterprise. The traditional ways of data processing is out of date; and have been replaced with Data Mining which is used to. not only extrapolate data but also reveals the meaning behind the raw data collected in such a manner the information can be interpreted and utilized. This research focuses on the study of building materials retail business, from the perspective of the manufacters who purchase of the construction materials and the customers who need to do the interior decorating. Attempt to understand the Pareto Principle whether through a combination of the 80/20 rule and association rules to enhance the proportion of loyal customers. As well as through the association rule to analysis the basic information of customer and the transaction data to understand the customer’s buying behavior and provide customers tailor-made products and services. And establish a good interaction relationship with customers. First, we will be combining the concept of data mining and customer relationship management in building the main structure of this study. Transforming the raw data obtained through data switching and data identification into materials which are acceptable by both analysts and the system. Using the Two-Steps Cluster to categorize the customer and then grouping of 80/20 into two groups and the customer churn. Finally, through the Association Rule in data mining, we will calculate the most significant rule for this company by Apriori algorithm. Meanwhile we also provide the explanation of the rule, suggestions and solutions as well as derive an optimal marketing strategy.