在現今大數據的時代，由於累積龐大的客戶資料，公司企業的主要方向已從商品經營轉為客戶經營。從客戶資料中尋找高價值的客戶做積極經營，也變為現今企業常使用的策略。除了找尋高價值客戶外，若欲提高商業的利潤，企業第一個想到的策略往往是開發新客戶，但其實舊客戶的維繫更是我們應該深究的方法，因為開發一個新客戶的成本等於維繫五個舊客戶的成本，維繫好舊客戶關係更能帶來效益。本論文將客戶資料區分新舊客戶後，將舊客戶做分類，並且計算其RFM價值分數，再透過關聯規則分析、決策樹、集群分析等方式去找出舊客戶分類與客戶價值之間的關聯性，並使用一組實際資料作應用分析。 While the age of big data is coming, the new strategy of commercial companies has been switched from commodity management to customer management since many companies have huge amounts of customer data. In additon, identification of high-value customers based on customer data is also a common strategy adopted by these companies, with the consideration of increasing profits. Many companies consider exploring new customers the first priority, but the maintenance of old customers is often more important because the cost of exploring a new customer is five times greater than that of maintaining old customers. In this study, the customer data will be divided into two major sections, old and new customers. We then classify old customers into four group and calculate their RFM score. Using association rule learning, decision tree and cluster analysis, we try to find the relationships between old customers and customer values. A real-world data application is also discussed in our study.
