銀行財富管理客戶貢獻分群機制之建立

本論文以銀行業財富管理客戶為研究對象，運用RFM (Recency, Frequency, Monetary) 模型以及貢獻度分析，並使用IBM SPSS Modeler中的兩階段 (Two Step) 分群方法針對活躍性較高之顧客進行集群分析。本研究首先根據顧客交易資料計算出個別顧客之RFM數值，並將RFM中之M (購買金額) 以客戶貢獻度取代。在應用兩階段分群法的輸入變數部分，除了RFM數值和RFM總分外，本研究亦加入每筆申購之平均金額、定期定額比例與顧客之年齡。根據分群結果顯示各群顧客分別呈現出不同的交易行為模式；論文中除了對各群顧客交易行為進行說明外，也嘗試針對各集群之間的差異進行比較並予以命名。本研究所提出之分群研究程序，除了可以準確的評估與分析客戶的交易特性與貢獻度外，亦可供實務上進行定期檢視與比較分析客戶貢獻度之參考。

關鍵字

RFM模型；兩階段分群；客戶貢獻度；財富管理

並列摘要

In this thesis, the information about the wealth management customers of a bank was analyzed by making use of RFM (Recency, Frequency, Monetary) and profitability analysis, and active customers were clustered by applying Two Step cluster method in IBM SPSS Modeler. First, the RFM values of individual customers were derived from transaction data and M value (purchase amount) was replaced by customers’ profitability. Then the RFM values, composite RFM score, average purchase amount, percentage of periodic purchase, and age were assigned as input variables for SPSS’s Two Step clustering. The results show that there were five groups (i.e. clusters) of customers with different behavior patterns being successfully identified. Besides illustrating the transaction behaviors of each group, this research compared the dissimilarity among clusters and suggested cluster names. The proposed clustering procedure can be used to correctly evaluate and analyze customer transaction features and contributions, as well as for empirical analysis and regular review on profitability of customers.

並列關鍵字

RFM Model ； Two Step Clustering ； Customer Profitability

參考文獻

[4] Chang, H. H. and Tsay, S. F., "Integrating of SOM and K-Mean in data mining clustering: an empirical study of CRM and profitability evaluation," Journal of Information Management, vol. 11, no. 4, 2004, pp. 161-203.

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國際替代計量

銀行財富管理客戶貢獻分群機制之建立

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