利用資料採礦技術提昇財富管理效益; -以個案銀行為主 Using Data Mining methodology to improve Wealth Management benefits-Base on a Case Study

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题名:	利用資料採礦技術提昇財富管理效益;-以個案銀行為主 Using Data Mining methodology to improve Wealth Management benefits-Base on a Case Study
作者:	黃青弘;Ching-Hung Huang
贡献者:	資訊管理學系碩士在職專班
关键词:	資料採礦;財富管理;顧客關係管理;精準度;CRM;Wealth Management;Data Mining;Accuracy
日期:	2008-06-27
上传时间:	2009-09-22 15:30:07 (UTC+8)
出版者:	國立中央大學圖書館
摘要:	隨著金融市場的自由開放，三步一小家本土銀行，五步一大家私人銀行，還有隨處可見的外商銀行，讓產業變得非常競爭。為了要爭取客戶，只能壓低利潤，削價競爭，賺取微薄利差。自從『顧客關係管理』開始被熱烈討論後，業界的經營模式，已擺脫過去以產品為導向的銷售方式，漸漸走向個人化與客製化服務。『財富管理』是近年來在金融業最受重視的業務。銀行除了要有能力提供高品質財富顧問服務，以及豐富的產品架構外，更要進一步了解自己的客戶，加強與客戶之間的互動關係，才能適時給予良好的理財建議，提升銀行銷售能力，為客戶與銀行創造利潤雙贏。本研究在積極找出個案銀行客戶的特徵輪廓，探索客戶的喜好，了解高價值客戶的生命週期特徵與其關聯式產品分析。另外，用更精確的方法，找到個案銀行高價值客戶。經過研究發現以下幾個特點：1.原本認定高資產客戶為個案銀行的貴賓理財戶，結果應建議銀行重新定義貴賓專戶，是有高貢獻的客戶。 2.客戶會因為消費能力與消費意願不同，而購買不一樣的商品。 3.高價值客戶的生命週期特徵與產品喜好。 4.透過客戶基本資料分析、理財專員業務經驗與資料採礦科學方法，可以提昇命中高價值客戶之精準度。 Along with the finance marketing free opening, a great diversity of banks has emerged to fulfill customers’ demands, and the industry becomes extremely competitive. Banks are willing to lower their profits hoping to obtain more valuable customers. Since the “Customer Relationship Management” starts to be discussed popularly in the field of the management pattern, market strategy is no longer led by product oriented, instead it targets on personalized and customized services. “Wealth Management” is becoming the cutting edge of business in the financial industry. The banks not only are expected to provide their with high quality wealth consultant service, they are must able to know their customers well, and to enhance customer interaction relations. Being able to provide the right product to the right person in the right moment will promote the bank sale abilities to make more profits. This research is discovering the case study bank’s customer characteristic outline, exploring customer preference, understanding the high value customer life cycle characteristic and the associated product analysis. Moreover, we hope to find the high value customer in each case study with a more precise method. In this research, we discover the following characteristics: First of all, the high property customers, we recognized, might not be really VIP customers in the case study bank. The result suggests the bank should redefine those profit customers as the high contribution customers. Secondly, customers’ choice of preference is variable because of expense abilities and wishes. Thirdly, high value customer’s life cycle characteristic and their product preferences can be vary. Lastly, from customer profile analysis, expert’s experiences and the Data Mining methodology, we could obtain the high value customer more accurately.
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