在近年來的金融風暴下國內金融環境急速丕變,金融業者在面臨全球競爭激烈的產業環境下,應思考如何強化與顧客關係,及獲得顧客長期支持,為企業帶來較大的利潤,是未來之趨勢。 由於國內近年來一直處於低利率時代,顧客無法以定存來對抗通膨,因而延伸出更多的理財規劃需求,在財富管理這部份,金融業者更是針對頂級重要顧客規劃出VIP貴賓理財中心,然而國內推廣VIP貴賓理財服務之金融業者,管理策略各有不同,因此金融業者採取何種積極的服務態度來提升VIP貴賓理財服務之滿意度,幫助金融業者搶攻市場,凸顯其競爭力益顯重要。 本研究依據吳婉寧所設計之問卷所提出之「企業形象構面」包含有12個變數之數據;「顧客滿意度構面」包含有11個變數之數據;「關係行銷偏好構面」包含有7個變數之三構面量表進行因素分析及群集分析技術,比較受訪者資料因素分析後進行群集分析與直接群集分析之差異性結果,協助企業從大量的顧客資料中挖掘資料間的結構,簡化資料的複雜性,進而了解並擷取資料背後所隱含的資訊,提供金融業者採取何種積極的服務態度來提升VIP貴賓理財服務之滿意度,幫助金融業者搶攻市場於適當時機提供VIP貴賓理財顧客適切之服務,幫助金融業者搶攻市場。
As our financial market changes rapidly due to the recent financial crisis, our financial industry is also facing severe competitions from companies around the world. In order to survive in such rapid changing market and fierce competitions, we should focus on how to improve the relationship with our clientele so that we could gain their trust and supports. The low interest rate triggered by the recession has made it quite difficult to fight against inflation rate by merely depositing money into saving accounts. People need different means to grow their fortune. Hence, the use of financial management service becomes especially important. In addition to the financial management for general public, financial companies also provide special VIP service designed for the exclusive clients. However, there are too many financial management companies competing for those VIPs. Thus, merely providing special investment strategies for the VIPs is not enough. How to tailor the company’s strategy to satisfy the specific need of each VIP will play a significant role in differentiating itself from many other financial management companies. The questionnaire is designed according to Wu Wanning who proposed 1)"corporate image dimensions", which contains 12 data variables; 2)"customer satisfaction dimension", which consists of 11 data variables; 3)"preferred dimensions of relationship marketing", which contains 7 variables in three dimensional scale with factor analysis and cluster analysis. This study employs two different approaches in analyzing data. The first approach is to use factor analysis to filter out unnecessary factors and then use cluster analysis to examine the data.