[[alternative]]The Development of the Customer Satisfaction Model and Customer Clustering for the Internet Stockbroker

[[abstract]] 顧客滿意度已被視為一種有效衡量企業服務缺口的工具，但由於網路券商為新興的產業，故相關研究仍相當缺乏。另外，企業在制定行銷策略時，往往因不知企業本身顧客的特性，而顯得困難重重，也因此資料挖掘技術廣泛地應用以協助企業來解決相關課題。 本研究將以投資者網路下單流程角度，建置一套完整的網路券商顧客滿意度，以作為網路券商審視本身服務缺口的參考依據，並以多變量分析之因素分析來簡化原本眾多的構念，最後將利用群聚分析之兩階段法(SOM+K-means)針對網路下單投資者下單特性進行分群，以協助網路券商了解現今投資者之網路下單特性。 本研究採取問卷調查方式，有效問卷為492 份，研究結果發現網路下單顧客滿意度之構念因素可分為客服人員之服務內容、證券交易系統之服務內容、交易穩定與安全性、網路下單之利益因素共四個構念。在群聚分析結果方面，則將投資者分為三群，其分別為低度潛力客戶、中度潛力客戶以及重要客戶。 [[abstract]] The customer satisfaction have regarded as a tool in measuring the businesses service gap. The internet stockbrokers are rising industries so that there are few literatures of customer satisfaction of internet stockbroker. Lack of knowledge about customers preference and attribute lead internet stockbroker difficult in decision-making. Data Mining is a good method that can help the industry solution in those problems. The purpose of the study is to build a customer satisfaction model of internet stockbroker from a perspective of the online stock trading process. The internet stockbroker can improve its service quality by using this model. The factor analysis of multivariate data analysis is used to extract the customer satisfaction into various dimensions. The clustering analysis (SOM + K-means) is used to divide the online stock trading customers by the trading attributes of the customers. The study is surveyed by questionnaire, and finally 492 questionnaires are obtained.The result shows that the customer satisfaction model divides into four factors,including personal service quality, trading system quality, trading stable and safety, and benefits.The customers divide into three group, i.e. low potential, middle potential, and important customers.