| Abstract: | 全球國際金融開放，臺灣與中國大陸簽署MOU以及ECFA，促使國內金融業進入更具國際競爭性市場；銀行業者要如何成為客戶理財上最佳的夥伴則是下一刻需要面臨的關鍵基礎。本研究以市場區隔理論為基礎來區隔客戶群，以提供銀行業者作為行銷策略之參考。
本研究是用一般生活型態變數做為區隔變數，以大台北地區的日盛商業銀行存放款客戶為主要抽樣對象，採便利抽樣方法，有效問卷420份。研究問卷分為兩部分，第一部分為生活型態，第二部分為人口統計變數。分析中應用了描述性統計、因素分析、集群分析、鑑別分析、及卡方檢定等統計方法。
經研究分析後，本研究結果發現：
1.以三十一題AIO量表，透過因素分析萃取出「社交活躍因素」、「生活規律因素」、「名牌時尚因素」、「家庭溫馨因素」、「情感衝動因素」、「創新活力因素」、及「非務實因素」等七個生活型態因素。
2.依集群及鑑別分析，區隔為三個市場區隔，分別命名為「家庭事業兼顧集群」、「社交時尚集群」、及「行為外控集群」。三個市場區隔在七個生活型態因素上皆有顯著差異，表示消費者對生活型態因素的看法，會因為生活型態集群的不同而有所差異。
3.三個市場區隔與人口統計變數中的「性別」、「年齡」、「職業」，以及「個人平均月收入」等有關，表示三個市場區隔在這四項人口統計變數上的分佈有顯著差異，所以可依照不同市場區隔的人口變數特性，來制定行銷策略。
Upon Global international financial liberalization, Taiwan and China signed the MOU and the ECFA, which makes Taiwan financial market began to enter more competitive international markets; how banks to capture the loyalty of customers have become the best partner of wealth management is the key to success. The purpose of this study is to adapt the theory of market segmentation by separating the customers, and it will yield the best of marketing strategy for banks.
By using the lifestyle variables as segmentation bases, a total of 420 questionnaires were collected from customers who opened an account with Jih Sun Commercial Bank in Taipei.
The questionnaire of this research was divided into two major parts; fist part was on the lifestyle and the second part was on the demographic variables. Descriptive statistics, factor analysis, cluster analysis, discriminant analysis, and Chi-square test of statistical methods were used to analyze the applications. The research findings are as follows:
1.By using the 31 questions from the lifestyle (AIO) questionnaires, seven factors namely, “Socialization active”, “Disciplinary life”, ” Fashion brand”, “Family Warmth”, “Feeling Emotion”, “Creativity Active”, and “Non-practical Active”.
2.By using Cluster and Discriminated analysis, respondent samples can be segmented into three market segments, namely, “Family – Business”, “Fashion Socialization”, and “External – Control behavior”. Significant differences were found in both the three market segments and seven lifestyle factors.
3.Significant differences were found in “gender”, “age”, “occupation”, and “personal average monthly income”, in the three market segments. |
