請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/21979
標題: 設計並檢視互動性個人化保險電子郵件推薦之有效性
Designing and Inspecting the Effectiveness of Interactive Personalized Insurance Recommendation by E-mail
作者: Chen, Yi-Chung
陳怡君
關鍵字: Insurance marketing
保險行
Personalized recommendation
Data mining
Flow Theory
E-mail Marketing

個人化推薦
資料
探勘
沉浸理

電子郵件行
出版社: 電子商務研究所
摘要: 對人壽保險業而言,隨著資訊技術環境成熟、網際網路使用人口的增加、顧客需求改變等影響下,人壽保險公司紛紛計畫應用網際網路的力量,創造新的經營模式,以增加顧客購買意向與提高顧客忠誠度。而隨著網際網路的盛行,電子郵件也因為「快速」、「有效」、「低成本」被大量使用做為行銷的管道之一。而推薦系統是網路上常運用的技術,主要用來提高顧客的購買意向,我們將此技術應用在保險業上,希望藉由個人化保險推薦系統推薦給顧客其有興趣及需要的保險商品及資訊,進而增加顧客對於保險商品的購買慾望跟提高顧客對保險公司的忠誠度。此外,沉浸理論近年來廣泛地被用在網路研究上,它是一種暫時性的、主觀之經驗,並且可用來解釋人們為什麼願意繼續再從事某種活動之原因,在本研究中欲透過沉浸理論來協助檢視其保險個人化推薦之有效性。 根據上述幾項論述,本研究採用資料探勘技術去建立ㄧ套個人化保險電子郵件推薦系統並且利用網路問卷協助探討個人化保險電子郵件推薦是否能夠在顧客瀏覽個人化保險推薦時產生沉浸效果,而透過沉浸效果的產生是否能夠影響其保險推薦的施行效果。 本研究依據所欲研究的問題,針對台灣地區20歲以上有使用電子郵件民眾為研究對象,採用雙重取樣法回收最終有效網路問卷130份,回收率為78.78 %。而研究方法包括次數分配(Frequency Distribution)、因素分析(Factor Analysis)、Cronbach’s α、結構方程式模型(Structural Equation Model)。本研究得出的結果如下: (1) 透過資料探勘技術建立的保險個人化推薦系統能夠依據保險公司資料庫預測新顧客適合的保險推薦險種。 (2) 當顧客透過電子郵件瀏覽保險公司所提供的個人化保險推薦能夠產生沉浸效果。 (3) 顧客瀏覽個人化保險電子郵件推薦所產生的沉浸經驗對保險推薦效果有其直接的影響。
With the stability of information technology environment, the growth of Internet population and changes in customer needs have prompted life insurance companies to adopt their business models to grasp opportunities in the Internet area, and to increase more customers' purchase intention and loyalty. E- ail that has the characteristics including “Faster,” effective,” and “low cost,” has become one of marketing hannels. Recommendation system have been widely adopted in the nternet area, it can help to increase customers' purchase intention and loyalty. In this research, we adopt ecommendation technology in insurance area, and hope it can upport insurance company. Furthermore, “Flow Theory” is extensively applied in the Web environment and it can help to explain why people repetitiously use particular service. In this research, we adopt flow to inspect whether the personalized E-mail insurance recommendation work or not. According to the statement as above, in this research, we manipulate data mining technology to create a personalized E- ail insurance produces recommendation system, and we adopt the online questionnaire to find out the causal relationship among personalized E-mail insurance recommendation attributes, experiential flow, and personalized E-mail nsurance recommendation performances. In this research, objects are people living in Taiwan, more han 20 years old and are used to use the E-mail. We adopt double sampling to collect 130 usable questionnaires and the esponse rate is 78.78 percent. The analysis methods in this research are analysis of frequency distribution, factor nalysis, Cronbach's α and structural equation model. The important results of this research are as follows: 1. We can predict suitable insurance products to new customers with the data mining developed personalized insurance recommendation based on insurance company's database. 2. Customers are in the flow state when they reading the personalized E-mail insurance recommendation provided by nsurance company. 3. The flow experience has direct impact on the personalized E-mail insurance recommendation performances.
URI: http://hdl.handle.net/11455/21979
顯示於類別:科技管理研究所

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