服務產業已逐漸成為許多國家的核心產業，處於資訊科技的時代之下，傳統服務型態亦受到影響，從面對面的服務型式，已逐漸衍生出透過網路方式加以滿足需求，使服務傳遞的方式有更多元化的選擇，同時，如何有效的衡量顧客價值，並且結合傳統議題進一步探討，為本研究主要動機。依據過去相關文獻指出，顧客價值的研究可分為概念型與數理型，並以企業和顧客的單方面角度做為思考基準，因此，本研究主要探討如何融合兩方觀點建立屬於E化服務產業的顧客價值衡量模式。 顧客價值衡量模式，主要以利潤值與使用時間為概念，探討在每單位時間之下顧客所能創造出的利潤值，在變數的選擇上，分別以企業和顧客角度選擇適當變數，採用預測方式中的簡單移動平均法與加權移動平均法為基礎方法，並依據行銷指標配合變數進而構成顧客分群的衡量構面，將E化服務產業下的顧客群體區分成四種型態，並以模擬情境的設置加以探討在不同的型態之下面對不同的情境其對於整體顧客價值的影響，以推衍各種顧客型態的特性，提供管理者在實務上的策略方向。 經由模擬情況得知，額外變數的考量對於整體顧客價值有正向影響效果，且各種型態中以最佳型顧客群體所帶來的效益相對較大，而流失型的顧客群體其效益雖相對較小，但其對於管理者仍不可忽視，本研究彌補過去傳統顧客價值衡量模式的不足，除了在學術上以新思維考量資訊科技服務之下顧客價值的衡量方式，並在實務上中透過模式之變數達到顧客分群依據，配合模擬來驗證本研究，進一步達成在E化服務產業下以建構顧客價值衡量模式為核心的目標。 The service industry has major impact on economics in many countries. In the information age, the concepts of traditional services must be changed in order to adapt the Internet environment. Many customers start to use e-services over the Internet to achieve their demands. Therefore, the motivation of this research is to investigate how to build the new measurement of customer value for e-services. According to the literature, the researches of customer value can be separated into two concepts: enterprise and customer viewpoints. The research purpose is to understand how to synthesize the different viewpoints and develop the measurement of customer value in the e-services environment. The major concepts of our customer value model are profits and time usage. In the model, we choice appropriate variables from enterprise and customer viewpoints and utilize simple average method and weighted average method as the basic notion for computation. Finally, we aim to segment the customers in the e-service environment by two marketing indicators: customer profitability and customer satisfaction. This research also provides experimental simulation to validate our proposed model. The simulated results indicate the significance of customer value for managers by analyzing customers’ attributes of each segment. Eventually, researchers can make furnish certain managerial suggestions and establish practical implications of our model. The result of simulation shows the positive effect of “reach rate” for estimated customer value. Meanwhile, the “best type” customers of customer segments create more profit compared to other types. The “worst type” customers of customer segments create less profit, but it still needs further investigation in order to discover the significance of managerial implications. In summary, this research provides a complement concept for traditional customer value and customer lifetime value model. In addition, our proposed model provides another perspective to consider the customer value for e-services not only for researches but also practices in the future.
