關鍵服務特性分析—以某一電信平台為例

Abstract

本篇研究應用聯合分析法，分析 Duke 大學在 Teradata 顧客關係管理中心所 提供之一間美國主要無線通訊公司顧客資料。研究結果發現，在篩選出來做為研 究自變數的電信服務屬性當中，以「簡訊」對於 MOU 及 ARPU 影響最大，其次 為三方通話，而其中以「分區通話服務」的重要性最小。在重要性排序方面，以 MOU 與 ARPU 分別作為依變數來研究，發現只有小部分差異，顯示 MOU 及 ARPU 的確關係相當密切。 此外，本研究也參考之前文獻選出有意義的人口統計變數，將之區分為四個 組間相異性大、組內同質性高的族群，以進一步探討電信加值服務屬性的聯合分 析。這讓電信營運商能得知不同服務在不同族群中對 MOU 及 ARPU 所造成的效 用與相對重要性,並據此在各群體推廣相對提升 MOU 與 ARPU 較大的服務。

In the thesis, conjoint analysis is applied to analyze the data of a main wireless telecommunication company in the U.S. The data is provided by Teradata Center of Duke University. The results show that between all selected variables, the variable “SMS” influences both MOU and ARPU the most, and followed by “three-way calls”. In contrast, “division type code” has the minimum influence over MOU and ARPU. Besides, there are few differences when referring to the ranks of relative importance of each service attribute toward MOU and ARPU. Therefore, the relationship between MOU and ARPU is closed. In addition, demographic variables is selected by consulting papers and then divided them into four groups, which are large dissimilarities between groups but homogeneous in each group, to further conduct conjoint analysis individually. According to the analyzed results, telecom operators could understand the relative importance of each service attribute in each group. Hence, telecom operators could promote the service attributes which enhance MOU and ARPU significantly in different groups accordingly.