標題: 影響行動通信客戶流失因素之探討-以臺灣電信業者為例
Determinants of Customer Churning Behavior in Mobile Communications- A Case Study of Operator in Taiwan.
作者: 廖子逸
Liao, Tzu-Yi
楊千
Yang, Chyan
管理學院經營管理學程
關鍵字: 手機門號可攜式服務;客戶流失;潛在客戶流失預測系統;大數據;資料探勘;Number Portability;Customer Churn;Potential Customer Churn Prediction Model;Big Data;Data Mining
公開日期: 2015
摘要: 民國1989年開放行動電話,民國1997年開放行動通信業務接受民營業者的營運執照申請,從此開啟臺灣行動電話經營業者間激烈競爭及行動電話客戶數不斷增加,截至目前臺灣行動電話市場共計有9家業者分別經營2G、3G、4G、PHS及WiMAX。由於行動通信服務提供了便利且隨時隨地的移動通信電信服務,加上近年來智慧型裝置普及(手機、平板等),故在過去的十年來在臺灣行動客戶不斷成長,但近年來許多地區的行動客戶已呈現成長趨緩,加上2006年10月15日開放手機門號可攜式服務(Number Portability簡稱NP),號碼可攜服務係指電話客戶更換提供電信服務之經營者時,仍可繼續沿用原有之號碼,大幅降低轉換電信公司之障礙,也因此更加劇在臺灣行動電話市場的激烈競爭。 隨著臺灣行動通訊市場日趨飽和,競爭日益激烈,各業者均面臨客戶嚴重流失問題。依據國家通訊傳播委員會(National Communcations Commission)統計資料,從NP服務開放迄2014年底NP門號生效數共計約達2千8百萬門號(包含各業者自家2G、3G、4G服務異動)。除開發新客戶外,降低客戶流失問題亦關係到企業的成敗及能否維持競爭優勢的關鍵。本研究藉由電訪問卷及運用行動信相關大數據資料(Big data)進行多維度資料探勘(Data mining),藉此定義顧客流失的關鍵影響因素,以期建立潛在客戶流失預測系統,俾利電信業者能有效的及早預測潛在可能會NP客戶,儘早擬定預防措施,以降低流失客戶。
In retrospect the telecommunication operation in Taiwan, government has implemented liberal policy in the telecommunications service industry in 1997. The decision has opened the door for private telecommunication firms and the telecommunications market changed to the condition of fierce competition. As of today, there are 9 operators running 2G, 3G, 4G, PHS and WiMAX system in Taiwan. With the growth of the mobile penetration and the Number Portability policy’s opening, the NP policy intensifies the market competition develops well. According to National Communications Commission’s data, the total effective cellphone numbers in Taiwan reached 28 millions in Dec. 2014. Because churn management has became a fundamental concern since the Taiwan mobile communication market is on the verge of saturation, companies’ strategies to handle churn issue have been directed to survive or maintain an advantage in such a competitive marketplace. This study constructs questionnaire by phone interviewing with mobile users and using data mining methodology. This study examines the key factors for customer churn rate and builds up the churn rate prediction model to help mobile operators to identify the possible NP threat.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT070263710
http://hdl.handle.net/11536/126010
Appears in Collections:Thesis