電信數據電路客戶流失資料探勘

Abstract

民國九十年二月起交通部陸續核發三家（台灣固網、速博電信、東森寬頻電信）固定通信網路經營特許執照，加上原有之中華電信共計有四家均可經營第一類電信業務，包括出租各種速率之數據電路供應國內市場需求。因而整個電信市場瞬間形成多家激烈競爭的局面，業者紛紛推出各種優惠促銷方案來拉攏客戶。各家業者為維持市場佔有率及降低日趨嚴重的客戶流失問題，均不惜降低利潤來保有客戶。據統計，企業得到一位新客戶所花的成本是維繫一位老客戶的五至十倍代價。因此業者莫不想盡辦法來留住老客戶，同時也對客戶的消費行為進行探勘分析，企圖找出可能流失的客戶並及早速謀新的行銷策略。本研究主要是應用叢集分析(Cluster Analysis)技術，以IBM Intelligent Miner及MATLAB為資料探勘(Data Mining)工具，分別將國內某電信業者的數據電路退租資料進行「類神經叢集化」(Neural Clustering)、「人口統計叢集化」(Demographic Clustering)、「模糊C均值叢集化」(Fuzzy C-Means Clustering)探勘分析，進而選取較佳之叢集分析結果做合理推論，以探勘出可能流失的客戶及哪些產品在競爭市場上已漸居劣勢，必須速謀因應對策。經由探勘分析，本文發現：⑴Group2的16565位客戶為獲利性較低的高忠誠客戶。⑵Group3的31位客戶是具有高度流失傾向的大企業。⑶Group4的319位客戶為高收益且移轉性低的高忠誠中小企業。在本研究預測出的31個高潛在流失客戶中，經過印證實際潛在流失客戶數後，發現有23個客戶確實已與其他同業進行新合約的協商，印證準確率達74%。這些經資料探勘的成果，將對電信固網業者之數據電路經營具有提昇競爭力的加分效果。

The Ministry of Transportation and Communications had issued three concession licenses of Type Ⅰ telecommunications to Eastern Broadband Telecom, Sparq Telecom, and Taiwan Fixed Network Telecom respectively from February 2001. Counting the original Chung-Hwa Telecom, there are four telecom companies permitted to operate Type Ⅰ telecommunications business, including renting out any rate of data circuit to meet the needs of the domestic market. As a result, fierce competition soon arises in the telecommunication market. In order to attract the clients, the providers come up with various favorable sale promotions, which make chaos in the telecommunication data circuit market. On the purpose of maintaining market share and reducing the downward problem of customer moving out, the providers choose to cut down profits to retain customers. According to statistics, however, the cost for a company to gain a new customer is as much as five to tell times more value than that of retaining an old customer. Therefore, the service providers spare no effort to retain old customers. Meanwhile, they try to analyze the attitudes of the customers with data mining technique, attempting to find out customers of the possible moving out and thus help companies to propose new marketing strategies. This study mainly applied the technique of Cluster Analysis, used IBM Intelligent Miller and MALAB as tools, and analyzed the customer's withdrawal data with Neural Clustering, Demographic Clustering, and Fuzzy C-Means Clustering, then selected a best one of chum data to infer the result reasonably. The purpose is to find out the possible moving out of customers and products that are going downward in the market, and thus launches strategies against the problems. These will give an effective help to the company decision maker, particularly obvious benefits and improvements on the marketing strategy planning and the performance. According to the results of data milling, this paper finds: ⑴ In Group2, 16565 customers are high loyal customers with low profitable. ⑵ There are 31 large enterprises with high shift tendency in Group3. ⑶ In Group4, 319 customers are high loyal firms with high profitable and low shift. These results will provide the conduct of data circuits of Telecom with the bonus effect of competitiveness improvement.