在目前商業環境不斷快速變遷，迫使企業必須持續求新求變來因應。而顧客意識的抬頭，更使得企業不得不去關注，並想辦法將其網絡往外擴展，將顧客所擁有的資訊纳入企業的競爭能力當中。而在資訊科技的推波助瀾下，激增的市場交易也使得各企業所需儲存與處理的資料量越來越龐大。在這種情況下，企業的焦點已從以往的資料蒐集與整理，轉變成如何有效的利用資料庫來進行資訊的獲取。本論文整理出與客服中心顧客滿意度具有顯著關係之關鍵績效指標，客服中心管理者可透過績效指標與產業平均之比較來衡量客服中心營運績效進而改善。另外，本論文將關鍵績效指標以加權平均方法產生顧客滿意度與客服人員績效兩個指標，透過這兩個指標值對顧客與客服人員分別進行分群分析，分群後，客服管理者可以找出顧客滿意度高的群組成員進行一對一關係行銷，或找出績效略低之客服人員加強訓練，藉此維持顧客忠誠度與改善客服人員表現進而提升客服中心營運績效。在產生顧客滿意度指標方面，本論文以類神經方法來訓練推導出各變數的權重值，而實際的顧客資料由問卷方式進行蒐集。本論文主要目的在於應用資料採礦方法於客服中心上，並搭配關鍵績效指標的輔助，分析顧客的滿意程度以及客服人員工作績效，以落實顧客關係管理的理念，並實做資料分析雛型系統供顧客服務中心作為參考之用。

With the rapid development of advanced information technology in data collection and data analysis, an enterprise has more opportunities to analyze and synthesize customer behaviors and profiles to increase competitiveness, adjust product market position, and build customer loyalty. Thus, a customer-centric enterprise shifts its focus from simply getting data to obtaining meaningful knowledge. A contact center is an important part in Customer Relationship Management (CRM). A customer gets the first impression of an enterprise from contacting with the contact center. The performance of contact center may influence the loyalty of the customers, so an appropriate evaluation of its achievement and performance is necessary. We define the significant Key Performance Indicators (KPIs) of contact center operations, and generate the indicators of customer satisfaction and agent performance. The customer satisfaction and agent performance are measured using the weighted average of KPIs. The weights of the customer satisfaction KPIs are derived by neural network methodology. An enterprise can improve its contact center performance by comparing its KPIs values to the industry’s benchmarks values. This research uses data mining technology (such as clustering and neural network) to develop the contact center evaluation methods for the continuous improvement of customer service quality.

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