論文摘要
隨著資訊科技快速的發展，企業與客戶間互動的媒介己漸形多元，台灣對客服最大宗的需求來自銀行，尤其是信用卡發卡數量動輒上百萬的消費金融單位。企業發展到成熟階段，客服中心通常是員工數最多的部門；只要規模夠大，走向獨立，經濟規模就是最大的利基，不僅人力方便調度、成本也符合效益。傳統客服的定義，就是找一批人接電話，解決客戶的抱怨，並且以接聽來電的速度，作為績效評估的標準。這定義，正被推翻中。一個專業客服系統，運用許多策略找出最適合的客服人員，施以最好的訓練、提供最佳的機會，培養他們成為多種技能在身的專業人員。
面對當今消費型態的轉變，企業提供的客戶體驗服務已經與時俱進，跳脫傳統一對一人工客服模式，開始建構全渠道、行動化與智慧化的客服平台。
建立全渠道智能客服，無非是想在行動網路時代，縮短與客戶之間的信息溝通斷層，一方面實現客戶體驗核心價值，一方面強化客戶黏著度、開創新商機。尤其智能客服還具備「人機協作」與「多工模式」等特色，在協助減少人事支出、提昇服務效能、避免營運風險等方面都有出色表現。像客服中常見的FAQ，可以改由智慧語音助理代勞，正職員工則訓練成高階人力，出現艱難問題時再接手處理，藉以降低人工成本。或者遇到客服電話滿線，即時性對話可以引導給聊天機器人，或者分散到簡訊、文字或APP等其他交流渠道，提供彈性選擇也有助於拉高行動世代滿意度，幫企業自己累積商譽價值。
 
新客戶入門的過程中，會撥電話到客服求助，試圖釐清產品資訊，或搞懂帳單內容。客服人員的回覆，只是解決了當下的問題，造成問題的根本原因，還是沒解決，問題的根源，都來自於提供服務單位的本位心態，以及不同單位間各自為政的文化、行為、流程和政策。要改變，就得重新思考如何管理服務運作流程，並且重新設計顧客體驗旅程。
關鍵字：客服中心、智能客服、顧客體驗

ABSTRACT
With the rapid development of information technology, the medium of interaction between companies and customers has become more and more diversified. The main customer service demand comes from banks in Taiwan, in particular, millions of consumer financial units with issuance of credit card.

While the enterprise develops to the maturity stage, the customer service center is usually the department with the largest number of employees; as long as the scale is large enough and independent, the economic scale is the largest niche, and not only the convenience of manpower management, but the efficiency of cost. The definition of traditional customer service is to find a group of people to answer the phone, solve the customers’ complaints, and take the speed of answering calls as the performance evaluation criteria. However, one professional customer service system uses many strategies to identify the most suitable customer service agent, apply the best training, provide the greatest opportunities, and train them to become professionals with multiple skills. In view of changes in today's consumption patterns, the customer experience services have been advancing with the times and have escaped the traditional one-on-one manual customer service model, and have begun to build an Omni-channel, portability and intelligent customer service platform. The establishment of Omni-channel intelligent customer service is nothing more than to shorten the gap in information communication between customers and customers in the mobile internet era. On the one hand, it realizes the core value of customer experiences, and on the other hand, it strengthens customer adherence and creates new business opportunities. In particular, intelligent customer service also has features such as “human-machine collaboration” and “multi-tasking mode”, which has performed well in helping reduce personnel expenses, improve service effectiveness, and avoid operational risks. Take common FAQs in customer service for example, intelligent voice assistants could use instead, while the full-time employees trained as executives to take over and deal with problems that are more difficult to reduce labor costs. Besides, while there is an encounter of a full phone line of customer service, instant dialogue could direct to the chat robot, or dispersed to the text message, APP, or other communication channels, to provide flexible choices to help heighten the satisfaction of portability generation, also to accumulate the value of goodwill for the company.

Most of the time, new customers will call the customer service for assistance in trying to clarify the product information or the contents of the bill. The reply from the customer service agent only solves the current problem, but the root cause of the problem remains unresolved, while the problem lies in the basic mentality of providing service units, the culture, behavior, process, and policies of different units. In order to change and make progress, you have to rethink how to manage service operation processes and redesign the experience journeys for customers.

Key Words: Customer Experience, Customer Service Center, Intelligent Customer Service

參考文獻
1. 中信金企業社會責任報告書，2016。http://www.ctbcholding.com/care_index.html
2. 中信金年報，2016。http://ir.ctbcholding.com/c/financial_reports.php
3. 中華民國金融監督管理委員會-銀行局，2018，信用卡業務統計。
https://www.banking.gov.tw/
4. 玉山金控企業社會責任報告書，2016。
https://www.esunfhc.com/zh-tw/csr/overview/csr
5. 玉山銀行年報，2017。
https://www.esunfhc.com/zh-tw/investor-relations/company-overview/annual-reports
6. 吳怡靜，2016，企業新優勢：贏在顧客體驗旅程，天下雜誌，第599期，台北市：天下雜誌出版社。
7. 吳友仁，2005，支援企業客服中心運作之自動問答與知識摘要技術。
8. 李明德、曾俊欽，2003，科技客服-客服中心的系統建置，新北市；培生教育出版集團。
9. 沈桂鳳，2004，應用灰關聯分析於資料倉儲中實體化視域之動態挑選。
10. 何維涓，2017，資料科學家看FinTech趨勢金融業有4大AI成熟領域，電週文化事業。https://www.ithome.com.tw/news/117795
11. 明雲青，2005，知識管理融入客服中心運作。
http://www.iq-t.com/news/data/
12. 周文卿，2001，資料倉儲－企業拓展商業智慧領域的基石，經營決策論壇，第24期，http://www.gss.com.tw/eis/。
13. 林孟儀，2001，每一通電話都是商機，CHEERS線上雜誌，Vol.15。
http://www.cheers.com.tw/
14. 洪世章，2016，創新六策：寫給創新者的關鍵思維，台北市：聯經出版社。
15. 洪辭禮，2005，ISP 客服中心服務品質之改善－導入客服知識庫系統。
16. 莊文明 ，2014，客服中心之價值、創新與展望，台灣客服中心發展協會。
http://www.tccda.org.tw/
17. 國泰金年報，2017。
http://www.ir-cloud.com/taiwan/2882/irwebsite_c/annualreports.php
18. 國泰金企業社會責任報告書，2016。
https://www.cathayholdings.com/holdings/csr
19. 商業周刊，2017，銀行數位轉型無路可退－擁抱數據，搶攻手機世代，台北市：城邦文化事業股份有限公司。
20. 商業週刊，2017，智能客服解決方案 企業成長的創新戰略，台北市：城邦文化事業股份有限公司。
21. 張榮貴，2001，客服中心知識庫管理策略，程曦資訊。
https://www.chainsea.com.tw
22. 馮元甫，企業導入知識管理：認知階段之研究—以 A 公司為例。
23. 童啟晟，2000，CALL CENTER 的發展現況與趨勢，資訊與電腦： 41-43。
24. 謝明慧，2018，將顧客體驗管理化為銷售績效，哈佛商業評論，台北市：遠見天下文化出版股份有限公司。
25. Agrawal R., Imielinski T. and Swami A., 1993. Mining Associations between Sets of Items in Massive Databases Proceedings of the ACM SIGMOD International Conference on Management of Data,Washington D.C.: 207-216.
26. Agrawal R., and Srikant R., 1995. Mining Sequential Patterns Proceedings of International Conference on Data Engineering, Taipei, Taiwan: 3-14.
27. Bhatia A., 1999. Roadmap to Implementation for CRM White Paper, Business Analyst (ERP) Infosys Technologies Ltd., http://www.crmassist.com.
 
28. Bodin, Madeline, & Dawson, Keith., 1999. The Call Center Dictory, NY: Telecom Books.
29. Chen M.S., Han J., and Philip S. Y. 1996. Data Mining: An Overview from a Database Perspective, IEEE Transaction on Knowledge and Data Engineering, December, Vol. 8, No. 6: 866-883.
30. Dawson, Keith., 2005. The Science of Customer Happiness, Call Center Magazine, Vol.18: 6.
31. Feinberg, R. A., Trotter, M., and Anton, J., 2000. Anytime, Anywhere, Anyhow, in Anderson Consulting (Eds.), Defying the Limits, Montgomery Research, San Francisco, CA: Montgomery Research Inc., : 297-303.
32. Han J. and Kamber M., 2001. Data Mining concepts and Techniques, Morgan Kaufmaun.
33. Holt S. 1998. Customer Service Marches Front and Center.Infoworld
September: 75.
34. Inmon,William H. 2005. Building the data warehouse. John wiley & sons.
35. Inmon W.H. and Kelley C., 1993. Rdb/VMS: Developing the Data Warehouse, QED Publishing Group, Boston, Massachussetts.
36. Kinball R., 1995. Is ER Modeling Hazardous to DSS, DBMS.
37. Kuochung Chang;Nating Liu;Ko-Li Jung, Ming-Yuan Cheng, 2006. Journal of Management & Systems, Vol.13, No.2: 201-220
38. Lin W.Y., Wu P.S., and Tao Y.H., 2004. A model of building data warehouse for customer relationship management, Journal of Information Management Concepts, Systems, and Applications, Vol. 6, No.1: 99-115.
39. Matthew Dixon, Lara Ponomareff, Scott Turner, Rick DeLisi, 2017. Kick-Ass Customer Service, Harvard Business Review
 
40. Ng R.T. and Han. J. 1994. Efficient and Effective Clusting Methods for Spatial Data Mining, Proceeding of International Conference on Very Large Data Bases: 144-155.
41. Sheshunoff A., 1999. Winning CRM Strategies, ABA Banking Journal,10: 54-66.
42. Wayland R.E. and Cole P.M., 1997. Customer Connections: New Strategies for Growth, Harvard Business School Press, Boston.