A web-based system for analyzing the voices of call center customers in the service industry |
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| Affiliation: | 1. Department of Industrial and Management Engineering, HANBAT National University, SAN 16-1, DuckMyoung-Dong, Yusong-Gu, Daejeon 305-719, South Korea;2. School of Business Administration, Kyungpook National University, Sangyeok-dong, Buk-gu, Daegu 702-701, South Korea;3. Department of Industrial Engineering, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Kusung-Dong, Yusong-Gu, Daejeon 305-701, South Korea;1. Huazhong University of Science and Technology, State Key Lab for Multispectral Information Processing Technology, School of automation, Wuhan, China;2. Kyushu University, Human Interface Laboratory, Information Science and Electrical Engineering, Fukuoka, Japan;1. Research and Development, Prehospital Emergency Medical Services, Central Denmark Region, Aarhus, Denmark;2. Department of Anesthesiology, Aarhus University Hospital, Aarhus, Denmark;3. Department of Cardiology B, Aarhus University Hospital, Aarhus, Denmark;4. Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark;5. Emergency Medical Communication Center, Capital Region of Denmark, Copenhagen, Denmark;6. Mobile Emergency Care Unit, Department of Anesthesiology and Intensive Care Medicine, Odense University Hospital, Odense, Denmark;7. Institute of Clinical Medicine, Aalborg University, Aalborg, Denmark;1. Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Center for Petroleum and Minerals, 94300 Kota Samarahan, Sarawak, Malaysia;2. Petroleum Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;1. Clemson University, Department of Automotive Engineering, 4 Research Dr., Greenville, SC 29607, USA;2. Auburn University, Department of Mechanical Engineering, 1418 Wiggins Hall, 354 War Eagle Way, Auburn, AL 36849, USA |
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| Abstract: | We developed a Web-based system for analyzing the voices of call center customers of a life insurance company, so that it would help decision makers understand customer needs better and it would help them make consistent decisions regarding customer support. It used conventional statistical and data mining techniques to identify customer voice patterns. To demonstrate results, we gathered actual customer complaints from the service operation of a target company. Using this data, the system pinpointed problematic areas where complaints happened (one-dimensional analysis), the relationship among problems (two-dimensional analysis), and the root cause of problems (Failure Mode and Effects Analysis). |
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