毕业设计-软件工程-外文翻译-客户服务支持原文.docVIP

Data mining for customer service support Abstract In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: unstructured customer service reports record machine problems and its remedial actions and structured data on sales, employees, and customers for day-to-day management operations. This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis. A data mining process, based on the data mining tool Database Miner, was investigated to provide structured management data for decision support. In addition, a data mining technique that integrates neural network, case-based reasoning, and rule-based reasoning is proposed; it would search the unstructured customer service records for machine fault diagnosis. The proposed technique has been implemented to support intelligent fault diagnosis over the World Wide Web. Author Keywords: Data mining; Knowledge discovery in databases; Customer service support; Decision support; Machine fault diagnosis 1、Introduction Customer service support is becoming an integral part of most multinational manufacturing companies that manufacture and market expensive machines and electronic equipment. Many companies have a customer service department that provides installation, inspection, and maintenance support for their worldwide customers. Although most of these have some engineers to handle day-to-day maintenance and small-scale troubleshooting, expert advice is often required from the manufacturing companies for more complex maintenance and repair jobs. Prompt response to a request is needed to maintain customer satisfaction. Therefore, a hot-line service centre (or help desk) is usually set up to answer frequently encountered problems from the customers. Fig. 1 shows the workout in a traditional hot-line servic