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Most existing network-based customer services heavily rely on manpower in replying e-mails or on-line requests from customers, which not only increases the service cost, but also delay the time for responding the service requests. To cope with these problems, this paper proposes a customer service system, which can automatically handle customer requests by analyzing the contents of the requests and finding the most feasible answers from the frequently asked question (FAQ) database. In the situation that a customer is not satisfied with the reply, the system will forward the request to the appropriate service personnel for further processing. An assistance mechanism has been developed to help the service personnel in finding potential answers from existing FAQ data or creating more appropriate answers. Experimental results on practical applications showed that over 87.3% of users were satisfied with the replies given by the system; therefore, we conclude that the system can significantly reduce the service cost and provide more efficient and effective customer service.

Researchers showed that enterprises would increase over 60% profits if they can build strong relationships with customers [15]. Nevertheless, some investigations also indicated that most businesses loose their customers by 25% rate per year in average [14]. The cost of finding new ones, however, is five times that of keeping original customers. Therefore, one of the most important issues for increasing enterprise competitive advantages is the development of new mechanisms to provide good service to customers. Customer relationship management (CRM) is an integrated solution designed to reduce costs and increase profitability by solidifying customer loyalty. A successful CRM includes four key elements: consumer commutation management, customer relationship management, decision support management and integration of financial or logistic system. WorldTalk Corporation estimates that over 60 millions of people prefer to deal with their work by e-mail. The investigation of Department of Industrial Technique in Taiwan also observed that 96% of companies have employed e-mail systems to serve their customers. It seems that e-mail has become an important communication tool for most people. However, an investigation in 1998 has pointed out that enterprises gave no response to 65% of customer’s inquiry [17]. The report also indicated that 63% of responded customers spend 5 days or longer to wait for the response. From these observations, it seems to be valuable to build up an efficient system for dealing with customer requests. In recent years, e-mail based customer service system has been discussed and presented in several researches [3] and [5]. In most existing systems, automatic e-mail reply functions and customer request classification functions are not taken into consideration. Li and Tseng [13] proposed a system that can automatically handle customer requests by analyzing the request contents; however, the domain expert must define the weight of each keyword in advance, which is time-consuming and will decrease the accuracy of the analysis results. To cope with these problems, this study proposes a new system, automatic customer service system (ACSS), which can automatically reply the requests from customers by invoking a knowledge base containing a set of frequently asked questions (FAQ’s) and the corresponding character vector (CV) of each FAQ. While receiving a request, ACSS will generate a CV for the request, and compares it with those of the FAQ’s by employing the space vector concept to find the most feasible answer for the customer. If no feasible answer can be found, ACSS will forward the inquiry to the appropriate service staff. Once the service personnel has provided an answer to the request, the new request as well as its answer will be recorded in the FAQ database, such that the system will be able to reply similar requests automatically.

This study proposed an automatic customer service system (ACSS) that can automatically reply customer requests by selecting the mostly feasible answers from the FAQ database. If a customer is not satisfied with the answer given by ACSS, a service request assignment algorithm is invoked to assign the inquiry to the most relevant service staff, and the new answers given by the personnel will be recorded for further usage. From several experimental results, it can be seen that ACSS can provide high quality services efficiently, and hence the work burdens of service personnel can be significantly relieved. Meanwhile, the 24-h service provided by ACSS can also shorten the customer waiting time, which not only reduces the service cost, but also increases the competition advantages of enterprises. The idea of this research can also be applied to other applications, such as the development of an intelligent BBS system that can find past relevant discussions for a new proposed question to avoid duplicated discussion contents, and the development of an intelligent tutoring assistance system to solve the students’ problems in learning and to ease the burden of the teachers.