Customer Management And Evaluation Method In Booking Center

Marine logistics is a containerized shipping logistics with the features of containerization, standardization and global integration mode of transport, starting from the idea of provide shippers, freight forwarders, ship owners with efficient, convenient and low cost of booking services, integrated maritime logistics industry resources, reducing the industry's internal friction, improve the competitiveness of the industry for the purpose. Booking centre is a core maritime logistics that determined the customers is the key to success. The reasonable customer management and evaluation is prerequisite and crucial factor to improve customer relations of booking center. Reasonable evaluation of the customer helps booking center to effectively allocate scarce resources, strengthen ties with customers and obtain real competitive advantage. This paper studies the booking center's customer management and evaluation from the following aspects:(1) Through the study of customer management and evaluation's methods at now, we probe into customer management and evaluation methods of marine logistics and analysis of the current major problems.(2) On the background of marine booking and through the analysis of the booking business, we study the booking center's customer management and evaluation methods from his characteristics and needs. We put forward the methods of booking center's customer management and evaluation with marine shipping features. We used customer value theory, AHP and weighted fuzzy C means algorithm to solve the problem of the booking center's customer classification.(3) We designed the booking center's customer management and evaluation system's structure, the main function, database and algorithm that relied on combining the specific projects and the actual situation of the shipping companies.(4) We developed B/S model booking center customer management and evaluation systems with graphical interface to display results. Through the practical application and test, we verify the correctness of customer classification algorithm.