Customer Relationship Management System with Decision Support

Abstract

This paper studied customer relationship management system with decision support. At present, the fitness studio management system has accumulated a large amount of customer data, but the studio still has problems in customer management such as inaccurate customer target positioning and imperfect customer relationship management functions. It is still unable to conduct intelligent analysis, resulting in a decline in the overall management ability of customer groups. As such, customer turnover is high, and customer loyalty is reduced. To solve such problems, this research constructed a customer relationship management system with decision support, which can be used to identify customers with high studio and high platform loyalty, and with repeated purchase of GT fitness studio. With the purpose of helping the studio achieve accurate customer relationship management and business decision management, this study built an intelligent customer cluster analysis system through K-means clustering algorithm, which can automatically cluster customers into four (4) categories, indicate the characteristics of consumer behavior under each category, and screen out high-quality fitness customers with large consumption amount, high consumption frequency, high platform evaluation, high loyalty, and heavy consumption. At the same time, it also screens out general customers. Moreover, the intelligent group display of the clustered customers is visualized in the form of reports, and the visual display of each group is also carried out, so that decision makers can conveniently implement accurate group management of different groups of customers.