Customer relationship management (CRM) is one of the most important recommendation systems to manage customer groups and understand potential relationships. CRM systems use many communication approaches such as telephone, email and social media to collect mass of customer data, which can help the companies (managers) to increase sales growth and enhance customer retention. Since 2000s, the advancements of RFID technology brought a new perspective on customer in-store behavior. Based on such RFID data, this paper describes a novel analytic approach applying social network analysis (SNA) into customer relationship network (CRN) and presents three contributions. First, to be different from existing studies, we attempt to construct the CRN based the shopping path (visiting patterns), which is recognized as a data stream of digitally encoded coherent signals. The CRN can help us to understand the customer relationships from the network perspective. Second, we employ network clustering (also called community detection or node classification) method to extract customer groups by maximizing the modularity. The modularity provides us a structural and relational understanding of customer groups. Third, we use within-module agree and participant coefficient to measure how a customer well-connected to other customers in the CRN; and identify such customers as 'hub' customers by a topological process.