Fuzzy Clustering Algorithm for Silent Customer Segmentation in Securities Industry

Due to the unique advantage of soft classification, fuzzy cluster analysis can realize the customer segmentation and improve customer relationship according to the uncertainty and fuzziness of customer’s behavior. Based on the research approach, this study applies the fuzzy cluster algorithm to silent customers’ segmentation in securities industry and to identify the customers with similar characteristics and value. This study proposes a multi-dimensional segmentation model for silent customers with 8 indicators of industry characteristics based on the theory of customer relationship management (CRM), and carries out an empirical research on real customer data and transaction data by the fuzzy clustering analysis of data mining, then identifies different groups of silent customers with similar characteristics. Finally, some marketing strategies are put forward in correspondence with different traits and preferences with the purpose of waking them up.