Studying about the customer segmentation and begetting customer ranking plan diverts more attention in recent years. In this regard, this study tries on providing a methodology for segmenting customers based on their value driver parameters which extracted from transaction data and then ranks customers with regard to their customer lifetime value (CLV) score. Discovering hidden pattern between customers ranking result the other data such as customer product ownership data and socio-demographic information is the other work which addressed in this paper. Achieving this, we used data mining techniques such as different classification and clustering approaches, and implemented them on real data from an Iranian private bank. Current study can provide good insights for marketing and CRM department of the organization in identifying different segments of customer for designing future strategic program.