Integrating RFM Model and Cluster for Students Loan Subsidy Valuation

Abstract

With the reform of tuition fees in higher education, many poverty-stricken students cannot afford tuition fees. The government has built up a set of support system for poverty- stricken students in colleges and universities. Limited funds allocated require resources and targeted toward needy students. Our goal in this paper is to build RFM-based customer segmentation model to assist students loan subsidy valuation through analyses consumption transactional histories in dining room. This study build a framework for identify needy students to assist students loan subsidy valuation. The paper first used analytic hierarchy process (AHP) to determine weights of RFM variables, then applied RFM model to customer segmentation, finally, this study applied cluster algorithm to identify students who should loan subsidy. Through case study, the method can efficiently identify needy students and assist student's loan subsidy valuation.