Combining RFM-based Clustering and GIS-based MCDM for Effective Promotion Location Selection in Higher Education Marketing

This study proposes a novel approach for identifying potential promotion locations for higher education institutions (HEIs) by combining Recency-Frequency-Monetary (RFM)based clustering and Geographic Information System (GIS)-based multi-criteria decisionmaking (MCDM).The author uses historical student enrollment data from an Indonesian university to demonstrate the effectiveness of the proposed method.The study aims to extend RFM-based targeting analysis with suitability analysis using the GIS-based MCDM method to determine potential areas for HEI promotional activities.The author applies K-means clustering to identify high-value feeder schools as the target segment and then uses GIS-based MCDM with a weighted linear combination (WLC) to select the most potential areas for promotion based on three criteria: accessibility, market potential, and market concentration.The results show that by focusing on a small number of high-value feeder schools in selected areas, the university can potentially gain a significant portion of its enrolled students while optimizing resource allocation for promotional activities.The proposed approach contributes to the field of higher education marketing by providing a data-driven method for determining promotion target markets and locations.