Unveiling Student Segments: Leveraging Clustering Analysis of Registration Data for Enhanced Recruitment Strategies

Abstract

Recruiting the right students is crucial for higher education institutions to achieve their goals of attracting talented individuals and fostering academic success. This study aims to investigate the effectiveness of data-driven recruitment strategies in identifying target student populations and tailoring recruitment efforts accordingly. By utilizing K-means clustering analysis on a dataset of student profiles, this research identifies distinct clusters based on factors such as province of residence, parent occupation, and parent revenue. The findings reveal valuable insights into the characteristics and preferences of different student clusters, enabling the development of targeted recruiting strategies. The results indicate that a significant number of students in specific clusters are from Songkhla Province, have parents predominantly engaged in the private sector, agriculture, and fishery occupations, and come from families with moderate income levels. Building upon these findings, several recommendations for recruiting strategies are proposed. These include focusing marketing efforts in the identified regions, forging partnerships with local businesses, offering financial aid programs, and establishing connections with agricultural and fishing communities. This can involve targeted online advertisements, promotional campaigns in local schools, and active participation in career fairs or educational expos held in Songkhla Province. This study contributes to the field of higher education recruitment by leveraging data-driven approaches to identify target student populations and develop tailored strategies.