CONCEPTUAL FRAMEWORK OF PREDICTIVE STUDENT RELATIONSHIP MANAGEMENT (PSRM) USING BUSINESS INTELLIGENCE FOR UNDERGRADUATE STUDENT RETENTION

Abstract

This research proposes a conceptual framework of Predictive Student Relationship Management (PSRM) Using Business Intelligence (BI) to improve undergraduate student retention. A qualitative approach, including document analysis and thematic synthesis, identified SRM processes aligned with BI strategies. The SRM framework follows the student journey across three phases: 1) Pre-enrollment, focusing on attracting prospective students and sharing institutional information; 2) Enrollment, the critical phase, involving four processes: student lifecycle management, personalized counseling, academic support, and managing expectations; and 3) Post-graduation, emphasizing alumni networks and career development. The enrollment phase is pivotal, leveraging BI foundational components, including predictive analytics, to enhance the framework’s capacity to analyze historical and real-time data. Techniques such as data preparation, feature engineering, model development, and validation provide actionable insights for identifying at-risk students. Predictive models based on performance and engagement metrics enable institutions to implement timely interventions, such as academic support and counseling, improving student retention and fostering academic success. This research offers a structured approach for institutions to proactively address student needs, enhance retention, and ensure academic success through predictive modeling.