Admission Yield Modeling: A Feature of Comprehensive Enrollment Management

Session Abstract: Achieving enrollment targets in higher education is increasingly complex due to evolving policy changes, shifting demographics, and unexpected internal and external factors. Effectively managing yield and melt rates is essential for successful admission strategies; otherwise, institutions risk under- or over-enrollment. This proposal introduces a robust admission yield modeling framework, using machine learning and other approaches, to help navigate challenges. The model analyzes yield and melt rates from historical data to provide enrollment insights. It projects anticipated acceptances and recommends the optimal offer numbers for the current cycle to meet census targets at a high level of granularity––across majors, residency, etc. By delivering comprehensive and detailed projections, coupled with daily tracking, this model empowers enrollment management teams to develop informed strategies, respond to evolving real-time trends, and achieve enrollment goals.

Keywords: enrollment management, machine learning, predictive modeling, evidence-based decision-making