This document discusses using predictive modeling to improve a high school's admissions and financial aid processes. It summarizes the current processes, results from testing a new financial award strategy, and proposes an ideal predictive model. The ideal model would use more detailed applicant data like geography, referral sources, test scores, financial aid applications, and captured interests to better identify qualified candidates most likely to enroll. Defining enrolled student profiles through predictive modeling provides insight to optimize admissions, financial aid, and scholarship practices.