Reviewing and selecting applicants is complex, and identifying students most likely to enroll and thrive requires more than a single-dimensional view of applicant data. WebAdMIT Predictive Insights integrates advanced machine learning directly into your admissions workflows, leveraging WebAdMIT data to provide likelihood-to-enroll scores, as well as supplemental metrics and explainable AI insights—such as Likelihood Factor, Decile, and the features most impacting each student’s prediction—right within the applicant record. This session shows how to use those predictions to tier applicants, focus on high-yield segments, and trigger targeted outreach using WebAdMIT exports. Learn how continuously updated predictions shift your strategy from reactive to proactive, helping you make faster, more confident yield and enrollment decisions across undergraduate and graduate programs. This solution complements WebAdMIT Holistic Insights, which evaluates applicants’ quality and alignment with your institution’s mission, providing a combined, comprehensive view of each candidate.