WebAdMIT Predictive delivers a likelihood-to-enroll score powered by machine learning—right inside WebAdMIT. With actionable insights at a glance, you can prioritize outreach, strengthen yield, and make smarter decisions without adding complexity.
WebAdMIT Predictive uses advanced machine learning models to analyze historical application and enrollment data, uncovering patterns that indicate likelihood to enroll. These predictions appear directly inside WebAdMIT, with additional interpretation fields for added context. Institutions can export predictive scores and related data to SIS systems or reporting environments, ensuring seamless integration with existing workflows.
Upgrading to Liaison Othot, an AI-powered analytics platform, provides access for even deeper predictive power. Going beyond score-viewing in WebAdMIT with advanced tools for variable impact analysis, cohort dashboards, segmentation, trend monitoring, and scenario modeling gives you the foresight to plan yield and enrollment with confidence.
WebAdMIT Predictive models analyze historical WebAdMIT application and enrollment data to identify patterns tied to enrollment likelihood.
No. WebAdMIT Predictive offerings are available without association-level buy-in.
Predictions are displayed as a likelihood-to-enroll score inside WebAdMIT, along with supplemental interpretation fields for added context.
Yes. Predictive scores and related data fields can be exported to SIS systems or reporting environments for seamless integration.
WebAdMIT Predictive gives you more than a score, it delivers foresight. By leveraging machine learning, you’ll identify enrollment likelihood, prioritize outreach, and optimize yield strategies with confidence. Make smarter decisions that shape the future of your class, without guesswork or complexity.
