Understanding Predictive and Holistic Insights in WebAdMIT

Overview

Liaison’s predictive and holistic insights for WebAdMIT, powered by Othot’s data science models, introduce a more comprehensive way to evaluate applicants by combining predictive analytics with holistic review. Together, these models provide two distinct but complementary perspectives: one grounded in historical enrollment patterns and one focused on the full context of an applicant’s profile.

This article provides foundational information to help you understand predictive and holistic insights within WebAdMIT. These features are available only to participating institutions and CASs. For guidance on how to view, configure, and use these scores within WebAdMIT – including adjusting weights and incorporating scores into workflows – refer to the corresponding help articles on accessing and using scores.

Understanding Predictive and Holistic Models

Predictive and holistic models are designed to answer different – but equally important – questions about your applicant pool.

• The holistic model evaluates each applicant based on the full scope of their application, including academics, experiences, attributes, and context. It is designed to reflect how well an applicant aligns with your institution’s mission, values, and priorities.
• The predictive model uses historical data and patterns to estimate the likelihood that an applicant will matriculate if admitted. By analyzing trends across previous cycles, it provides insight into expected enrollment behavior and yield.

Individually, each model offers value. Together, they provide a more complete view – combining qualitative alignment with data-informed projections.

Utilizing Predictive & Holistic Insights Together

Together, predictive and holistic models provide complementary perspectives on each applicant, helping institutions prioritize outreach, tailor decision strategies, and better understand the composition and needs of their overall applicant pool.

High Scores in Both Models

• Applicants who score highly in both models demonstrate strong alignment with institutional priorities and a high likelihood of matriculation. They often exhibit the preparation, experiences, and attributes programs seek, and they are more likely to enroll if admitted.
• Because these applicants support both mission alignment and enrollment outcomes, they are typically strong candidates for admission and may be prioritized accordingly.

Low Scores in Both Models

• When both the predictive and holistic scores are low, these applicants may show less alignment with institutional priorities and also have a lower likelihood of matriculation. 
• In a competitive pool, this group is often a lower priority for both admission and outreach, allowing programs to focus time and resources on applicants with stronger overall alignment.

Low Predictive, High Holistic

• Applicants with high holistic scores continue to reflect many of the qualities an institution values, including strong preparation and alignment with mission. However, a lower predictive score suggests they may be less likely to matriculate – often because they are highly competitive candidates with multiple attractive options.
• Many programs still choose to admit these applicants, recognizing their potential value to the class. At the same time, institutions must weigh the likelihood of lower yield against the importance of attracting high-potential candidates, making this a deliberate and strategic trade-off.

High Predictive, Low Holistic

• When the predictive score is high but the holistic score is lower, the data suggests a strong likelihood of matriculation, but less alignment with the program’s qualitative criteria or mission.
• These applicants may have fewer competing offers and therefore enroll at higher rates when admitted, which can be appealing from a yield perspective. However, institutions must balance these benefits against potential impacts on class composition, student success, and long-term goals.

Availability & Getting Started

Predictive and holistic insights may be made available through your CAS association partner. Institutions that choose to participate work with Liaison to implement and configure the models within WebAdMIT. As part of onboarding, institutions receive guidance on the setup process, including data access, configuration requirements, and institution-specific customization options.

For more information about availability or implementation options, contact a member of your account team.