Strategic Yield Management in Higher Education: How AI Transforms Enrollment and Financial Aid

Higher education is in the middle of a profound shift. Institutions across the country are grappling with demographic changes, heightened competition, budget pressures, and increasing expectations for personalization and value. As a result, colleges and universities must now rethink the strategies they’ve relied on for decades.

With those challenges in mind, Liaison’s new whitepaper, Strategic Yield Management: Using AI to Optimize Enrollment, Retention, and Financial Aid Decisions, explains how institutions can leverage modern AI-powered tools to overcome obstacles and take advantage of emerging opportunities to enhance enrollment success for schools and students alike.

As the whitepaper illustrates, the environment in which enrollment teams operate has fundamentally changed. Demographic shifts have led to a shrinking number of high school graduates, reducing the size of the prospective student pool and intensifying competition among institutions. The “birth dearth” makes it increasingly difficult to meet enrollment goals, especially as colleges also work to maintain both accessibility and financial sustainability.

Financial aid offices are under their own pressure: limited budgets, growing student need, and the challenge of balancing affordability with net tuition revenue. Institutions must carefully distribute aid to attract and retain students while managing limited budgets, all while ensuring that access remains a cornerstone of their mission.

Operational hurdles adding to the complexity include slow or fragmented processes, overwhelmed staff, and students who expect quick, personalized support. Traditional approaches simply can’t keep up with these demands.

The central thesis of the whitepaper is clear: AI is fundamentally reshaping how institutions can—and should—manage enrollment, retention, and financial aid. Specifically, tools like Liaison’s Othot AI platform and Liaison Search empower institutions to anticipate student behavior, tailor financial aid strategies, and streamline engagement across the entire student journey.

Rather than relying on intuition or static historical data, enrollment leaders can now turn to predictive and prescriptive analytics that help answer:

• Which students are most likely to enroll?
• What aid packages drive the best outcomes?
• Which recruitment interventions actually move the needle?

These tools don’t just improve accuracy—they allow teams to make smarter, faster decisions with confidence.

One of the most powerful AI applications outlined in the report is predictive modeling. By analyzing academic, demographic, engagement, and historical data, AI forecasts student enrollment probabilities and evaluates the likely effectiveness of financial aid strategies.

This insight helps institutions:

• Identify students most likely to enroll.
• Understand what drives their decisions.
• Allocate aid resources efficiently.
• Maximize both enrollment and net tuition revenue.

The real value lies in eliminating guesswork. Instead of relying on static aid grids or broad segmentation, institutions can design aid strategies that align directly with student needs and institutional goals.

AI-driven segmentation goes far beyond basic academic or demographic groupings. Institutions can divide their applicant pool based on likelihood to enroll, financial need, or merit and then create more personalized aid packages for each segment. This helps optimize limited resources and attract a diverse, academically strong class.

The takeaway? Personalization isn’t optional anymore. It’s the key to increasing yield and meeting institutional objectives.

The whitepaper also emphasizes the importance of scenario-based analysis, an AI capability that allows institutions to model the real-world impact of potential decisions. Enrollment teams can explore how increasing aid for certain groups, shifting dollars across segments, or adjusting recruitment strategies could influence outcomes.

This empowers leaders to plan proactively instead of reactively, which is an essential advantage in a dynamic enrollment environment.

The white paper highlights two compelling institutional examples: Indiana University of Pennsylvania (IUP) and Oklahoma State University (OSU).

IUP’s Retention Turnaround

Using Othot’s financial aid sensitivity analysis, IUP improved its aid strategy and saw major gains, including:

• A 90% fall-to-spring retention rate.
• An increase in first-year fall-to-fall retention from 71% to 75% in just one year.

OSU’s Record Enrollment

With the help of Othot’s scenario modeling, OSU surpassed its enrollment goal two years early, enrolling 5,030 new students and increasing representation of first-generation, out-of-state, honors, and Native American students.

These case studies show the measurable impact of adopting AI-powered yield strategies.

Modern student search is no longer about buying lists—it’s about finding the right prospects using AI-driven insights, segmentation, and omnichannel marketing. Precision targeting, appended data, improved SEO, and targeted messaging help institutions reach the students most aligned with their programs and mission.

By integrating Othot and Liaison Search, institutions create a seamless flow of insights, from identifying prospects to optimizing aid to supporting retention. This produces stronger outcomes, more efficient use of resources, and more personalized student engagement.

Ultimately, AI-powered analytics give institutions a more holistic, strategic, and proactive way to meet enrollment targets, allocate aid effectively, and improve retention.

Download Strategic Yield Management: Using AI to Optimize Enrollment, Retention, and Financial Aid Decisions now for deeper insights and actionable recommendations for a smarter, more strategic approach to enhancing enrollment, financial aid, and retention.