How Analytics in Higher Education Is Reshaping Enrollment Management

In theory, undergraduate enrollment leaders now have access to more data than ever about their students and applicants. However, that data is often decentralized, and many decision makers lack the resources to decipher it and use it strategically.

A recent webinar featuring enrollment experts from Liaison and Florida Institute of Technology (“Florida Tech”) demonstrated how the use of predictive and prescriptive analytics in higher education is helping institutions overcome these obstacles to institutional and student success. You can now watch Overcoming Enrollment Bottlenecks With Analytics: How to Turn Data Into Strategy to hear their insights into creating a roadmap for turning data into strategy and strategy into results.

The long-anticipated “demographic cliff” is here. As Florida Tech Executive Director of Admissions Mike Perry noted, the number of graduating high school seniors in the U.S. is projected to drop by 700,000 over the next five years. That’s a 19% decrease in the traditional college-bound population.

Add to that the geopolitical shifts affecting international enrollment and the ongoing volatility in financial aid policy, and it’s clear: Institutions can’t rely on old playbooks. They need smarter, more targeted strategies to attract, enroll, and retain students.

Predictive analytics in higher education uses historical data to forecast future outcomes, such as which students are most likely to enroll or persist. Prescriptive analytics goes a step further, recommending specific actions to influence those outcomes.

Liaison Client Success Director Chris Rose pointed out that such technologies are more common than you may realize. “Everyday things that we interact with, such as Amazon and Netflix, predict or prescribe actions. ‘Here's your favorite show, here's one just like it.’ Or, ‘You just bought this thing. You might want this thing too.’” In higher ed, the same general principles can guide enrollment decisions. For example, Liaison’s Othot AI platform applies these tools to help institutions make data-informed decisions across the entire student lifecycle, from inquiry to graduation.

With fewer students applying, institutions are feeling the squeeze. But as Liaison Regional VP (and former Executive Vice President and Chief Enrollment Officer at the University of the Cumberlands) Jonathan Shores said, fewer applications don’t have to mean fewer enrollments.

“The instinct is to panic,” Shores said. “But it’s actually an opportunity to rethink how we engage students.”

Instead of casting a wide net, institutions can use analytics to focus on the students most likely to enroll. Personalized digital experiences, including customized landing pages and targeted, personalized text messages, can deepen engagement. When paired with predictive tools, these strategies help enrollment teams prioritize their outreach and maximize yield.

Financial aid is one of the most powerful levers for influencing enrollment decisions, but only if it’s used strategically.

Too often, institutions take a one-size-fits-all approach to aid packaging. The result? Overspending on students who would have enrolled anyway and underfunding those who needed just a little more to say yes.

With Othot, for example, Florida Tech was able to model the impact of aid adjustments on individual students. As Perry explained, “In the past, maybe you've just given $2,000, $3,000, or $5,000 aid adjustments. But Othot can predict that’s actually what’s going to help a student is $1,000. As a result, you don't have to outspend your aid budget.”

The platform also helps manage appeals more effectively by offering data-backed recommendations for how much aid to offer in response to student requests.

Retention is often overlooked in enrollment strategy, but it shouldn’t be. As Shores put it, “Retention is the silent driver of enrollment health.”

Shores talked about using analytics to identify students at risk of dropping out before they even enroll. By analyzing data such as LMS logins, it’s possible to flag students who might need extra support.

At Florida Tech, his team used Othot to increase retention from 79% to 84% in just one year by using pre-enrollment modeling to guide early interventions.

“It's all because we're able to track the students we thought were at risk and follow them once they arrive. As soon as we see them start struggling a little bit, we can apply appropriate resources from different departments. It could be academic support, it could be student affairs, it could be the financial aid office. That's what the modeling shows us. And then we're able to focus attention on them, apply resources in order to make them successful. If they're successful and happy, they’re more likely to stay. If they're not successful and they're not engaging, they're more likely to leave.”

For institutions just beginning their analytics journey, the key is to start small and stay focused.

Don’t try to do everything at once. Pick one or two areas, like yield or aid optimization, and build from there.

Predictive and prescriptive analytics aren’t just buzzwords; they’re essential tools for navigating today’s enrollment challenges. By helping institutions focus their efforts, allocate resources more effectively, and support students more holistically, these tools turn data into strategy and strategy into results.

Whether you’re looking to boost yield, optimize aid, or improve retention, the message from the Overcoming Enrollment Bottlenecks With Analytics: How to Turn Data Into Strategy was clear: the future of enrollment management is data-driven, and the time to start is now.