Colleges and universities today face pressure to make every tuition and financial aid decision count. Demographic shifts and changing student behaviors have driven down the yield rate — the share of admitted students who enroll — at the average college by nearly 15 percentage points from 2007 to 2017, falling to just 34 percent in 2017, according to Brookings. Since then, yield rates have remained in the low 30s.
At the same time, institutions have escalated tuition discounting. According to the National Association of College and University Business Officers, private, nonprofit institutions offered discounts for the 2024-25 school year averaging over half the cost.
Recruiting investments and increasing aid come even as institutions grapple with declining state and federal funding and skepticism at the value of a college degree. To stretch limited scholarship dollars, colleges are turning to data-driven models that help boost yield and support institutional goals.
Modern data analytics in higher education allow colleges to take a more scientific approach to this, using predictive modeling and statistical analysis to inform how different levels of aid will impact students’ decisions to enroll.
FORECASTING AND OPTIMIZING AID
Jack Neill, vice president of product at the data analytics firm HelioCampus, said traditional aid-awarding models often focus on academic merit and financial need, but deeper analysis can reveal less obvious factors. Institutions are analyzing more variables, from academics and family income to a student’s hometown and engagement with recruiters, to predict how likely a given student is to enroll and how that probability changes with different aid offers.
“You have to look at it from different angles to figure out where the sensitivity is to the price,” he said. “What we found is it’s not often the traditional kind of metrics. It’s other things, like how far they are from the university, or where they went to high school, lots of different characteristics.”
Analytics-driven aid strategy also enables administrators to run simulations and forecast scenarios before making policy changes.
“We were somewhat flying blind before,” Ronald Nowaczyk, then-president of Frostburg State University in Maryland, said in a case study. “Now, working with HelioCampus, we have some what-ifs and modeling that is helping us move forward.”
In Frostburg’s case, the university built several statistical models aimed at maximizing both net tuition revenue and enrollment yield by experimenting with data on admitted students’ GPAs, residency, proximity to campus and aid offers. Those models helped Frostburg adjust its scholarship allocations to address a spike in unmet financial need and an enrollment decline following the 2008 recession.
What-if analyses can also help institutions respond quickly to changes that may impact student aid.
“A really common problem is, you know, you’re in a meeting, someone comes up with an idea or question and then no one knows the answer, and ... a week later, you come back and, 'oh, well, here's the answer,'” Neill said. “Having the data at your fingertips allows you to cut down on the decision cycles.”
For example, he said, with changing federal policy on student loans, data and forecasting can take some of the guesswork out of how policies will impact current and prospective students.
“You don’t want to have an anecdote where someone says, ‘Oh, we don’t have a lot of graduate students that rely on PLUS loans,’” he said. “You want to be able to dive into it and really analyze it.”
KEEPING A HUMAN TOUCH
Experts emphasize that data-informed does not mean data-dictated. Predictive models can surface insights and likely outcomes, but leadership still sets goals and parameters.
To make these analyses possible, institutions are working to break down data silos across campus. At Connecticut State Community College (CSCC), this meant merging data from 12 campuses to create a unified financial aid approach following a system merger in 2023.
“By analyzing trends across the legacy institutions, CT State developed aid optimization models based on enrollment projections and student financial need,” Steve McDowell, vice president for financial aid services and Title IV compliance at Connecticut State, wrote in an email to Government Technology.
Their analysis incorporated external variables like FAFSA simplification efforts and state policy changes to anticipate impacts on the CSCC system. It also included student success data like student persistence, credit completion and enrollment trends. McDowell said the college now conducts daily data analyses to track year-over-year application and enrollment patterns, allowing the team to spot emerging shortfalls early.
McDowell said targeting of financial aid has helped improve student outcomes: In fall 2024, a record high 75 percent of students applied for financial aid and 61 percent paid $0 in tuition and fees through grant aid alone.
However, McDowell said the CSCC system takes care to ensure the human element of aid remains intact. Its colleges house financial aid offices at each major campus, partner with local high schools and agencies to encourage FAFSA completion, and ask for feedback on students’ financial aid experience through surveys.
Looking forward, as the higher education landscape continues to shift, the need for data-informed decision-making isn’t going anywhere.
“You can't just make a change now [without data],” Neill said. “Every year, institutions are going to have to be making critical decisions.”
