Data-Driven Strategies Reshape Financial Aid in Higher Education

Higher education institutions are increasingly utilizing data and analytics to optimize financial aid strategies. According to EDUCAUSE, more than 60 percent of colleges now rely on analytics to guide their financial aid decisions. This marks a significant increase from just 39 percent a decade ago, reflecting a shift towards data-informed approaches.

The landscape of higher education has changed dramatically over the past decade. Demographic shifts and evolving student behaviors have led to a decline in enrollment yield rates, which represent the percentage of admitted students who choose to enroll. From 2007 to 2017, the average yield rate fell by nearly 15 percentage points, reaching just 34 percent in 2017. Since then, these rates have remained in the low 30s, prompting institutions to rethink their financial aid offerings.

Data-Driven Approaches to Financial Aid

In response to these challenges, colleges and universities have increased tuition discounting. For the 2024-25 school year, private, nonprofit institutions offered discounts averaging more than half the cost of tuition. As institutions face declining state and federal funding, they are turning to data-driven models to maximize the impact of limited scholarship dollars and improve enrollment outcomes.

Modern data analytics enable colleges to adopt a more scientific approach in determining how various levels of financial aid affect student enrollment decisions. Jack Neill, vice president of product at the data analytics firm HelioCampus, notes that traditional models often emphasize academic merit and financial need. However, deeper analysis can uncover additional factors influencing a student’s likelihood to enroll.

Colleges are now considering a broader range of variables, including a student’s academic background, family income, and geographical proximity to campus. Neill explains, “You have to look at it from different angles to figure out where the sensitivity is to the price.” Insights derived from these analyses can help institutions tailor their aid offers more effectively.

Case Studies in Financial Aid Optimization

Data-driven strategies also enable administrators to simulate various scenarios before implementing policy changes. Ronald Nowaczyk, former president of Frostburg State University, stated, “We were somewhat flying blind before. Now, working with HelioCampus, we have some what-ifs and modeling that is helping us move forward.”

Frostburg State University has developed statistical models to maximize both net tuition revenue and enrollment yield. These models consider factors such as admitted students’ GPAs, residency, and proximity to campus, allowing the university to adjust scholarship allocations in response to financial need fluctuations, particularly following the 2008 recession.

The ability to conduct what-if analyses enables institutions to respond swiftly to changing conditions that may affect student aid. Neill pointed out a common challenge: “You’re in a meeting, someone comes up with an idea or question, and then no one knows the answer.” With data readily accessible, decision-making processes can be streamlined.

For instance, data analytics can clarify how policy changes may 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,’” Neill emphasized. “You want to be able to dive into it and really analyze it.”

Experts stress that while data plays a crucial role in shaping financial aid strategies, leadership ultimately defines institutional goals and parameters. To facilitate these analyses, many colleges are working to dismantle data silos across their campuses.

At Connecticut State Community College (CSCC), the integration of data from 12 campuses aims to create a cohesive financial aid strategy following a system merger in 2023. Steve McDowell, vice president for financial aid services and Title IV compliance, described how CSCC developed aid optimization models based on enrollment projections and student financial need.

The analysis incorporates external variables, such as state policy changes, while also factoring in student success metrics like persistence, credit completion, and enrollment trends. McDowell noted that the college now conducts daily data analyses to monitor application and enrollment patterns, allowing the team to identify emerging shortfalls early.

Targeted financial aid efforts have yielded positive outcomes. In fall 2024, a record 75 percent of students applied for financial aid, with 61 percent receiving grant aid that covered their tuition and fees entirely. Despite these advancements, McDowell emphasized the importance of maintaining the human element in financial aid processes. CSCC has established financial aid offices at each major campus and collaborates with local high schools to encourage the completion of the Free Application for Federal Student Aid (FAFSA). Student feedback on the financial aid experience is also gathered through surveys.

As the higher education landscape continues to evolve, the necessity for data-informed decision-making is likely to persist. “You can’t just make a change now [without data],” Neill asserted. “Every year, institutions are going to have to be making critical decisions.” The shift towards data-driven financial aid strategies highlights the importance of leveraging analytics to navigate the complexities of enrollment in today’s educational environment.