Data-Driven Financial Aid Strategies Reshape Higher Education

Higher education institutions are increasingly leveraging data analytics to refine their financial aid strategies. More than 60 percent of colleges and universities now utilize data-driven approaches to inform their financial aid decisions. This marks a significant increase from just 39 percent a decade ago, as reported by EDUCAUSE. As tuition costs rise and state funding decreases, institutions are under pressure to make every financial decision impactful.

The challenges faced by colleges are compounded by shifting demographics and evolving student behaviors. From 2007 to 2017, the average yield rate—the percentage of admitted students who choose to enroll—dropped by nearly 15 percentage points, reaching just 34 percent in 2017. Consequently, many institutions have resorted to increased tuition discounting. For instance, private, nonprofit institutions are projected to offer discounts averaging over half the cost for the 2024-25 school year.

Leveraging Data for Strategic Decisions

Faced with declining yields and skepticism about the value of a college degree, institutions are turning to sophisticated data-driven models to better allocate limited scholarship funds. These models employ predictive analytics and statistical analysis to assess how different financial aid levels can influence students’ enrollment decisions.

According to Jack Neill, vice president of product at the data analytics firm HelioCampus, traditional aid-awarding models typically focus on academic merit and financial need. However, deeper analysis can uncover less obvious factors affecting student choices. Institutions are now considering a broader range of variables, including academic performance, family income, and even geographical factors such as a student’s hometown distance from campus.

“You have to look at it from different angles to figure out where the sensitivity is to the price,” Neill explained. The insights gained from these analyses enable administrators to run simulations and forecast potential outcomes before implementing policy changes. As Ronald Nowaczyk, former president of Frostburg State University, noted, “We were somewhat flying blind before… Now, working with HelioCampus, we have some what-ifs and modeling that is helping us move forward.”

Frostburg has developed several statistical models aimed at maximizing both net tuition revenue and enrollment yield. These models have facilitated adjustments to scholarship allocations in response to unmet financial needs, particularly following the 2008 recession.

Improving Student Outcomes Through Targeted Aid

Rapid response to changing financial aid needs is another advantage of data analytics. Neill highlighted that having instant access to data can significantly reduce decision-making cycles. For example, institutions can quickly analyze the effects of proposed policy changes on current and prospective students.

At Connecticut State Community College (CSCC), the integration of data from 12 campuses following a system merger in 2023 has led to a unified financial aid strategy. “By analyzing trends across the legacy institutions, CT State developed aid optimization models based on enrollment projections and student financial need,” said Steve McDowell, vice president for financial aid services and Title IV compliance at CSCC.

The college’s approach includes external variables such as state policy changes and student success data, tracking metrics like persistence and credit completion. As a result, CSCC has seen a record high of 75 percent of students applying for financial aid, with 61 percent covering their tuition and fees entirely through grant aid.

While data-driven strategies enhance decision-making, experts emphasize that these models should inform rather than dictate actions. Leadership still plays a key role in setting institutional goals and parameters. McDowell noted that CSCC maintains a human-centered approach in its financial aid process. Each major campus has dedicated financial aid offices, and the institution actively collaborates with local high schools to promote FAFSA completion, gathering feedback from students on their financial aid experiences.

As the higher education landscape continues to evolve, the reliance on data-informed decision-making is expected to grow. “You can’t just make a change now [without data],” Neill stressed. Institutions will need to make critical decisions each year, supported by robust analytical insights to navigate the complexities of student financial aid.