Why Colleges Use Data Analytics to Inform Financial Aid Strategy

The high sticker price of college today means few families can afford to pay the full cost of attendance at most private and many public universities without receiving some sort of financial assistance or taking out loans. Colleges use financial aid awards to help close the affordability gap. The vast majority of institutions also rely on tuition revenue to stay solvent. Through a process known as “financial aid optimization” (or FAO), colleges work to provide a viable path to and through higher education for students from all backgrounds and economic circumstances while balancing that effort against the challenge of maintaining their own financial viability.

For years, colleges have experimented with various scholarship and financial aid programs, often relying on past practices and good intentions, but lacking a sophisticated, data-driven approach to strike the right balance. Given the magnitude of the budget for financial aid, schools understand that they need something better than a gut-level understanding of how financial aid strategies affect enrollment, retention, graduation, and net revenue. However, most still lack the in-house expertise required to gain the deeper insights they need.

When supported by rigorous modeling and data analysis, FAO is one of the most powerful tools colleges and universities have at their disposal to diversify enrollments and generate the revenue they need to operate. It’s no surprise then, that schools have increasingly turned to companies like EAB who are experienced at applying comprehensive data analytics to the discipline of FAO.

EAB helps colleges understand how different types and amounts of financial aid will help meet a range of enrollment goals. We examine dozens of variables, including the unique competitive environment in which each institution operates, to better estimate the likely results a given aid strategy will achieve. The use of such data and statistical tools gives schools better information to inform their strategy while minimizing the effect of human bias. It also brings a new word into the conversation: algorithm.

To some, even the mention of the word triggers a knee-jerk response and a suspicion that the use of algorithms might automatically reduce the amount of scholarship funding offered. In reality, their use is more likely to encourage increases in funding as a means to increase enrollment and net tuition revenue.

The construction of the algorithms, or models, used matters a great deal, and they must function within the framework of a school’s mission, goals, and financial aid policies. Since schools are looking to serve students from all backgrounds, the models EAB uses are constructed in a way that demonstrates to university officials how many more or fewer students from various ranges of financial need and academic achievement are likely to enroll based on changes to the policy framework. The implications from any potential policy change can be estimated more accurately when we utilize all available data and employ the best statistical methods, but those methods only inform, not dictate the policy.

We at EAB feel strongly that the modeling gives us a strong guide as to direction, but that initial analysis is always paired with human guidance and input from school leaders to deliver the best policy.

That is how we ensure the FAO strategies we develop in collaboration with schools reflect their institutional values. When advising a partner school on financial aid strategy, EAB applies measures assuring that:

1. the policy aligns with overall institutional goals,
2. all relevant consequences are explored and shared, and
3. the policy produces the results that meet and balance as closely as possible all of an institution’s enrollment goals.

EAB works with each school to develop a strategy framework that helps them understand every factor used to determine the amount of suggested aid that might be offered to each student. We also model for them all estimated outcomes, including total enrollment and net tuition revenue, as well as predicted performance against equally important diversity metrics, academic profile, financial needs, and other characteristics each institution deems important.

Applying algorithms to optimize financial aid improves the precision and accuracy of decision making as compared to more simplistic methods. EAB’s data-informed approach to financial aid optimization helps illuminate and avoid erroneous assumptions that are rooted in many cases in a fundamental misunderstanding of what drives the choices that today’s students make about whether and where to attend college. In the process, we help partner institutions offer more students the opportunity to earn a college degree, regardless of their economic circumstances.