Tips to Update Your Financial Aid Strategy Using Advanced Analytics

It’s the summer of 2018, and a key priority in Enrollment Management is “how to best shape my 2019 Freshman and Transfer class to meet my goals (NTR, discount rate, census enrollment, academic profile, demographic profile)”.

Now that it’s 2021, most institutions have made some drastic changes to their enrollment processes, either temporarily or permanently.

While the priorities of 2018 are still relevant today, there are many other factors and obstacles that now exist, including questions like,

  • How will the pandemic influence my 2021 class?
  • Will 2022 be more like 2019 or more like 2021?
  • How do I get my discount rate back to an acceptable level?
  • How can I enroll a more diverse class?
  • What’s the right combination of in-person and virtual events?
  • How do we admit students with a test blind or test-optional policy?
  • How do we award institutional dollars based on the admission change?
  • How do we account for the upcoming FAFSA change?
  • How do we plan for the demographic cliff that is on its way?

That is an exhausting list of questions. We’ll focus on those tied to financial aid because now is the time when higher education institutions are looking to update their strategies.

Let’s first start with a few questions that will drive us towards the answers needed to implement the best financial aid strategy for 2022.

  • Are there any planned changes to how you determine your academic ranks for merit scholarships? A
  • Are there any planned changes to how you award financial aid based on need?
  • What are your goals for your 2022 class (NTR, discount Rate, institutional aid spend, academic profile, demographic profile)?

With those answers in mind, let’s walk through a Q&A to help you make the best decisions for your 2022 financial aid strategy.

Where do I start with formulating or updating a financial aid strategy?

The first step is to understand where your current academic ranks correlate with your new ones. By doing this, we can understand where the best opportunity is within those ranks. By analyzing where your current and new ranks overlap, we can determine count, likelihood to enroll, and average spend to set the stage to determine where there would be an opportunity to improve awarding.

Would it be helpful to understand how much to increase or decrease awards and what impact it may have on your goals?

Of course, it would. Using advanced analytics to analyze your previous awarding strategy can uncover some great opportunities to increase awards for better yield or decrease awards without impacting enrollment and, ultimately, increasing NTR.

By understanding financial aid sensitivity not only with the entire student population but down to an individual student, small changes to a strategy can significantly impact enrollment, NTR, discount rate, etc.

At Othot, we review your previous and current financial aid strategies and perform a sensitivity analysis. We take those steps to understand where there is an opportunity to increase or decrease awards depending on your institution’s goals.

Once we’ve established the criteria, either using a previous academic rank or a new one and how it maps to the prior year, the next step is to understand what aid value and impact level are most beneficial for entire enrollment classes or specific subsets at the different academic ranks.

By reviewing the previous strategy, we can look for opportunities to increase awards that will positively impact enrollment or decrease certain awards without compromising enrollment, thus resulting in improved aid allocation and, ultimately, NTR.

Can we test this strategy to understand the potential outcomes and how it will impact enrollment in specific populations?


At Othot, we can run a financial aid matrix simulation to test the new strategy and provide outputs for almost any population or a specific metric. Since there may be different opportunities to increase and decrease awards, running several simulations provides an understanding of those outcomes and which ones align best with your goals.

Without an advanced analytics solution, you can test out the scenarios by applying yield rates of past years based on awards and academic ability, although there is more assumption built into that answer.

With an advanced analytics solution like Othot, how can we trust these outputs following the pandemic and other external influences?

The answer is two-fold.

Since the modeling is built on more than just one year (typically two or three), this curbs some of the extreme outlying data, thus providing an accurate output. By using more than just a few variables in the financial aid leveraging model, we understand how specific students and groups react to changes in awarding and price.

For example, during the pandemic, there were mostly virtual interactions. The modeling can find opportunities for awards, providing insights into the impact of the virtual interactions compared to prior years as well as how additional variable values changed due to virtual events.

What about the outstanding questions that weren’t directly answered?

Demographic Cliff

With the demographic cliff, there may be opportunities to increase the likelihood to enroll among subsets of a population, such as specific underdeveloped geomarkets, first-generation, underserved communities, or simply bolstering your current market share. Regardless of the area of opportunity, through detailed analysis, we can review and determine the population with the best opportunity and the financial aid award with the greatest impact.

Upcoming FAFSA changes

By now, you’ve heard of the upcoming FAFSA changes. While they are not taking effect until July 2023, it is important to start planning now.

There are two significant ways the changes may impact your enrollment. How positive or negative the impact will depend on your specific population of students.

  • With the new FAFSA rules, the EFC is changing to SAI (Student Aid Index). Under the previous rules, the EFC was reduced significantly when a family had multiple children in college. Under the new rule and using the SAI, this will not occur. The change will mainly impact middle to upper-income families who have multiple children in college.
  • The other change is associated with the Pell Grant. More students should be eligible, and the change should help lower-income families that may not have been eligible previously.

This may be a good time to analyze those specific populations to better understand their real sensitivity to financial aid and what changes could make to your NTR, discount rate, and institutional aid spend.

What’s next?

While this may seem like the end, and it may be from a strategy perspective for this year, the tactical portion is just starting.

With advanced analytics and machine learning, we can understand how financial aid awards impact individual students who have been admitted and make real-time decisions on awards.

If you want to know who could use an additional aid, what appeals work for which students, or how to increase enrollment in certain populations through aid, let the model do the hard part and figure out who is most impacted and by how much. Then, you can focus on other pressing enrollment management questions.