Product Feature: Financial Aid Optimization

Mike Keane May 10, 2024 Mike Keane SVP, Modeling + Client Strategy Persona The Influential and Resilient Energizer

Product Feature: A Behind-the-Scenes Look at Financial Aid Optimization

Achieving new student enrollment goals each year is a critical priority. But in today’s landscape of intensifying competition and pressures to keep costs affordable, it’s a delicate balancing act to bring in an incoming class that meets both academic and financial objectives.

That’s where Financial Aid Optimization can be a game-changer. By leveraging advanced quantitative modeling, we can help you strategically deploy your financial aid resources to shape your class profiles and yield targets. However, the process behind developing these optimized aid strategies can seem like a black box from the outside.

We aim to bring full transparency to financial aid optimization. Whether you’re new to this area, in the midst of an RFP process, or already partnered elsewhere, we believe in pulling back the curtain on our approach. Here’s a behind-the-scenes look at how Carnegie could partner with you to optimize your institution’s financial aid strategy.

Data Collection and Discovery

The first phase focuses on gathering and validating your institution’s data on the current enrollment cycle and at least two prior Fall enrollment cycles. Our experienced team, including former directors of admission and enrollment leaders from universities like yours, will request a series of key student-level factors like:

  • Academic records: High school GPA, transfer GPA, test scores, ratings from applications.
  • Financial aid data: FAFSA and CSS Profile dates (if applicable), federal and institutional methodology aid eligibility indicators (SAI).
  • Geodemographic details: Address, state, home country, gender, ethnicity.
  • Engagement data: First source, campus visit patterns, attendance at virtual events, and the timing of these behaviors.

While collecting data, we’ll also spend considerable time learning about your institution’s goals, policies, recent enrollment trends, and past financial aid strategies through meetings and documentation reviews. Think of us as consultants aiming to fully understand your context before prescribing solutions.


After we collect all the data, our team will upload it to our Analytics environment. This enables us to combine and analyze several years of your admissions and financial aid data together.

In the Analytics tool, we can easily filter the data to look at specific groups of students, like separating out by their academics, demographics, finances, and behaviors. This helps us understand how different factors impacted enrollment across your previous cycles.

We can’t move forward with modeling and developing the aid strategy until we’ve completed this full data validation step. Ensuring we have an accurate picture of your institution’s historical data is critical before building projection models.

Yield Modeling

With a solid foundation of your data, goals, and constraints, our team of analysts (with advanced degrees and specialized training in areas like economics and data science) will develop predictive models to understand what factors most influence a student’s likelihood to enroll or stay enrolled at your institution.

We’ll design yield models that calculate a student’s probability of enrolling based on their academics, demographics, finances, and behavior. We’ll share the findings with you, allowing you to validate that the patterns make sense based on your own experiences. Getting this yield modeling right is crucial since it drives the next phase of developing your financial aid strategy.

Aid Strategy Development and Implementation

This is where the real optimization happens. Using our custom software tools, we’ll simulate different financial aid spending strategies to model their potential impact on yield and net tuition revenue for your institution.

In our Strategy Tool, we’ll load the yield model which then allows us to rapidly test different aid award scenarios for your prospective students. As we adjust the institutional aid amounts for individual students, their probability of enrolling is automatically recalculated to show the overall impact on your whole class.

You can see the details behind each scenario, including the proposed awards for the same students from your most recent actual class. This enables you to evaluate the trade-offs between different awarding approaches.

We’ll work closely with your team, making iterations until we land on a final financial aid strategy that best meets your priorities—whether that’s shaping the class profile, increasing net revenue, or balancing those goals.

Once the strategy is approved, we can implement it for your institution through a few different methods:

  • Award Matrix Implementation: We’ll build matrices that automatically assign gift aid amounts using rules and calculations based on factors like GPA, financial need, academic program, geography, and any other student attributes prioritized by your institution.
  • Individualized Implementation: We’ll calculate personalized aid awards for each of your prospective students aligned with the agreed-upon strategy. We’ll then return customized data files that can be easily imported into systems like Slate or your student information system. Learn more about individualized modeling.
  • Linear Equation Model: To provide more granular award recommendations compared to a matrix approach without the complexity of individualized files, we’ll design a linear equation. It combines a baseline award amount with specific dollar values tied to your priorities like academics, need level, region, and more.

We’ll customize the implementation approach to fit what works best with your institution’s systems and resources.

Tracking, Interventions, and Cycle Assessment

However, developing a great strategy is just the start. Throughout your active recruitment cycle, we’ll analyze how actual applicant behaviors are tracking compared to the assumptions in the model.

If material changes occur—like a larger than expected applicant pool or stronger admit volume in higher GPA groups—we may recommend timely interventions or awarding tweaks to keep you on target.

After the cycle wraps up, we’ll do a full assessment breaking down what worked well, opportunities for improvement, and how to plan an even better approach for the next year. Key aspects include:

  • Comparing the actual makeup of your admit pool vs what was projected
  • Analyzing any groups that ended up enrolling at much lower or higher rates than expected
  • Identifying patterns that could be temporary or longer-term trends to watch

This continuous cycle of analysis, iteration, and refinement is what allows us to keep delivering results for your institution, year over year.

Case Study: LMU Financial Aid Optimization

For a real-world glimpse at our process in action, consider our partnership with Loyola Marymount University. Over 15 years of partnership, LMU experienced a 40% increase in first-year enrollment and a 115% rise in first-year net tuition revenue, all while enhancing selectivity and maintaining discount rates. Additionally, the diversity of the student body improved, with historically underrepresented first-year students increasing by 10%. These changes helped LMU become a top 100 institution in national rankings​.


Financial aid optimization is both an art and a science. The science comes from our best-in-class data practices, secure platforms, and quantitative expertise. But the artistry is in taking that analysis and translating it into strategies that align with your distinct priorities as an institution.

If you’re interested in exploring financial aid optimization for the first time, reevaluating your current approach, or just beginning an RFP process, Carnegie can help. Reach out and start a conversation about how financial aid optimization can help you shape your class and meet your enrollment goals.

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