The Dual Challenge of Access and Overwhelm

What They Say vs What They Do: The Financial Aid Disconnect

Every spring, institutions invest enormous energy in perfecting their financial aid strategy. And understandably so—survey data consistently ranks “financial aid” and “scholarship amount” as top factors in student decision-making.

But here’s the paradox: when we look at the actual enrollment outcomes, financial aid amounts don’t rise to the top in predictive power.

At enroll ml, our machine learning models analyze real-time behavioral signals across the funnel. After three years of deployment and tens of millions of student records processed, we’ve found that financial aid amount is rarely a top predictor of enrollment.

Why the disconnect?

Because what students say—and what they do—often diverge. Behavioral science calls this the gap between stated preference and revealed preference. In enrollment, it plays out like this:

• Students say aid was the reason they didn’t enroll.
• But the data shows their engagement dropped long before the award.
• Or that they received a generous award—but never responded at all.

Financial aid is a stimulus, not a decision driver. The key isn’t the size of the award—it’s what the student does after receiving it. Did they log in? Schedule a meeting? Submit documents? Go dark?

That’s where the real insight lives. enroll ml decodes not just whether a student received aid, but how they responded. And those reactions—fast engagement, lingering silence, last-minute activity—are far more predictive than the dollar amount.

As you guide your team through yield season, don’t rely solely on what students say. Pay attention to what they do. That’s where the enrollment decision actually happens.
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If you're interested in further conversation on this topic, I invite you to watch a recording of an enroll ml webinar from August, 2024, How Predictive Is Financial Aid, with Matt Osborne from Ardeo Education. https://www.crowdcast.io/c/aid

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The future of college admissions isn’t just about generating more applications or refining marketing strategies. It’s about how we engage with students once they’re in the funnel. Institutions that focus on personalized, proactive outreach—targeting the right students with meaningful engagement—will outperform their peers in yield, retention, and student satisfaction.

Those who don’t? They’ll risk becoming marketing machines, focused more on response rates than building real relationships with students.

The Risk of a Marketing-Driven Future:
If proactive outreach isn’t widely adopted, admissions will continue to drift toward a purely marketing-driven profession. Response rates, open rates, and click-throughs will dominate the conversation, but real student engagement will fall by the wayside.

Colleges will see more applications, but fewer enrollments. Counselors will spend more time crafting mass emails and less time guiding students through critical decisions. This shift will erode both enrollment outcomes and counselor morale.

Technology Is the Key to Making the Shift:
Some institutions hesitate to adopt proactive outreach because they believe it’s too hard to scale. But the reality is, technology makes this shift not only possible but essential.

Machine learning and advanced data analytics allow institutions to identify students who are most engaged—those who are genuinely considering enrollment but need counseling to make a decision. This level of insight isn’t something you can achieve with pivot tables, no matter how much you love Excel.

Unless you’re prepared to build an in-house team of data scientists (which most institutions aren’t), the key is to partner with platforms that can provide these insights at scale. Tools like enroll ml can decode complex engagement patterns, helping counselors focus on the students who matter most.

Why Some Institutions Will Resist (and Why They’ll Fall Behind):
Many institutions will resist this shift, clinging to the belief that batch emails to groups of 40 to 400 students are “proactive enough.” The assumption is that personalized outreach isn’t scalable, especially for larger public institutions.

But here’s the truth: proactive outreach doesn’t have to apply to every student. The key is focusing on those who are clearly interested and engaged. Institutions that adopt this mindset—whether large or small—will outperform their market position because they’re connecting with the right students at the right time.

The Mindset Shift Admissions Needs:
The biggest barrier to adopting proactive outreach isn’t technology—it’s mindset. For too long, admissions teams have relied on the same formula: subject line, information dump, call to action. This approach may generate responses, but it doesn’t build relationships.

The institutions that thrive in the coming years will be those that identify who to engage with and approach those students with thoughtful, personalized communication. It’s not about volume—it’s about precision.

Proactive outreach isn’t just a trend—it’s the new standard for admissions success. Colleges that embrace personalized, data-driven engagement will see higher yields, stronger counselor morale, and better student outcomes. Those that don’t? They’ll be stuck chasing response rates in an increasingly crowded market.

The future of admissions belongs to those who engage with purpose. Are you ready?

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Admissions teams are drowning in data. From FAFSA submissions and campus visits to email opens and application statuses, there’s no shortage of signals to track. But here’s the problem—not all signals mean what we think they do. And when teams rely too heavily on traditional indicators, they end up making decisions based on assumptions rather than facts.

It’s time to move from guesswork to precision—and that starts with understanding the difference between surface-level signals and deeper behavioral patterns.

The Problem with Over-Indexed Signals:
Many admissions processes are built around major milestones: FAFSA submissions, campus visits, and application completions. These are easy to track and feel like strong indicators of student interest. But are they?

Take FAFSA submission for example. For those of us who’ve been in admissions for a decade or more, we know that where a student lists your institution on the FAFSA matters. If you were listed first, that likely meant high interest. If you were listed fifth, your chances of yielding that student dropped significantly. But now that we can’t see FAFSA rankings, many teams treat all submissions the same—ignoring the nuances behind the data.

The Role of Gut Instinct (and Its Pitfalls):
Without clear, nuanced data, many counselors fall back on gut instinct. And while intuition plays a role, it’s inherently biased. A counselor might assume that a student who hasn’t applied for financial aid isn’t serious about enrolling—because they wouldn’t have enrolled without aid. But that assumption doesn’t hold true for every student. Relying on personal bias leads to missed opportunities and inconsistent outcomes.

The Hidden Signals We’re Missing:
What admissions teams often overlook are the subtle, behavioral signals that truly indicate student intent. It’s not just whether a student opened an email—it’s how quickly they opened it after receiving it, how many links they clicked, and whether they engaged again after a follow-up. The time windows between student actions matter far more than a simple yes/no.

For example:

• A student who visits your website multiple times within a week but hasn’t yet applied might be more interested than a student who submitted an application months ago but hasn’t engaged since.
• A student who opens your emails consistently but doesn’t respond may still be deeply engaged—they just need the right kind of outreach.

These patterns aren’t easy to spot manually. Machine learning tools like enroll ml analyze these time windows and engagement rates, surfacing students who are truly ready for proactive outreach.

From Data-Aware to Data-Driven:
Most admissions teams believe they’re data-driven. In reality, they’re data-aware. They track numbers, run reports, and create lists—but the depth of analysis needed to uncover true enrollment signals isn’t something you can get from pivot tables in Excel.

To make the leap from guesswork to precision, admissions teams need to pull the highest level of data analysis possible. This isn’t about working harder—it’s about working smarter, using tools that can decode complex behavioral patterns and guide counselors to the right students at the right time.

The Bottom Line:
Moving from guesswork to precision isn’t just about improving enrollment outcomes—it’s about freeing counselors from the endless cycle of assumptions and giving them the tools to engage meaningfully with the students who need it most. It’s time to stop guessing and start understanding.

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