Your Melt Strategy Can't Wait For July

Every summer, around the same time, admissions teams across the country go into rescue mode. They pull a list of deposited students, sort them by last contact date, and start dialing, texting, or emailing. The goal is simple: make sure those students actually show up. But by the time July arrives, the damage has often already been done.

We tend to think of melt as a moment—a decision a student makes late in the summer to go elsewhere. But that’s not really how it happens. Melt is not a sharp turn. It’s a slow drift. And it usually starts much earlier than we want to admit.

In most cases, the early signs of melt are behavioral, not procedural. A student who was once quick to respond now takes a few days. Someone who logged into the portal every week in March suddenly disappears in May. A student registers for orientation but skips the pre-session checklist, and no one notices.

These are the patterns that matter. But they’re almost impossible to spot manually. The volume of students is too large. The signals are too subtle. And most CRMs simply weren’t built to detect them.

That’s the heart of the problem.

When melt begins to show up in your reports—when deposits drop or no-shows spike—you’re seeing the results of something that started weeks ago. By that point, you’re reacting to outcomes you no longer have much control over.

Roosevelt University confronted this dynamic head-on last year. Like many schools, they were seeing deposit growth, but also higher melt than expected. And with counselors stretched thin, they couldn’t chase every deposited student equally. So they used enroll ml to reframe the question.

Instead of asking, “Which students have deposited?” they started asking, “Which students are disengaging, even though they’ve deposited?”

That’s a very different kind of analysis—and one most systems aren’t equipped to handle. But that’s exactly what enroll ml was designed to do. The platform surfaces behavior-action mismatches—students whose visible stage (like a deposit) doesn’t match their actual behavior (like a long gap in engagement).

This allowed the Roosevelt team to stop treating every deposit the same. They reallocated counselor time toward the students whose behaviors were signaling risk—even if they still looked solid on paper.

The impact was immediate. As Thomas Ott, Executive Director of Admissions, put it, “Until we started using enroll ml, we never had a good way to track drift when a student was moving away from us.” They weren’t chasing ghosts anymore. They were intervening early, and with purpose.

That’s the shift more teams need to make. Because the idea that you can wait until July to start thinking about melt just doesn’t hold up anymore. By then, students who are at risk have often already made up their minds. And if you're only beginning to look for disengagement once the summer hits, you’re already behind.

The most effective melt strategies don’t start when students begin to disappear. They start when their behavior first begins to change. That means tracking subtle indicators, responding to shifts in engagement, and giving your team the tools to see what's happening before it becomes a problem.

At enroll ml, we don’t believe in treating all deposits the same. And we don’t think counselors should be left to guess. Every morning, we flag the students who are sending signals that something’s off—students who need a nudge, a check-in, or a reset.

Not all of them will melt. But if you wait to act until you know for sure, you’ll miss your window.

If you're building a melt plan, it shouldn't start in July. It should start now.