The Myth of Predictive Models

For the last decade, predictive modeling has been treated as the gold standard of modern enrollment strategy.

If you had a model, you were ahead of the curve. If you had a vendor producing that model for you, even better. Predictive modeling became synonymous with being data-driven, future-focused, and operationally savvy.

But there’s a growing problem no one talks about.

Predictive models tell you who is likely to enroll. They do not tell you what to do next.

And in admissions, that's the entire point.

Predictive Models vs. Proactive Strategy

We’ve seen hundreds of institutions rely on predictive tools to build outreach lists, target travel, or shape financial aid. The logic is familiar: if we can rank students by likelihood to enroll, we can concentrate our resources on the right group.

In theory, it makes sense. In practice, it’s incomplete.

Prediction isn't the hard part anymore. Prioritization is.

Because the real challenge isn't identifying likely students—it's deciding which actions will make a difference, and when.

Let me give you an example.

A student has a high predicted likelihood to enroll. Great. But she hasn’t opened a single email in three weeks, hasn’t logged into the portal, and skipped a recent admitted student event. Her predicted score may still be high—but her actual behavior is telling a different story.

Does your model notice? Does your team know? And more importantly—does anyone act?

This is the gap between prediction and precision. And it’s where most enrollment teams are flying blind.

What We Learned from the “Model-Heavy” Years

At enroll ml, we work with institutions that already have predictive scores baked into their CRMs or reporting dashboards. And what we hear, over and over again, is that those scores are informative—but not actionable.

They describe the funnel, but they don’t direct daily work.

They estimate probability, but they don’t flag risk.

They assign a label, but they don’t recommend a next step.

That’s not a flaw in the models. It’s a misalignment between the output of the model and the needs of the team. Counselors don’t need to know the odds. They need to know who to call and why.

They don’t need predictions. They need clarity.

What Comes After Prediction

At enroll ml, we took a different approach. We don’t try to predict intent in a vacuum. We model behavior over time—what students do, when they do it, and how it aligns (or doesn’t) with expected outcomes.

Instead of assigning a single probability, we surface proximity scores that reflect a student’s current momentum toward enrollment. We flag mismatches—students whose engagement level no longer matches their stage.

And most importantly, we translate those insights into daily, ranked priorities that counselors can actually use.

We’re not trying to replace your model. We’re replacing the guesswork that happens after the model.

A Better Question for June

As you enter the most critical weeks of the enrollment cycle, ask yourself this:

Does our strategy rely on knowing who's likely to enroll?

Or does it help our team do the right thing for the students who haven’t yet decided?

Because if your system can’t tell the difference between a drifting depositor and a surging non-deposit, you’re not working with a model. You’re working with a mirage.

Prediction has value. But prioritization drives action.

And action is what enrollment comes down to.