Women in Enrollment Management

Admissions directors rarely say it outright, but here’s the truth: time is their team’s most valuable resource, and it’s slipping through their fingers.

Counselors are overwhelmed, managing thousands of applicants and admitted students while being tasked with creating personal connections. The tools they’ve relied on for decades—mass emails, blanket event invites, and “list-of-the-day” call strategies—aren’t just outdated. They’re wasteful. Too much effort is spent chasing students who won’t enroll, leaving less time for the ones who will. The result? Counselors burn out, enrollment numbers suffer, and the mission of providing a personalized experience falls flat.

Holistic admissions is often heralded as the solution, but it’s usually confined to the pre-decision phase. We use it to see the "whole student," balancing GPAs, extracurriculars, and personal stories to craft diverse and dynamic classes. But here’s the problem: once the decisions are made, holistic admissions gets tossed aside, and teams revert to one-size-fits-all strategies. This misses the bigger opportunity—to use the same principles to manage counselors’ time and attention after decisions go out.

What would it look like to extend holistic admissions into post-decision engagement? It starts with data. Every admitted student sends signals—portal logins, email clicks, event RSVPs—that reveal their level of interest and intent. Machine learning tools can analyze these signals in real time, showing which students are actively deciding, which are drifting away, and which need a nudge. Armed with these insights, admissions directors can direct their counselors to focus where their time will have the greatest impact.

This isn’t about replacing human connection with algorithms; it’s about empowering counselors to work smarter, not harder. When machine learning handles the heavy lifting of sorting through thousands of data points, counselors can focus their energy on meaningful, relationship-driven work. That means personalized calls to the students who need it most and tailored follow-ups that address real concerns—not wasted hours chasing disengaged students or following up on irrelevant metrics.

The benefits are clear. Counselors feel more effective and less overwhelmed, leading to better morale. Yield improves because the right students get the attention they need. And the institution wins by making the most of its resources without sacrificing the personal touch that makes holistic admissions so powerful.

Admissions leaders must redefine holistic admissions as more than just a tool for selecting students—it’s the framework for everything that comes after. Managing time and attention post-decision isn’t just a tactical adjustment; it’s a philosophical one. If institutions truly believe in treating students as individuals, they must embrace a smarter, data-driven approach to yield. Because when it comes to enrollment, precision isn’t optional—it’s everything.

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Holistic admissions has been championed as the antidote to reductive, numbers-driven decision-making. By considering the full breadth of an applicant’s background, experiences, and potential, institutions proudly craft classes that reflect their values. But here’s the uncomfortable truth: most colleges abandon the principles of holistic admissions the moment the acceptance letter is sent.

Why do we stop listening?

Admissions teams pour resources into understanding who students are before they’re admitted, but once decisions are made, they often revert to outdated, scattershot approaches to yield management. Every admitted student is treated the same, despite the clear signals they send about their level of interest, intent, and engagement. This approach wastes time, alienates students, and undermines the entire purpose of holistic admissions.

It’s time to redefine holistic admissions—not as a process that ends with an offer, but as a commitment to respecting and responding to each student’s journey through their decision-making process. Machine learning makes this possible, revealing the hidden patterns in post-admission behaviors like event attendance, email replies, and portal activity. These signals hold the key to understanding where students stand and what they need to convert.

Instead of flooding students with generic outreach, machine learning enables admissions teams to prioritize their efforts where they matter most: with high-potential students who are actively deciding, or those who need encouragement to stay engaged. By ignoring these signals, institutions squander their counselors’ time and lose valuable opportunities to connect with students who might otherwise enroll.

Redefining holistic admissions demands that we extend its principles beyond the decision. The same care used to admit students should be applied to yield them. If institutions truly believe in treating students as more than data points, they must commit to engaging admitted students with the same intentionality—powered by machine learning and the respect for individual signals it enables. Anything less is a betrayal of the values holistic admissions claims to champion.

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Introduction
In today’s competitive enrollment landscape, yield and retention hinge on data-driven precision. Traditional methods often fall short, relying on subjective judgment and missing critical engagement signals. enroll ml changes this by identifying behavior-action mismatches, helping admissions leaders spot opportunities and risks in real time for measurable impact.

Understanding Behavior-Action Mismatches
Behavior-action mismatches occur when engagement doesn’t align with commitment indicators, such as:

• High Engagement, No Commitment: High-potential students show interest but haven’t reached a commitment milestone, signaling a prime yield opportunity.
• Low Engagement, Commitment Met: Students with commitment indicators but low engagement, often an early indicator of melt risk.

Challenges with Traditional Methods
For many admissions teams, sifting through high application volumes and disconnected engagement data can feel overwhelming. Traditional models track disconnected behaviors that may correlate with enrollment but don’t provide enough depth to detect patterns. This often leads to delays or missed opportunities for timely intervention.

How enroll ml Closes the Gaps
enroll ml’s machine learning and Outcome Optimization Theory detect behavior-action mismatches in real time, transforming how admissions teams prioritize engagement:

• Multi-Marker Modeling: By analyzing an interconnected range of behaviors—engagement depth, timing, communication patterns—enroll ml precisely identifies high-potential, high-impact students.
• Automated Flagging and Prioritization: enroll ml flags behavior-action mismatches daily, eliminating time spent on manual record review. This prioritization empowers teams to re-engage at-risk students before melt occurs, turning data analysis into focused, real-time action.

Strategic Impact of Addressing Behavior-Action Mismatches
By automating prioritization, enroll ml can reduce counselor time spent on administrative tasks by up to 30%, freeing them to focus on high-impact engagement with top-yield prospects.

• Enhanced Yield: With immediate engagement, admissions teams lift yield by focusing on high-engagement students nearing commitment.
  • Case in Point: Within days of launch, Columbia College Missouri identified and converted high-potential students previously overlooked, directly boosting yield by X%.
• Reduced Melt: Proactive outreach to at-risk students with commitment indicators minimizes last-minute withdrawals.
  • Case in Point: Roosevelt University significantly reduced melt by re-engaging low-engagement depositors flagged by enroll ml, leading to X% decrease in summer melt.

Takeaway for Enrollment Leaders
Monitoring and responding to behavior-action mismatches directly impacts final yield outcomes. With enroll ml, admissions teams continuously identify actionable signals, strategically unlocking yield and reducing melt. Ready to turn data into yield? With enroll ml’s behavior-action insights, admissions leaders can unlock hidden yield potential and reduce melt, leading to X% gains in yield and X% reductions in time spent on administrative tasks.

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