The New Admissions Dream Team: Humans and AI

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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When we look back on the last couple years in college admissions, the issue of staff morale will be one of the hallmarks of the post-pandemic era.  That's why I was excited to host this important conversation with Elizabeth Kirby about how - specifically - she was paying attention to this issue for her team.

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The full conversation is available on demand: https://www.crowdcast.io/c/morale

The admissions landscape is changing, and so should the way colleges shape their class. As we've discussed, relying on FAFSA submissions as one of the primary indicators of student interest has become increasingly unreliable. Delays, procedural submissions, and the elimination of FAFSA Position data mean that admissions teams are left with less information to accurately assess a student’s intent to enroll. But this change also presents an opportunity. Schools that adopt a more holistic, data-driven approach—focused on engagement metrics rather than just financial aid forms—will be better positioned to build a strong, committed class.

Moving Beyond FAFSA: Building a Holistic Strategy

It’s time for admissions teams to move beyond relying so heavily on FAFSA data and embrace a more comprehensive approach to shaping their admit pools. This means shifting the focus to behaviors that more accurately reflect student intent, like campus visits, virtual interactions, and communication frequency. The students who actively engage with your institution throughout the admissions process are the ones most likely to enroll, and their behavior should guide your outreach strategy.

This isn’t to say that FAFSA data should be ignored—it’s still a critical part of the financial aid process. However, it should be considered alongside a broader set of engagement metrics to provide a more complete picture of student interest. Admissions teams can use predictive analytics to combine engagement data with financial aid information, giving them the ability to make better-informed decisions about which students to focus on and when.

Using Machine Learning and Predictive Analytics to Drive Decisions

Machine learning and predictive analytics are game-changers in the admissions space. These tools allow schools to analyze vast amounts of data—including student behavior, communication patterns, and interactions with admissions counselors—and make predictions about which students are most likely to enroll. This is particularly valuable in an era where FAFSA data alone is no longer a sufficient indicator of commitment.

For example, predictive models can assign engagement scores to students based on how often they visit your website, attend events, and respond to personalized outreach. By focusing your efforts on students with higher engagement scores, your admissions team can prioritize those most likely to enroll, saving time and resources while improving your yield.

Predictive analytics also allow admissions teams to identify students who may be at risk of "melt"—as well as those who initially show interest but disengage later in the process. By tracking when and how student behaviors change, schools can intervene at the right moment, ensuring that at-risk students remain engaged and ultimately matriculate.

Proactive Engagement: Shaping the Right Class

To effectively shape your class, it’s critical to adopt a proactive engagement strategy. This means not only identifying which students are most likely to enroll but also engaging with them in a way that fosters deeper connections with your institution. Personalized communication is key here. Students are far more likely to respond to messages that address their specific interests, concerns, and goals.

For example, rather than sending generic emails, admissions teams can tailor messages to individual students based on their demonstrated interests—such as their preferred academic programs, extracurricular activities, or campus visit experiences. This kind of targeted communication shows students that your institution values them as individuals, which can significantly increase their likelihood of enrolling.

It’s also important to engage students early in the admissions process—well before FAFSA submission is even possible. By building relationships early on and maintaining consistent communication, you can keep students engaged throughout the entire admissions cycle, reducing the risk of losing them to other schools.

enroll ml Examples: Schools Leading the Way

Several institutions are already embracing this more holistic approach to admissions, using data and technology to guide their decision-making processes. These schools have seen improved enrollment outcomes by focusing on student engagement rather than relying solely on FAFSA submissions.

One example is a mid-sized liberal arts college that integrated predictive analytics into its admissions strategy. By tracking student interactions with their website, participation in virtual events, and frequency of communication, the college was able to identify students who were highly engaged but had not yet submitted their FAFSA. By focusing outreach on these students, the college increased its yield and reduced the number of students who disengaged late in the process.

Another example comes from a large public university that implemented a machine learning model to prioritize high-value students based on their engagement scores. By shifting resources away from students who showed little engagement, the university was able to better allocate its admissions efforts and ultimately build a stronger incoming class.

A New Path Forward

The days of relying so heavily on FAFSA data to shape your admit pool are over. Today’s admissions landscape demands a more comprehensive, engagement-driven approach that takes into account the full spectrum of student behavior. By embracing predictive analytics, machine learning, and proactive engagement strategies, colleges can build stronger, more resilient classes and ensure they are focusing on the students most likely to enroll.

In this new approach, FAFSA remains an important tool—but it is just one piece of the puzzle. Admissions teams that prioritize engagement and use data to guide their decisions will not only improve yield but also create more meaningful connections with students, leading to better outcomes for both the institution and the students themselves.

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