Software As A Helping Profession

Staff Morale

Admissions Team

Burnout

Counselor Satisfaction

Enrollment Management

June 13, 2024

minutes read.

Over the past two years, we've all been talking about the staff morale crisis in college admissions. Rather than rehashing the same points we've all heard, I want to share a story about someone truly exceptional: Chase.

Chase is one of the most dedicated and fascinating individuals I've had the pleasure of working with in admissions. Over the years, I hired him for several roles, and each time, he exceeded even my highest expectations. He was the kind of person for whom you create positions.

Naturally, hiring him multiple times means I also lost him a few times. One notable instance was after he helped us implement our CRM, which started from scratch. The experience was so fulfilling for him that he decided to pursue a second bachelor's degree in computer science. His first degree was in education, but after a brief teaching stint, he realized it wasn't his calling.

When asked why he chose such a different academic path, he explained that it wasn't as different as it seemed. He always wanted to be in the helping professions, and implementing the CRM showed him how software could improve people's daily lives. In this case, it automated a lot of manual tasks that were dragging down morale and productivity. He envisioned many other scenarios where software could make life better and decided to pursue computer science to do just that.

This story came to mind when I left campus for the first time in my adult life to work for enroll ml. Yes, we're a data science company with an amazing machine learning engine. Yes, we help colleges identify the best opportunities to increase enrollment. All of that is true, and we can share more about it anytime.

More importantly, at least for me, is that we make the experience of being an admissions counselor better. By directing counselors to students who want or need to hear from them, we enable more productive, positive, and proactive conversations. Our data models handle the rejections, allowing counselors to focus on making a difference in students' lives.

Chase was right, and I see it every day in my role at enroll ml. When done well, software can indeed make people's daily lives better.

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Teege Mettille

Higher education professional with experience in admissions, enrollment, retention, residence life, and teaching. After working on six different college campuses, I'm excited to be consulting with a wide variety of institutions to better meet enrollment targets.I have been fortunate to serve as President of the Wisconsin

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Featured blog posts

Unlock Yield by Addressing Behavior-Action Mismatches in Enrollment

October 30, 2024

minutes read.

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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