Measuring What Matters: How Machine Learning Can Illuminate Enrollment Impact

This month’s EduData Webinar Series unpacked one of the most pressing challenges in higher ed marketing: How do we measure what really moves the needle in student recruitment? Hosted by Erin Fields of Enrollify, with Element451’s data experts Jamie Boggs and Timothy Davis leading the charge, this session tackled how machine learning can help us connect the dots between staff-initiated recruitment efforts and actual enrollment outcomes.

If you missed the live webinar, here’s your fast-track recap of the big ideas, key models, and actionable insights.

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Key Takeaways from the Webinar

1. Machine Learning Can Decode What’s Actually Working

Machine learning isn’t just a buzzword—it’s a powerful tool that can detect patterns in massive datasets. In this webinar, the team explored how enrollment marketers can use simple machine learning models to analyze student engagement data and determine which tactics—like emails, texts, and appointments—are actually influencing enrollment.

“The model is only useful if the results are actionable and adopted operationally.” — Timothy Davis

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2. Boosted Regression Delivered the Strongest Results

Among the models explored—linear regression, boosted regression, and chi-square tests—boosted regression emerged as the most accurate for this use case. Using real-life Element451 data, the model showed an 80% accuracy rate in predicting student enrollment based on engagement activities.

Why it works:

• It weighs variables automatically (like emails vs. texts) based on impact.
• It’s ideal for binary outcomes (did the student enroll: yes or no).
• It’s scalable and runs inside systems like Snowflake, making it more accessible to non-data scientists.

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3. Email is Still King

Contrary to what some may believe, delivered emails had the highest predictive value for enrollment in the model—surpassing even SMS messages and appointments. Here's how the rankings shook out based on feature importance:

• #1: Emails Delivered
• #2: SMS Delivered
• #3: Staff Ownership (a student being assigned a staff member)
• #4: Conversations Initiated by Staff
• Lower-impact activities included appointments and phone calls, likely due to lower adoption in the dataset—not lack of effectiveness.

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4. User-Level Data Beats School-Level Aggregates

A key comparison in the webinar was between two datasets:

• A user-level dataset with nearly 1 million rows of student interactions
• A school-level dataset with only 178 rows

The results were clear: The user-level dataset produced far more reliable and insightful predictions. The smaller school-level data failed to yield a statistically significant model, underscoring the importance of granularity and scale in machine learning.

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5. Understanding ML Outputs = More Confident Decision Making

The team walked through common outputs from machine learning models and how to interpret them:

• AUC (Area Under the Curve): 0.80 in the test, showing strong predictive ability
• Confusion Matrix: Helped evaluate the number of correct vs. incorrect predictions
• Feature Importance Scores: Revealed which recruitment efforts mattered most
• Chi-Square P-values: Confirmed statistically significant relationships between activities and enrollment

Even if you're not a data scientist, knowing how to interpret these outputs can help you champion data-backed decisions on your campus.

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6. Machine Learning Can Help You Advocate for Change

Whether you’re trying to:

• Justify the ROI of a new appointment scheduling feature,
• Increase investment in texting campaigns,
• Or prove that personalized staff assignment improves yield…

Machine learning gives you the evidence to back your gut instinct.

And for those feeling overwhelmed by the math? Tim and Jamie reminded us that tools like ChatGPT can help interpret model outputs and even craft explanations for leadership.

“Don’t say, ‘Look at my p-values.’ Use that data to make smart, simple, strategic asks.” — Jamie Boggs

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Final Thoughts

This webinar was a masterclass in using data to make enrollment marketing smarter. The team didn’t just talk theory—they showed real models, shared actionable takeaways, and made the case for embracing AI and machine learning as practical, accessible tools for recruitment strategy.

If you’re serious about measuring what matters—and making confident, data-informed decisions—this is the kind of work that will shape the future of your enrollment efforts.

Listen to the replay here.