About the Episode
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About the Episode:
Jamie Boggs and Timothy Davis dig into how machine learning can finally help enrollment marketers measure what truly matters. They explore how machine learning models—specifically linear regression, boosted regression, and chi-square analysis—can untangle the complex relationship between recruitment activities and actual enrollment outcomes. With real-life data from Element451’s CRM, this episode demystifies machine learning for higher ed pros looking to back their strategies with data—not just intuition.
Key Takeaways
- Machine learning in higher ed marketing helps identify which recruitment tactics have the most impact on student enrollment outcomes.
- Boosted regression models reveal which staff-initiated student interactions—like emails and text messages—most strongly correlate with enrollment.
- Email is not dead: Delivered emails ranked as the most impactful activity for influencing enrollment in the dataset.
- Data quality matters: A larger, more granular dataset at the user level gave stronger insights than aggregated school-level data.
- Understanding outputs like AUC and p-values helps enrollment leaders determine if machine learning results are trustworthy and actionable.
- Tools like Snowflake, SQL, and Python are increasingly accessible for running ML models—even without a computer science background.
- Actionable analytics empower enrollment teams to scale what’s working and pivot away from what’s not—no more guessing.
Machine Learning and Enrollment Marketing—What This Webinar Answers
What is machine learning, and why does it matter for enrollment marketing?
Machine learning is essentially a pattern recognition tool. In this episode, Tim Davis breaks it down with humor and clarity, comparing it to how humans learn language or develop habits. Machine learning isn’t about fancy buzzwords—it’s about recognizing repeatable behaviors that drive outcomes. For enrollment marketers, it helps answer the age-old question: Which of our actions actually lead to enrolled students?
Machine learning models can process large datasets and identify trends that would take humans weeks to uncover. Whether it’s emails, texts, phone calls, or event invites, these models can assign real value to each activity and predict outcomes like enrollment likelihood.
What machine learning models were covered in the episode?
Three major types of analysis were discussed:
- Linear Regression – This classic model finds relationships between variables. For instance, how does the number of emails correlate with student enrollment?
- Boosted Regression (Random Forest) – A more complex method that tweaks variable weightings to find the most predictive model. This was the strongest model in the demo.
- Chi-Square Test – Used to identify statistically significant relationships between categorical variables, such as favorite courses and student behaviors.
The team showed how each model uses inputs (like emails sent, texts delivered, appointments scheduled) to predict outputs (like whether a student enrolls or not), making the math digestible—even fun—with analogies like coffee consumption and pizza slices.
What did the real-life dataset reveal?
The Element451 dataset, which included nearly one million rows of user-level data, produced strong results. The key finding? Delivered emails had the highest impact on enrollment, followed closely by delivered text messages. Conversations and staff ownership (being assigned a staff member) also mattered. Surprisingly, newer features like appointments and phone calls ranked lower—likely due to limited use across institutions rather than lack of value.
In contrast, a smaller, school-level dataset (178 rows) yielded poor results with low predictive accuracy. The takeaway? Granular, student-level data is essential for meaningful insights.
How accurate was the machine learning model?
Using evaluation metrics like the area under the curve (AUC) and confusion matrix, the team found an AUC of 0.80—meaning the model was 80% accurate in predicting enrollment outcomes. This gave them confidence that their insights were actionable.
They also ran a chi-square test to validate the model’s findings, confirming a strong statistical relationship between variables like email delivery and enrollment.
How can institutions use these insights operationally?
Once a model identifies the most impactful activities, institutions can:
- Double down on high-performing channels like email and SMS.
- Reassess underperforming strategies or those with low usage (like appointment scheduling).
- Communicate results to leadership to drive investment in successful tactics.
- Train AI tools like ChatGPT to interpret results and craft presentations for stakeholders.
Machine learning doesn’t just give you data—it gives you confidence to act. Whether you're making the case for new tools, tweaking communication plans, or measuring campaign ROI, these insights make your work measurable, defendable, and scalable.
Connect With Our Co-Hosts:
About The Enrollify Podcast Network: The EduData Podcast is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!
Some of our favorites include Generation AI and The Higher Ed Geek.
Enrollify is produced by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.
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