It's Not An Export

After attending the NACAC conference, it’s clear that AI is a hot topic in admissions. Session after session showcased AI’s potential to automate tasks like reading transcripts and sending mass emails. But that conversation misses a critical point: AI’s real power is in guiding human decisions, not just replacing human tasks.

While automation has its place, the true value of AI in admissions lies in its ability to help us understand which students need attention, where they are in their decision-making journey, and how best to approach them. AI can analyze patterns in behavior and engagement, providing admissions teams with a data-driven way to make smarter decisions.

Rather than scripting emails or processing applications, AI can empower counselors with insights on when and how to engage with individual students. It gives humans the tools to personalize their outreach, make timely decisions, and enhance relationships—all with greater confidence.

The future of AI in admissions isn’t about turning the work over to machines. It’s about using machine learning to make better, more informed human decisions. By expanding the AI conversation beyond automation, admissions leaders can unlock new opportunities to connect with the right students at the right time, ultimately improving enrollment outcomes.

Next week, I’ll give examples of how AI can help you pinpoint which students to focus on for more impactful engagement.

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The next level of leveraging data to have a competitive advantage is not available as an export in your CRM. Indeed, an appropriate and intentional embrace of machine learning and artificial intelligence to identify and respond to student behavioral patterns is the next level.

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Customizing Predictive Models for Unique Institutional Enrollment Patterns

Recently, enroll ml shared a whitepaper, “Harnessing the Power of Machine Learning in Enrollment Management” - sharing deep insights made available from our work studying thousands of behavioral markers of nearly one million students at dozens of institutions.  Previous posts revealed three key breakthroughs and the impact of time-based behaviors, another element discussed in the whitepaper is the uniqueness of each institution’s ml model.

Every institution has unique enrollment patterns, and traditional profile-based prediction models often fail to capture these nuances. At enroll ml, we've found that customized predictive models, tailored to each institution's specific data and strategic priorities, can significantly enhance enrollment outcomes.

Uniqueness of Institutional Enrollment Signals

Machine learning models can be tailored to capture the specific dynamics of each institution's student population, far surpassing the capabilities of traditional models. Our analysis shows that engineered features derived from raw data through sophisticated transformations and combinations can provide nuanced insights into student behaviors and enrollment patterns.

For instance, the importance of different predictive features varies significantly across institutions. One institution might find that applicant characteristics are highly predictive, while another might prioritize academic interest data or system interaction behaviors. By customizing predictive models to align with these unique patterns, institutions can achieve more accurate predictions, optimize resource allocation, and implement more effective enrollment strategies.

Advantages of Customization

- Personalized Predictions: Machine learning models can reflect the specific enrollment dynamics of each institution, providing more personalized and accurate predictions than traditional methods.

- Enhanced Resource Allocation: Customization allows institutions to allocate resources more effectively, focusing efforts on high-impact activities and strategies.

- Nuanced Insights: Machine learning models offer detailed insights into student behaviors and decision-making processes, enabling institutions to develop more sophisticated enrollment strategies.

- Adaptability to Changing Environments: Machine learning models continuously learn and adapt to new data, ensuring they remain relevant and effective in evolving enrollment environments.

Conclusion

Customizing predictive models to reflect the unique enrollment patterns of each institution offers a significant advantage in optimizing enrollment outcomes. By leveraging machine learning for personalized predictions and strategic resource allocation, institutions can stay ahead in the competitive landscape of higher education.For comprehensive insights and strategic recommendations on how to customize predictive models for your institution, download our full whitepaper, "Harnessing the Power of Machine Learning in Enrollment Management."