At NACAC, there was plenty of discussion around using AI to automate tasks, but few touched on AI's ability to track and predict where students are in their decision-making process. This is one of AI’s most powerful, yet underutilized, capabilities in admissions. Understanding what stage a student is at—whether they’re still exploring options, actively researching, or ready to apply—enables admissions teams to tailor their communications, making every interaction more meaningful.

The Challenge: Understanding Student Decision-Making

Admissions teams often rely on broad, linear models to track where students are in the funnel—prospect, inquiry, applicant, admit, and enrolled. While these categories are helpful, they don’t account for the complexity of individual student behavior. Some students may linger in the exploration phase for months, while others fast-track to applying after a single campus tour.

Knowing exactly where a student stands isn’t always clear with traditional methods. You might rely on surface-level cues—did they submit an inquiry form? Have they opened emails?—but these only provide part of the story. This is where AI can step in to give a clearer, more nuanced picture.

AI's Ability to Predict the Student Journey

AI excels at processing large amounts of data quickly and spotting patterns humans might miss. By analyzing behavior like how often a student visits your website, what pages they view, and when they open emails, AI can track subtle signals that indicate their progress through the decision-making process. AI models can even predict future behaviors, offering insights into where a student is headed and what kind of interaction they’ll need next.

Here are some ways AI can help map the student journey:

• Behavioral Analysis: AI looks at the frequency and nature of student interactions—such as visiting specific academic program pages or registering for events—to estimate how far along a student is in their journey.
• Predictive Staging: Based on past behaviors, AI can predict a student’s next steps. For example, if a student starts spending more time on financial aid or housing pages, AI might suggest they’re getting ready to make a decision, prompting your admissions team to send a personalized outreach.
• Engagement Milestones: AI helps track when students hit certain engagement milestones, like signing up for an info session or downloading an admissions brochure. These milestones help admissions teams know when it’s time to switch from general engagement to more specific, personalized communication.

Why Predicting Decision Stages Matters

Understanding where a student is in their journey allows admissions teams to deliver the right message at the right time. A student still exploring their options might need a broad overview of campus life, while a student deep into the decision phase might appreciate personalized insights into scholarships or financial aid opportunities.

Instead of bombarding every student with the same set of emails or phone calls, AI enables admissions teams to focus their efforts more strategically. This targeted approach can make the student experience feel more personal and relevant, which in turn increases the chances of them moving forward in the process.

A More Tailored Approach to Engagement

AI doesn't just help predict where students are—it also helps you communicate with them more effectively. A student in the early stages of exploration likely won’t respond well to an email asking them to apply now, but they might engage with content highlighting the academic programs or campus life they’re interested in. Conversely, a student nearing the end of their decision-making process will likely appreciate direct guidance on deadlines, financial aid, or next steps for enrollment.

By meeting students where they are, you’re not just improving your chances of conversion—you’re improving their experience with your institution. A more tailored, relevant approach to engagement shows students that you understand their needs and are there to support them throughout their journey.

AI and the Power of Predicting Student Behavior

AI’s ability to map and predict the student decision journey is a game-changer for admissions teams. By analyzing behavior patterns and engagement milestones, AI provides invaluable insights into how students are progressing through the funnel. This allows for more personalized and strategic communication, ultimately improving the chances of turning prospects into enrolled students.

Next week, I’ll explore how AI can help admissions teams craft the perfect approach for each individual student, providing guidance on the best way to engage based on their unique profile and preferences.

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At the NACAC conference, AI was all the rage, but most of the conversation focused on automating tasks like processing applications or sending emails. While automation is helpful, there’s a bigger opportunity being overlooked: AI’s ability to help admissions teams figure out which students to engage with and when. That’s where its real value lies.

The Challenge: Prioritizing the Right Students

In any admissions cycle, you’re often inundated with student inquiries, applications, and expressions of interest. From the student who’s just starting to explore your school to the one on the verge of applying, how do you know who deserves your attention? Traditional methods, such as prioritizing based on GPA or test scores, don’t always give you the complete picture of a student's potential to enroll. Moreover, these methods often miss the nuance of where students are in their decision-making process.

That’s where AI steps in—not to replace your expertise, but to help you focus your efforts. By analyzing student behavior—like how often a student visits your website, what they click on, and how they interact with your communications—AI can predict who’s ready to engage and who might need more nurturing. This saves your admissions team time and allows for more personalized outreach.

How AI Identifies Key Prospects

AI does more than crunch numbers. It spots patterns in student behavior and engagement that humans might miss. For example, AI can track not just whether a student opens an email but how often they return to your website afterward, how much time they spend on specific program pages, and whether they sign up for a virtual tour or webinar. All these actions are indicators of interest, but they also provide a deeper sense of where the student is in their decision journey.

Here are some specific ways AI can help you identify key prospects:

• Engagement Scoring: AI assigns scores to students based on how engaged they are with your institution. A student who clicks on multiple emails, revisits the website, and signs up for events will score higher, indicating they may be ready for more focused outreach.
• Behavior Tracking: AI can look beyond simple interactions and assess deeper behavior patterns. For example, a student who consistently asks about specific programs or scholarships is likely more serious about enrolling than one who’s only browsing general information.
• Predictive Timing: AI can help admissions teams understand when students are most likely to respond to communication. Maybe a student opens every email in the early morning—AI will track that and suggest sending future emails during that window.

Smart Outreach: Prioritizing for Impact

One of AI’s biggest advantages is helping admissions teams focus on the right students at the right time. Instead of casting a wide net with mass communications, AI allows for targeted engagement. For instance, students who show high levels of engagement can be flagged for immediate follow-up, while those showing lower engagement can be put into nurturing campaigns to keep them warm without wasting time on direct outreach.

Some practical benefits of AI in outreach include:

• Optimizing Time and Resources: AI helps admissions counselors prioritize high-value students, freeing up time to focus on those most likely to apply and enroll.
• Personalized Communication: By tracking the specific needs and interests of students, AI helps you craft messages that speak directly to their unique situations, leading to more meaningful interactions.
• Intervention Before Drop-Off: AI doesn’t just highlight students who are ready to engage; it can also flag those who are disengaging, allowing for proactive outreach to keep them in the funnel.

Rethinking Your Engagement Strategy

With AI’s insights, admissions leaders can uncover students who might not have been flagged based on traditional criteria. Maybe they don’t have the highest GPA, but their behavior shows a deep interest in your programs. By digging into these hidden signals, you’re not only broadening your pool of high-potential students but also making your engagement strategy more inclusive.

This kind of data-driven, personalized approach helps ensure that you’re not missing out on great prospects simply because they didn’t meet the standard academic criteria.

AI’s Role in Identifying Key Prospects

AI is transforming the way admissions teams engage with prospective students by helping to identify who to focus on and when to reach out. With a data-driven understanding of student behavior, you can personalize your outreach and maximize the impact of your efforts.

Next week, I'll explore how AI can help map the entire student journey, allowing admissions teams to tailor their communications even more effectively.

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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."

Enhancing Enrollment Strategies with Time-Based Behavioral Insights

Recently, we shared three key breakthroughs that the machine learning engines managed by the data scientists at enroll ml have made available for enrollment leaders.  Another item discussed in depth in our whitepaper, “Harnessing the Power of Machine Learning in Enrollment Management” that should inform admissions strategies immediately is the impact of time-based behaviors.

Understanding student behaviors is crucial for effective enrollment management. Traditional methods often fail to capture the dynamic and nuanced nature of student interactions, leading to missed opportunities for engagement. At enroll ml, we've identified that time-based behavioral insights can significantly enhance the precision and effectiveness of enrollment strategies.

Significance of Time-Based Features

One of the more surprising insights from our machine learning implementations is the critical role of time-based behaviors. Of course, it’s not groundbreaking that timing matters - but the precision our machine learning engine has found is quite significant. These features provide new and valuable insights that enhance the understanding of key enrollment periods and improve overall predictability. By tracking and analyzing the relationship between timing and frequency of student actions, institutions can gain a deeper and more precise view of student engagement and decision-making processes.

For example, the time elapsed between a student receiving information and scheduling a campus visit can reveal their level of interest and urgency. Similarly, monitoring the frequency and recency of interactions, such as email engagements and website visits, helps identify critical windows for outreach. By leveraging these time-based features, institutions can predict a student's likelihood to enroll more accurately and tailor their engagement strategies accordingly.

Practical Applications

In practice, implementing time-based behavioral insights has shown that timely actions and responses from admissions teams can significantly improve the effectiveness of outreach efforts. Understanding when a student is most likely to engage, or when they are at risk of disengaging, enables a proactive approach that can positively influence enrollment decisions.

Conclusion

Time-based behavioral insights offer a powerful tool for enhancing enrollment strategies. By understanding the dynamic nature of student engagement and leveraging real-time data, institutions can optimize their outreach efforts and improve enrollment outcomes. For detailed methodologies and strategic recommendations on how to implement time-based behavioral insights in your enrollment processes, download our full whitepaper, "Harnessing the Power of Machine Learning in Enrollment Management."

Understanding enroll ml and Outcome Optimization Theory in Enrollment Management

Marketers have long mastered optimizing the consumer journey by focusing on strategic touchpoints that drive long-term loyalty and value. It's time for enrollment management to harness that same power. After a decade of mixed results striving towards real-time predictive analytics in higher education admissions, we can now pose an interesting thesis: perhaps we may have been using the wrong tool for the job. This post introduces you to the concept of Enrollment Outcome Optimization, which is deployed by enroll ml to prioritize long-term outcomes over immediate predictions—empowering admissions teams to make better-informed, more impactful decisions. This approach leads to more consistent, data-stable results, fundamentally transforming the enrollment process.

What is Outcome Optimization?

Outcome Optimization allows admissions counselors to identify and guide best-fit students through the enrollment process strategically. Unlike traditional predictive analytics, which are like road signs requiring constant interpretation, Outcome Optimization provides a GPS-like path, highlighting the critical actions that influence a student’s decision to enroll. This approach helps counselors focus on meaningful interactions today while planning future steps, aligning efforts to support students' needs and boost enrollment success.

Key Benefits of Outcome Optimization:

• Strategic Decision-Making: Focuses on high-impact actions to keep best-fit students on the optimum enrollment path.
• Holistic View: Offers a comprehensive understanding of the student journey, from current status to future steps.
• Risk and Opportunity Identification: Detects deviations from expected behaviors, helping mitigate melt risks and capture incremental enrollments.
• Optimized Focus: Prioritizes high-value students, reducing time on low-impact interactions.
• Consistency and Efficiency: Streamlines prioritization, enhances resource use, and provides predictable outcomes.
• Proactive Engagement: Anticipates challenges and guides students toward their enrollment goals.

How enroll ml's Machine Learning Enhances Enrollment Strategies

enroll ml uses advanced machine learning to provide deep, actionable insights that transform how admissions teams approach enrollment:

• Complex Pattern Identification: Analyzes diverse data points to uncover hidden patterns signaling a student’s likelihood to enroll, disengage, or require intervention.
• Daily Re-Scoring: Continuously updates student scores based on the latest data, enabling timely, data-driven decisions.
• Clear Funnel Prioritization: Simplifies complex data into actionable priorities, ensuring focused strategies on high-potential students and melt risks.

Outcome Optimization vs. Traditional Predictive Analytics

• View of the Student Journey: Traditional analytics have a fragmented, short-term focus, while enroll ml's Outcome Optimization takes a holistic, long-term approach to key moments.
• Identifying Opportunity and Risk: Traditional methods are reactive and spread efforts thin, whereas Outcome Optimization proactively prioritizes high-interest students.
• Consistency in Outcomes: Predictive models can be inconsistent and reactive to data changes; Outcome Optimization replicates proven behaviors for consistent results.
• Decision-Making Approach: Traditional analytics react to current data points, while Outcome Optimization anticipates and addresses future challenges.
• Focus on High-Value Activities: Traditional models dilute focus across numerous signals; Outcome Optimization concentrates on impactful actions.
• Ease of Implementation: Predictive analytics often require complex, frequent recalibrations, while Outcome Optimization simplifies by focusing on critical moments.

Why Outcome Optimization is Essential in Today's Enrollment Landscape

Enrollment management has evolved with efforts like the Common App and direct admit programs, reducing barriers but complicating predictive models. Here's why Outcome Optimization is needed:

1. Dilution of Signals: Traditional indicators like campus visits now suggest less genuine interest due to easier application processes.
2. Increase in Data ‘Noise’: A surge in low-intent applicants creates significant noise, making predictive models less accurate.
3. Changing Student Behavior: Minimal effort in exploring options has shifted behavior from strong interest to casual exploration.
4. Limitations of Traditional Models: Predictive models lose effectiveness deeper in the funnel; real-time engagement provides a clearer picture of intent.
5. Need for Transparency: Students benefit from transparent, tailored communications aligned with their demonstrated interests, fostering a more connected enrollment journey.

When is Outcome Optimization Most Effective?

• Real-Time Decision Making: Traditional methods may suffice, but enroll ml’s approach offers a more strategic perspective.
• High-Frequency Low-Value Transactions: Predictive analytics can be useful, but Outcome Optimization focuses on high-impact actions.
• Complex Strategic Processes, Identifying Key Success Factors, Consistency Over Time, Resource-Constrained Environments, Long-Term Strategic Planning, Behavioral Change in Teams: All these areas benefit more from Outcome Optimization due to its strategic focus and ability to maintain consistent results.

Conclusion

While traditional predictive analytics remain critical for market understanding and investment decision-making, enroll ml's machine learning-driven Outcome Optimization is better equipped for the complexities of daily enrollment management. By offering a strategic, holistic view, reducing fragmented efforts, and consistently focusing on high-value activities, enroll ml helps admissions teams achieve more consistent and impactful enrollment execution and outcomes.

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The Transformative Power of Machine Learning in Enrollment Management

In the recently released whitepaper, “Harnessing the Power of Machine Learning in Enrollment Management,” we identified high-level data insights from watching thousands of behavioral data elements among nearly a million students.  While the full paper is available here, you can get a teaser on this page.

In the competitive landscape of higher education, enrollment management teams face increasing pressure to attract and retain high-fit students with limited resources. Traditional methods, such as top-of-the-funnel marketing and profile-based probability models, often fall short, leading to data overload and missed opportunities for meaningful student engagement.

At enroll ml, we know that artificial intelligence (AI), particularly machine learning (ML), offers a transformative potential to address these challenges. Our 2023 work-time study revealed that admissions teams spend over 30% of their time on data management, even with sophisticated CRM and predictive analytics systems in place. By automating data analysis, identifying high-potential students in real-time, and enabling more effective and efficient enrollment strategies, machine learning can significantly improve enrollment team efficiency, performance, and results.

Insights from Machine Learning

While the full whitepaper offers deeper context, there are three key breakthroughs that our machine learning engines have made available for enrollment leaders in 2024.

- Behavioral Tracking: The shift from simple if-then behavior tracking to complex pattern recognition captures nuanced and dynamic student behaviors, providing deeper insights into student engagement.

- Increased Precision: Quick identification and understanding of high-potential students through real-time behavioral pattern recognition enhance the accuracy of predictions.

- Enhanced Efficiency: Automating data tasks increases counselor capacity by up to 30%, allowing for more strategic use of time and resources.

These advancements demonstrate the critical role of behavioral-driven ML in modern enrollment management, offering early adopters a competitive edge. The insights and strategic recommendations outlined in our whitepaper can help higher education institutions enhance enrollment processes, optimize resources, and improve student engagement.

Conclusion

Machine learning has already demonstrated the power to transform enrollment management by providing real-time insights into student behaviors, increasing precision, and enhancing efficiency. Institutions that embrace these technologies early will be better positioned to meet future challenges and capitalize on emerging opportunities.For a deeper dive into how machine learning can revolutionize your enrollment management processes, download our full whitepaper, "Harnessing the Power of Machine Learning in Enrollment Management."