enroll ml

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This is a fantastic article by Jeff Selingo highlighting critical macro trends in college admissions, particularly the rise in application volume and shifts in student behaviors. For admissions leaders and teams focused on yield management in 2025 and beyond, five key impacts stand out to me: 1️⃣ Admissions team workload overload 2️⃣ Balancing precision vs. scale as application volume continues to climb 3️⃣ Managing melt and addressing yield gaps 4️⃣ Mitigating admissions counselor and team burnout 5️⃣ Addressing actionable data gaps Strategic enrollment plans have traditionally leaned heavily on demographic and geographic trends and top of the funnel acquisition. However, the evolving landscape demands a sharper focus on how admissions teams, technology, and processes can not only manage these challenges but also create a competitive institutional advantage through admissions and enrollment management process and data innovation.

For enrollment management professionals, yield will be the name of the game in 2025. Here's how we think about it at enroll ml: This chart from 2023 IPEDS data shows yield rates for 4-year non-profit institutions, grouped by yield buckets. A 20% yield rate means 80 out of every 100 admits said "no." Within those 80, there is almost certainly a group of "swing students"—those who were seriously considering enrollment but may have required more personalized engagement to convert. Often, we assume that many of these students are driven to their "no" primarily by financial aid offers. But our machine learning deployments reveal a different story: Not all swing students are single-issue (financially) motivated. In fact, financial aid awards are rarely identified as one of the most predictive features in our most predictive models (this is a whole different post!). Assuming financial considerations are the sole driver for the swing students can cause us to overlook those students who are sending subtle but exceedingly meaningful signals of interest. At enroll ml, we identify applicants with a "behavior-action mismatch"—complex signal patterns of interest paired with missing or delayed critical next steps. For admissions teams, these students should be flashing green lights, signaling significant interest. However, they often need personalized attention to overcome specific hurdles and move forward. Until now, the challenge has been identifying these students with enough precision—and time—to act. That’s where machine learning has changed the game: Shifting from static probabilities, scores, predictions (which often miss these deeper signals) or intuition to decoding the real time interconnected behavioral combinations that provide a more precise and nuanced signal of interest. By identifying and addressing behavior-action mismatches, admissions teams can act before it’s too late, improving both yield and melt rates. For example, one of our partners identified three students in their funnel on the day enroll ml launched. These students displayed high behavior-action mismatches but weren’t on active outreach lists. Acting quickly, the admissions team contacted each student, helped them overcome individual hurdles, and successfully enrolled all three—just days before registration closed. While there’s no magic solution to yield improvement, leveraging advanced technology like machine learning unlocks new opportunities for precision, automation, efficiency, and personalization - all critical elements for tackling a challenge where even a 1-2% boost in yield can have a transformative impact on an institution.

If you're planning to use "FAFSA = Y" or "FAFSA = N" as a sorting tool for your applicant pool this year ... you're missing the real signals from students. https://lnkd.in/gefZHNT8

At enroll ml, our machine learning models go beyond traditional benchmarks to uncover the intricate, interrelated behavioral patterns that signal enrollment likelihood. In the visualization below, you’ll see the currently deposited students segmented by their behavioral score ranges. If you are leading this institution, where should your admissions team focus in to shore up the deposit pool and mitigate melt risk?

How, specifically, is technology improving staff morale? Here's a short clip from our conversation with Elizabeth Kirby, MBA on that exact topic from a while back. The full conversation is available on demand: https://lnkd.in/gzYWp6xi

Powerful insights from our founder & CEO Geoff Baird on the critical actions institutions must take to navigate and thrive in the shifting demographic landscape of higher education.