Predictive Enrollment Management and College Admissions Decisions

This article cites real examples of how colleges are actively managing their admissions protocols to the bottom line via enrollment management. This includes ways to limit the number of students they accept based on historical data, compensating college presidents to manage to the U.S. News and World Report College Rankings via incentive bonus packages, and using merit aid as a tool to induce interested qualified students to enroll at their institutions.

Colleges today are not looking for students with the highest grades and test scores. They are looking for students with the highest grades (and perhaps test scores) who are likely to enroll and thrive at their institutions.

The Tufts Syndrome - Is Safety the New Reach?

“Yield protection (commonly referred to as Tufts syndrome) is an admissions practice where a university or academic institution rejects or wait-lists highly qualified students on the grounds that such students are bound to be accepted by more prestigious universities or programs.”

Stories involving yield protection date back to the early part of this century when the importance of the U.S. News & World Report College Rankings began to really take hold. In 2001, the Wall Street Journal reported how the admissions director at Franklin and Marshall College, Gregory Goldsmith, “spurned 140 of its smartest applicants.” Goldsmith’s decision was based on historical data showing that only a half-dozen or so of these highly qualified students actually enrolled at Franklin & Marshall. Because these applicants positioned Franklin and Marshall as one of their safety schools and demonstrated no other form of interest in attending, Goldsmith concluded that issuing acceptances only served to increase the school’s acceptance rates (bad) and lower the school’s yield (also bad).

“They think they’re Ivy League material and we’re not Harvard!”

— GREGORY GOLDSMITH, FRANKLIN & MARSHALL COLLEGE

This episode was an early indicator of proactive enrollment management principles at work. Pressured to accept the lowest number of students needed to fill an incoming class, colleges learned how to use data to assess enrollment probabilities. The biggest impact is arguably at the safety school level where colleges know they are sitting behind an applicant’s reach and target schools. “As the admissions process becomes more digital, it makes sense that data analytics would play an increasing role in the admissions office determining the depth of a student’s interest,” says Peter Zimmermann, a past Stanford admissions officer.

Students who simply click a box on the Common Application to apply to their safest college choices without doing anything else to trigger enrollment interest are finding it increasingly difficult to get into their safety schools.

NYIT’s Presidential Pay Proves Point

Let’s take a look at how predictive enrollment management was institutionalized at the New York Institute of Technology (“NYIT”).

NYIT is a small, private, non-profit, comprehensive senior institution of higher education, chartered by the Board of Regents of the University of the State of New York in 1955. NYIT offers approximately 90 degree programs, including undergraduate, graduate and professional degrees, in more than 50 fields of study.

Edward Guiliano was the president of NYIT from 2000 - 2017.

In 2016, Mr. Guiliano ranked as the fifth most highly compensated college president in the country.

That’s right, the president of little old NYIT was the fifth most highly compensated college president in the country in 2016.

In 2016, Guiliano’s $2,733,651 of total compensation included a base salary of $766,712, a performance bonus of $1,655,021, and other pay of $311,918. Let’s take a look at what President Guiliano did to earn his bonus and why the trustees of NYIT would agree to such a large compensation package.

In 2013-14, NYIT management decided to proactively increase enrollment by implementing more aggressive recruitment and targeted financial aid strategies. NYIT launched an algorithmic based website as part of this campaign. NYIT also engaged the consulting firm, Ruffalo Noel Levitz, to more aggressively market NYIT’s degree offerings to high school students and to better target institutional scholarships to particular students.

NYIT also used big data to increase retention and graduation rates. At NYIT, data showed that successful students who exhibited a strong interest in design, problem-solving and using their hands are likely a better fit for the highly technical programs than are those who leaned toward liberal arts.

“That information can’t easily be gleaned from a GPA or SAT score,” stated Mark Hampton in 2017, when the vice president for enrollment and enterprise analytics at NYIT. “What we’re looking for is that aptitude, that interest—which can be deduced by social media profiles, and from activities in high school and outside of the classroom. It’s why someone who identifies as a gamer and who loves science fiction and enters math competitions would likely be a better fit at NYIT than someone who’s an Emily Dickinson buff.”

Like all things digital, the use of data in college recruiting and admissions has only just begun. “We’ve moved from traditional institutional research to business intelligence to data science departments,” says Hampton. “Most universities realize this is how you need to play the game. And if not, you’ll be at a competitive disadvantage.”

As a result of the increased focus on enrollment and retention, NYIT’s undergraduate applications and acceptances increased by 47% and 36%, respectively, by the academic year 2015-16. NYIT’s graduation rates also improved and these successes boosted NYIT’s USN&WR ranking.

This all led to President Guiliano meeting his performance-based bonus objectives and earning his $1.6 million incentive bonus in 2016. College planners cannot underestimate how powerful the incentive dollar is at driving the analytical approach into college admissions. College admissions, once a pastoral calling, is now a science.

Whether your student is seeking acceptance into a highly selective college or looking to properly position themselves for a max merit package, learning how to use social media to demonstrate interest and to showcase fit has become an indispensable college prep tool.

Today – before a single human eye glances at a test score or the opening line of an essay – admission decisions are being pre-sifted by machine, using AI-driven “predictive analytics” fueled by data reaped from the Internet. Colleges rely on these “enrollment management systems” to accurately predict things like an applicant’s intention to enroll, their likelihood to succeed, and their ability to pay. And college applicants ignore this new reality at their peril.