New admissions metrics for today’s climate

Predicting the increasingly unpredictable

Although managing enrollment efforts has never been easy, it was not that long ago when the traditional funnel was somewhat predictable.

As an admissions counselor in my early days, I could easily work backward to set goals for my territory, starting with my goal for the number of enrolled students. I then used historical data and three-year trends to forecast the number of admits, applicants and inquiries needed from my territory to achieve the goal.

Those four metrics drove goal-setting, travel planning and tracking reports. Although missing the class goal was still possible, being surprised about it was unlikely because of how systematically students seemed to move through the traditional admission process. Today, life in the admissions office is far less predictable.

Jordan Bryant, interim director of undergraduate admissions at Trinity International University in Illinois confirms this reality: “Last year, on May 1, we had a record number of deposits, which led us to project our fall enrollment with optimism. However, we quickly realized that it wasn’t because of a large influx of students in our funnel or a significant increase in yield. They were behaving differently and depositing earlier than they ever had before. So while our class was larger than the previous three years, it was not what we were projecting in May.”

Certainly much has changed in the last decade within enrollment management. The competition for students has intensified. The population shifts from the Northeast and Midwest to the South and Southwest—as well as the growth in ethnic minorities and first-generation students—have required many institutions to think differently about how they recruit students and what the financial aid implications will be.

It’s no surprise that the metrics enrollment managers need to use have also changed. For example, social media has become a significant part of most recruitment efforts.

As Karissa Peckham, dean of admissions at the University of Bridgeport in Connecticut, notes: “With students’ increasing reliance on the internet, and in particular social media and peer recommendations, it is important to us that we have a multi-channel approach to recruitment; this means tracking the actions of students who may not yet be a part of the admissions funnel, as well as their actions throughout the application process.”

In short, this is not your father’s admissions funnel. The new funnel is a stream and students jump into it and out of it in organic ways, engaging with you on their terms.

So what new metrics are needed to forecast their enrollment results with some degree of certainty?

Inquiry by source code

Analyzing the inquiry data your institution collects in more detail is crucial. Monitoring the total number of sources—even segmented by territory—is no longer enough.

Now, it is critical to track how different source codes convert to the point of application, admit and enroll—and to monitor what percentage of your new pool is coming from high-performing sources.

Collapsing source codes into categories may help get this process started. Separating them into broad categories—such as name buys, organic leads, referrals and web-collected—may provide some indication of how your inquiry pool has shifted over time.

For example, if your inquiry pool has grown dramatically but the growth is primarily from low-converting sources, you can anticipate that your applicant pool may be stagnant despite the larger inquiry pool.

Applicants and admits by subpopulation

Admissions staff also need to monitor shifts in the applicant and admit pools by application type. This is how they can assess whether changes in the pool bode well for completion and admit rates.

For example, if growth in the pool is primarily from the Common Application—or pre-populated, free or low-cost applications—it is important to know how well that application type has converted to admitted students in the past.

Similarly, monitoring changes in the pool by subpopulations can help a school anticipate changes in yield rates should those students be admitted. For example, is the increase in applications primarily from out-of-state, where yields may be lower than average? Is the increase from majors with high demands and yield rates, but capped enrollments?

Without digging deeper, it will be difficult to know how well the pool will perform in converting to applicants and yielding a class.

Other interest indicators

Knowing the percentage of admitted students who have visited campus with date-to-date comparisons to the prior year can provide a strong indication of how committed the admit pool is compared to the past. Campus visits are one of the strongest variables in predicting the likelihood of enrollment.

Knowing what percent of your admit pool has filed a FAFSA compared to the same date in previous years is another critical measure of the commitment level of your admit pool. If the percentage has decreased, various enrollment offices (admissions, financial aid, athletics and honors) should strongly consider conducting a “call to action” blitz to non-FAFSA filers.

The goal of these contacts should be to understand and possibly remove any potential barriers.

A new metric institutions are starting to analyze is FAFSA position. Packaging students by position is not recommended.

But understanding how your yield rates change as your institution’s position was listed on the FAFSA changes may provide enrollment leaders with another important way to estimate the commitment of the pool.

If the position performs as expected—with those listing your institution first yielding the best—tracking the distribution of FAFSA position as applications are submitted may give you an early read on the interest level among FAFSA filers.

Metrics to guide strategy

The University of Bridgeport’s Peckham tracks electronic metrics and website traffic to shape messages.

“We track the students who visit our website through the various sources, but we can also see which pages students are spending their time on,” she says. “This becomes particularly helpful in drafting specific messaging for certain target populations, such as financial aid or campus visit information.”

Peckham also utilizes her CRM’s tracking capabilities to determine which messages are most effective.

“We populate our student records with each admitted students’ FAFSA information, including whether or not they’ve filed, where the University of Bridgeport is positioned in the list of schools, and what their financial gap would be if they chose to attend UB,” she says. “We use this information in communicating relevant information to both students and parents and to help us gauge the likelihood that an individual student will enroll.”

Understanding the role that merit and need-based aid plays in enrollment decisions is also important for setting packaging strategies. One simple approach to analyzing the impact of institutional grants is to conduct a cost/benefit analysis examining the yield rates for various subpopulations.

How much does yield increase for that population when grant increases? Would increasing grants to students in a specific subpopulation pay for itself because enough students would enroll to increase total net tuition revenue? Or would those increases in aid actually decrease net tuition revenue because very few students would be influenced by the additional money?

Data-driven decisions

Of course, more sophisticated price sensitivity studies—also known as “econometric” studies—can test multiple variables simultaneously and simulate the likely results of changes in awarding. However, it can be done only if aid offers are maintained in the system for both enrolled and non-enrolled students—so keep all aid offers available for analysis in the future.

Certainly the enrollment environment is increasingly competitive and less predictable than it was 10 years ago. While there is no “crystal ball,” digging deeper into these next-level metrics is a requirement for enrollment managers in the 21st century if they are to make data-driven decisions and any mid-course corrections that may be necessary.

Collecting and analyzing these and other metrics may not ensure you will reach your class goal, but they will reduce the element of surprise as you seek to forecast your class size, net tuition revenue and other class characteristics.

Aaron Mahl is an enrollment management consultant at Scannell & Kurz.

Most Popular