Data-Informed Recruitment and Enrollment

Moving beyond high school GPA and standardized test scores, you will want to look for predictors of student success and affinity for your institution by analyzing data on your past and current students at each stage of their relationship with your institution. Identify shared characteristics of those students who model behaviors you want to encourage.

Look for shared characteristics among:

  • Prospects who apply (versus prospects who don’t)
  • Admits who enroll
  • Students who take leadership roles
  • Students who perform high academically
  • Students who persist into the second year
  • Students who graduate within a certain time range
  • Students who stay engaged with the institution after graduation and take leadership roles in the alumni community

Once you know the characteristics of your current, most successful students, these characteristics become additional attributes that you can identify in your recruiting process.

Suppose that your data indicates that a significant percentage of the students who show high academic performance and persistence in your engineering program took calculus prior to entering it—and you find that many of these are transfer students who completed calculus while enrolled at a local community college. With this knowledge, you can look to your applicant pool and identify applicants who have taken college calculus.

“Give this target population special attention,” Jim Scannell, president of Scannell & Kurz Inc, suggests. “Reach out with a campus visit day for future engineering majors, or visit that calculus class at the community college and speak with the students.”

"But this is change by addition, not change by subtraction. You don’t focus your entire recruitment process on applicants with those attributes, but you are going to feature those people, approach them with special communications and with a more personal touch, and offer them more reason to consider you as their institution of choice."
Jim Scannell, Scannell & Kurz

Let’s look at two indicators in particular, and at sample tactics for acting on them:

  • Distance from campus
  • Early engagement during the enrollment process

Distance from Campus

Typically, institutions take the past history of applicants and admits from given geographical areas into account when recruiting. You may, for example, prioritize recruiting students from Region West rather than Region East if you have seen a higher yield rate from Region West in the past. Also, it’s common to consider which feeder schools yield students with predictors of success. But in prioritizing areas from which to recruit, have you looked for correlations between student retention and distance from campus?

Here’s an example. Loralyn Taylor, the director of institutional research at Paul Smith’s College, set out to answer that question about students at her institution. She found that:

  • Students whose families lived within a two-hour drive of campus had a high retention rate;
  • Students whose families lived further away from campus had a lower retention rate; but
  • Students whose families lived more than an eight-hour drive away had a higher retention rate.

After surveying and interviewing these students, Taylor concluded (with reservations) that what is likely occurring is that the students who live close to their families may “go home” to see them but remain fairly engaged in the campus community. Those who live three or four hours away often drive home every weekend and develop less of a sense of place and an affinity with the campus. In fact, missing their family and friends, they are less engaged in their classes and often schedule their courses so that they can end the week early.

By contrast, those whose families live more than eight hours away only go home for the holidays and remain very engaged in the campus, forming new friendships on campus and devoting more of the weekends to their studies.

If these were the results at your institution, then based on your enrollment strategy, you could respond to this data in a number of ways. For example:

  • You could place a higher priority during the recruiting phase on outreach to the other demographics—the local students and/or the out-of-state students
  • If you are primarily an open-enrollment institution, you could invest in student affairs programming to reach out to that middle population and their parents, to advise them that returning home on multiple consecutive weekends may impair their academic performance

Here is another question to look at. Beyond the simple fact of persistence, is there any correlation between those geographical demographics and:

  • Involvement in student activities and organizations
  • Student volunteerism
  • Engagement as young alumni

In other words, which demographics are most likely to connect with your institution in meaningful ways and are most likely to stay connected with your institution?

Early Engagement During the Enrollment Process

Here’s another example.

The University of Dayton is trying to identify signs of affinity with the institution among applicants and admits, with the goal of admitting and enrolling a more engaged class that will be enriched with potential student leaders, volunteers, and long-term ambassadors for the institution. Dayton is only in its second year of using its predictive model (developed in partnership with a third-party vendor) to drive specific investments in the enrollment process, but Rob Durkle, Dayton’s assistant vice president of enrollment management, notes that faculty members have already shared with him, anecdotally, that this is the most engaged class they can recall.

Durkle is testing the hypothesis that affinity and student engagement can be correlated to level of engagement with the institution during the enrollment process. “Look at what your students do between the application and the admit date,” he suggests.

For example:

  • Which students connect with the institution through additional communications (email, phone, text)?
  • Which students come to a campus visit?
  • Which students engage in programming such as a reception for admits?

“We are looking for indicators of engagement beyond just clicking ‘Submit’ on an application,” Durkle explains.

Durkle has actually included this in the criteria for selection. If the school has one spot left to give and two applicants, then all else being equal, if one student’s only communication with the institution was to click ‘Submit’ on the online application, and if the other student has visited the campus 1–2 times during the enrollment process, then Durkle theorizes that the second student is more likely to invest in their time at the institution.

Use predictive modeling to dig deeper and find out which specific interactions during the admissions cycle show the strongest correlation with later engagement:

  • What about students who not only did a campus tour but also did an overnight stay or attended a campus day for applicants?
  • What about students who also met with a faculty member?

“Look at the propensity to be engaged and thrive,” Durkle remarks, “and identify the right opportunities and programs to engage admits.”

If your data indicates that these students not only were more likely to enroll but also performed better in the first term and persisted to sophomore year at a higher rate (because they came in with right-sized expectations about the campus experience), try boosting both your yield and persistence by investing in getting more applicants to visit your campus and participate in specific activities during the admissions cycle.

Also, model your current alumni leaders and alumni volunteers, your regular givers to the annual fund—starting with your engaged young alumni, if you have a paucity of data from years further back. What did your young alumni do during the admissions process? What did they do as first-year students? What were those earliest signs of affinity?

In This Issue

A Letter from Amit Mrig, President, Academic Impressions
Reviewing Your Data: What You Might Find
Data-Informed Recruitment and Enrollment
A Data-Informed Approach to Student Retention
Mining Your Data: From Students to Alumni