As part of the traditional recruiting process, college admissions professionals may visit dozens or even hundreds of high schools on behalf of their institution. This fall there are roadblocks to that effort. School districts across the nation have announced they are closed to outside visitors, and many have shut campus altogether with plans for distance learning to begin the year. Standard methods for evaluating and scoring students have also been hampered by the pandemic restrictions. California is a prime example.
In response, enrollment teams are pivoting to virtual events and reaching out to students that would normally be missed in the course of in-person visits. Rural areas that were too sparse to warrant a visit are now accessible markets to cultivate from a distance.
Taylor University made such a pivot several years ago, by reducing travel and bringing the recruiting effort primarily inside. This transformation in the enrollment management process was enabled by the implementation of predictive analytics. By identifying potential prospects at each phase of the enrollment funnel, the admissions counselors and marketing teams get actionable insights each day. This is fundamentally worked out by knowing who to contact and when. A much larger and more diverse prospect pool is traversed by curating a target list of 150 or so students each week based on the probability scores.
This effort has paid off with successful recruiting alongside the significant travel savings. It has also made the current environment of restricted high school access far less intimidating. The opportunity to engage in more conversations with students is making up for the lack of personal interactions.
Technology is filling in many of the gaps in educating students that have arisen during these unprecedented times. While there is no perfect solution to the enrollment challenges facing institutions today, you can position your team to capitalize on the opportunities that do exist. Predictive analytics is a means by which you can constantly ingest new information and student data for continuous forecasting improvement.
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