For instance, the number of high school graduates in the Northeast is expected to drop from 650,000 in 2025 to 571,000 in 2037, WICHE says—a decline of more than 12 percent. In the southern states, enrollment will reach its peak in 2026 when 1.5 million students will graduate. In 2037, just one year later, that number is expected to drop by 6 percent—meaning there will be 100,000 fewer students to fill the freshman class. California, Illinois, and Connecticut are expected to see some of the biggest drop-offs.
Colleges and universities in areas that will be hit the hardest can’t afford to do business as usual, and that’s especially true for regional four-year institutions that serve mostly local students. These institutions could lose more than 11 percent of their students by 2029 if there isn’t a change in their recruiting strategy, Grawe predicts.
Some institutions will be able to adjust to this decline in their traditionally recruited pool of students because they are thinking ahead. Several of these institutions are applying advanced analytics and AI to their recruitment strategy. In simplified terms, data analytics produces highly sophisticated profiles of the types of students an institution is looking for. Armed with these profiles, it becomes possible to match profiles to students in other geographical areas that are ripe for recruiting.
Colleges already have the data they need to find these students. Advanced analytics becomes helpful when combining data sets together and applying them in different scenarios. With this technology, an institution can model the future, noticing trends that either increase or decrease the incoming class of first-time college students. Colleges and universities can see a global picture of where their recruiting efforts will be most successful, because they can drill down and better understand fit: Is a student likely to accept? Once admitted, what supports will best help that student graduate?
Many colleges and universities tend to recruit students from the same geographic areas each year. It’s a common refrain to hear an admissions officer say, “We get a lot of students from Springfield,” for example. In years when the number of graduating high school seniors is constant or increasing, recruiting heavily from Springfield has been sufficient for their needs in this hypothetical example, but data science can untether recruitment from geography. Advanced analytics and AI can dramatically improve diversity and reach into communities that institutions might not have considered before.
Data science has become so important that even three of the leading higher education associations, EDUCAUSE, AIR, and NACUBO, have issued a joint statement about the importance of using advanced analytics to make strategic enrollment decisions.
What is interesting about AI applied in data-heavy decisions, such as where to recruit students and how to project for the future, is that it uncovers the unseen. Humans tend to default to ingrained patterns and this is no different in college recruiting. Processes that have worked in the past become routines, the tried-and-true methods that have been successful for years but will probably not be throughout the rest of this decade.
A significant storm is brewing, and the danger faced by hundreds of institutions is real. Admissions teams applying advanced analytics that provide insights down to the individual student are better equipped not only to help their college weather the demographic storm front approaching them, but also to offer opportunities to millions of students traditionally left out in the cold.
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