Predictive analytics is far from a new technology; it’s been used in consumer industries for many years. However, higher education institutions have only recently starting using this effective management tool in their operations, as they strive to better serve their students by becoming better informed through data.
Universities and colleges already gather large amounts of personal data from their students and applicants. They look at recent transcripts, financial information, community involvement, and more. Although many post-secondary institutions collect this information, not many utilize it in the best way possible.
How Predictive Analytics can Help Postsecondary Institutions Recruit Students
Recruiting students takes a lot of time, money, and effort. University delegates attend college fairs at high schools, create and print an array of marketing materials, and put together fun events all in an effort to attract more students to gain a higher level of education at their particular school. In fact, the median cost to recruit a single undergraduate student for a four-year private post-secondary institution costs $2,232. With predictive analytics, that cost can be significantly lower.
Admissions offices analyze several indicators—academic history, geographical data, demographics—to better predict which students will enroll if they are accepted into a program. With predictive analytics, postsecondary institutions can use geographical data to narrow recruitment campaigns to locations and regions that show the most interest in their programs. By doing so, they can slash marketing costs while producing enhanced recruitment results for the school. Instead of creating and investing in unfocused recruitment campaigns, specific markets can be targeted.
Related: What is Predictive Analytics and What Can it do for Schools?
Other Uses for Predictive Analytics in Higher Education
Enrollment Management: Predictive analytics is a key element of many postsecondary schools’ enrollment management plans. By using this tool, schools can use previously collected information to help forecast the size of returning and incoming classes. This information can help determine how many sections of a particular class are needed. Using a scale, they can also predict each prospective student’s likelihood of applying to a program, being admitted, and deciding to enroll at the school.
Targeted Student Advising: Postsecondary schools can have thousands of students enrolled in one program, but they don’t employ thousands of student advisors to help them. Few colleges and universities employ an adequate number of undergraduate student advisors. A National Academic Advising Association survey found the average caseload of a professional academic advisor is roughly 296:1. This figure balloons to 441:1 at community colleges. With this workload, it’s challenging to give students the attention they deserve and need. However, the use of predictive analytics can help identify academically-struggling students who are most in need of support. Instead of seeing student dropout rates increase, predictive analytics can allow staff and faculty to intervene and foster student success.
Predictive analytics definitely has a place in higher education as a tool to save money, manage enrollment, and increase student success. It is only a matter of time before this tool becomes widely used in post-secondary institutions throughout the country.
