Like the fabled winds of song, new students come sweepin’ down the plains to the University of Oklahoma, with a little help from analytics. I recently had the opportunity to chat with Lisa Moore, Data Scientist at the University of Oklahoma, on her expanding use of predictive analytics. It had been a while since my previous blog post on how she successfully used SAS® Enterprise Miner™ to assess the probability that an admitted student would enroll and then determine what actions recruitment officers should take to entice students to come to the University of Oklahoma. And I was excited to learn about her newest opportunities for using analytics.
She explained that there is still stiff competition to entice the best and brightest students. As such, recruitment offices must focus their limited resources on the students who are most likely to enroll. But, they still need more information. They need new ways to increase applications. And they need to know what scholarships and what amounts would not only attract the select students, but also get them to apply and ultimately enroll? Once again, Moore turned to predictive analytics to help answer these questions.
She and Brendan Klein, Assistant Director of the Office of Strategic Technology, are using predictive analytics to determine new ways to increase applications by strategically targeting prospective students. They analyzed the prospective student data to score the prospect’s likelihood of applying to the university. Using those scores, recruiting officers determine the best way to contact the prospective student to encourage them to apply. This also helps with goal setting for each recruitment region so that each recruiter knows how many applicants they need to get in order to meet the overall enrollment goal.
Moore is also looking into how scholarship information is used to attract and enroll select students. She created a predictive model to understand an admitted student’s likelihood to enroll. Then she takes it a step further to determine what it would take to maximize their probability of enrolling. Administrators use this analysis to determine which students to offer department or college scholarships in order to entice them to enroll.
Building on their success, Moore and Klein created another predictive model to understand how scholarship amounts impact the likelihood of the incoming freshman class to enroll. Moore then reran the model using varying scholarships amounts. Using this information, Klein created a “what-if analysis calculator” for administrators to use. to do scenario analysis and better determine the impact on overall enrollment, demographics, and academic quality of the freshman class, if they change the amount of a specific scholarship.
It was exciting to hear all the different ways analytics are used to get to students to apply and ultimately enroll at the University of Oklahoma. I don’t imagine many of them arrive in a surrey with the fringe on top, however.
For more information on using analytics across the student life cycle, read this whitepaper, Analytics Across the Student Life Cycle: Empowering Higher Education With Analytics to Increase Student Success.
