However, the year after that record-setting class, due to a restructuring and reorganization of admissions and recruitment, the models were not used. The university fell short of its enrollment goals.
This year, the models are back and Oklahoma has exceeded its enrollment goals, setting a new school record in the process.
There have been other benefits, as well. The project improved the university’s data collection, resulting in far more reliable data.
The system is also helping at the director level. IRR has combined an individual student’s probability and their financial information available with a forecast and scenario analyses so the director of Admissions and Recruitment can know the applications and admissions targets that will lead to the desired enrollment goal. The director can balance workloads by using the predictive model to plan how many students each recruiter will work with.
IRR is now analyzing data from areas such as selectivity, retention, and student satisfaction with the goal of creating a more complete, data-informed view of all the factors affecting a student’s likelihood of enrolling, staying and successfully completing a degree. More and more of this information will be fed into IRR’s reporting dashboard to more easily share performance data across the university.
The models provided surprise insights that turned some assumptions upside down. Oklahoma found that better prepared residents are not more likely to enroll. Perhaps more shocking, large scholarship amounts are not significant.
Less shocking was the lesson about what happens when analytics is left out of the recruitment equation. Learn more about how analytics across the student lifecycle can lead to greater success.