As a statistician for Noel-Levitz, I spend a lot of time developing predictive models at the search stage. Predictive modeling uses enrollment data of current students to assess the enrollment probability of prospective students. Campuses can then use this model to estimate the likelihood of prospective students enrolling, and this can be done at various points in the funnel.
Noel-Levitz has a predictive modeling service for the search stage, SMART Approach, where campus historical outcomes are matched to survey response information from the National Research Center for College & University Admissions (NRCCUA). The resulting model allows campuses to purchase qualified NRCCUA names using a score based on each student’s likelihood of enrolling.
When discussing strategies for purchasing search names, the topic sometimes turns to the longer term concern for student persistence. I have been asked by colleagues and clients if it is possible to develop a SMART Approach model that can predict first-year to second-year student persistence. The question makes a lot of sense—more and more campuses are focusing resources on student retention, so why not use the predictive power of SMART Approach to purchase search names based on the likelihood to persist?
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