Customer Webinar: Predictive Modeling for SEM
Our next customer webinar, “Strategic Enrollment Management: St. Michael’s College and Predictive Analytics” will be given by Bill Anderson, CIO of Saint Michael’s College today at 2pm EDT and will be re-broadcasted on Tuesday, March 26th, and Thursday, May 2nd.
I got the chance to ask him a couple of questions about his session, which will describe the ways in which Veera Construct and Veera Predict are utilized on campus to produce predictions and other analyses for the scoring team.
What types of models have you been building?
Almost entirely enrollment management – mostly apply to enroll. We’ve been building them on and off for about five years now. I have someone on campus that I collaborate with and when we first started, she was using SPSS for the statistical analysis, but we’ve since abandoned that.
How has model building changed your Enrollment and/or Financial Aid practices?
There have been a number of ways that we’ve used the models – one as a sort of verification of what our consultant has been doing, two to be able to do some sensitivity and what-if analysis (and suggest different practices or emphases on where the aid awards should go), and three to help confirm in-semester and in-process prediction on where the class is going to end up.
In some occasions, this has impacted size of waiting list or the way we thought about awarding wait list spots, including the total number of admits. This last year, our model suggested that we could be more selective than we had been in the past.
What do you hope attendees will learn from your presentation?
One thing is that you can do it on your own – it’s not that hard. You have to have a background that supports responsible interpretation of the results, but you can sit down and do it. That’s one element: just do it. I think there’s another element that says once you start thinking this way, it can become infectious. In our enrollment management meetings, we have the opportunity to appeal to the data or look at a Veera Construct job that identifies the applicants we could avoid accepting. This changes the internal conversation – from a culture of anecdote, you can change the conversation with data. The use of the products has been fabulous in terms of making the data accessible to people.
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