Jenzabar Predictive Analytics

Join Simpson Associates and Jenzabar for the Third in a three-part Student Relationship Management (SRM) series, as we demonstrate how Institutions can improve Student Engagement and Success using predictive modelling to take intelligence driven action.

During this webinar we will demonstrate how predictive modelling can identify at risk students and determine the resources, or support a student may need, as part of a positive, perpetual improvement cycle.

Like many of you, our customers are seeking the magic, black box of retention, and many vendors claim to impact persistence, but in our experience, student success is not the simple story people want it to be. Student results and standardised scoring are just one strand of an intertwined set of data that tells the complex story of student outcomes.

Jenzabar Retention’s predictive modelling functionality reflects the unique characteristics of your student population. An analysis of your current data, as far back as three years if required, identifies trends and individual risk factors affecting your retention and graduation rates. Weighting selected risk factors creates your own unique Risk Assessment model which is then applied to your campus, allow a clear and consistent view of who is safe, who is at risk, or who is at high risk.

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