No, our models are customized for each institution. The first step in our process is to identify the “High Impact Questions” (HIQs) that matter most for your school, such as, “What is the likelihood that a student will enroll?” Then our team creates a machine learning model built around answering that question. Othot’s predictive engines also combine a school’s historical data with external data and can be customized to create predictions for a school’s unique “What-If” scenarios, such as specific campus visit types or scholarships.
This proprietary approach produces predictive intelligence that offers a more comprehensive solution than other analytic programs.
We recommend that you have two to three years of historical data student data to build a model. In addition, we also source external data to build your models. External data includes demographic, socio-economic, geographic, environmental and behavioral.
We release updates to our software several times a year and schedule the updates to minimize disruptions to our customers. We communicate with our customers often about the new releases and provide detailed instructions and notes on all new features in the platform.