4 Steps Higher Education Institutions Can Take to Prepare Their Data for Predictive Modeling
n follow-up to our September 9th post on The Biggest Challenges Facing Higher Education Institutions, we will be posting a series of interviews with enrollment management experts discussing how their jobs of recruiting, enrolling, and retaining a class have changed over the past several years.
We will do a deep dive into how advanced analytics can be used to help enrollment managers do their jobs more effectively and efficiently. They will share practical tips on how to boost inquiry-to-applicant conversion, how to predict the yield and composition of the incoming class, and how to ensure enrolling students will have the tools they need to succeed.
Of course, the first step to using advanced analytics to address challenges in higher education is to make sure your institution is collecting and storing the right data, in the right way. This is why we created a guide outlining the Four Steps you can take to prepare your data for predictive modeling and other advanced analytics. We look forward to seeing you back here over the next few months as we explore employing analytics to optimize every stage of the student lifecycle.
Want to learn more about our work? Visit our higher ed solutions page for an overview of our decades-long experience in the industry, including building predictive models, creating enrollment and financial aid strategies, and more.