It can be hard to know how to feel about artificial intelligence. One minute, it seems like AI will make companies wildly profitable by giving them magical powers that will allow them to understand their customers. Refresh the news feed, and suddenly AI is a menace, threatening our privacy and job security.
AI, machine learning, and the larger field of data science are indeed changing how business is done. But the future is neither perfectly rosy nor exactly dire. The ability to collect and store demographic and behavioral customer data combined with advances in analytics makes it possible to predict what customers will do, buy, or not buy like never before.
As time has progressed, the advances in business impact and analytics complexity have moved forward as well.
The cost, lack of technical expertise, and data-shy culture impeded higher education administrators from adopting predictive analytics. Sure, there were some early movers within student recruitment who learned the “likeliness to apply” models that predict the probability of a prospective student submitting an application. But, overall, we’re about 10 years behind where we could and should be.
Our white paper, Predict 101 for Enrollment Management, lays out how predictive models are built, and features real examples from schools we’ve worked with. It also includes three use cases to showcase the possibilities of using predictive analytics to better allocate resources and achieve other objectives.
Case 1: Alev, a director of admissions, wants to reduce marketing spending by targeting high-impact territories.
Case 2: Alev and the recruitment and admissions team want to determine who to visit during the upcoming travel season.
Case 3: A graduate school’s marketing team wants to create a highly personalized academic program recruitment campaign.