Using Predictive Analytics for Student Retention: WGU's Higher Ed Data Strategy

In this higher education data strategy session, Luis Sanchez (Product Manager of Analytics at Western Governors University - WGU) explains how to shift from reactive data reporting to proactive attrition prevention. For universities and enterprise organizations alike, user churn (attrition) is a massive challenge. Rather than analyzing why students or customers have already left, WGU architected a modern data platform designed to intervene before it happens. This session breaks down how WGU uses predictive analytics and unified customer data to identify at-risk behaviors and automatically trigger retention efforts in real time. Key Takeaways from this Case Study: - Predictive Data Architecture: How WGU built a data platform capable of fueling machine learning models to forecast future user behavior. - Identifying At-Risk Signals: The specific data points and behavioral signals used to predict student attrition before it occurs. - Proactive Retention Strategies: How to move from predictive insights to automated, real-time interventions that successfully retain users. Speaker: Luis Sanchez, Product Manager Analytics, Western Governors University (WGU) Topics Covered: Western Governors University (WGU), Higher Education Data Strategy, Predictive Analytics, Student Retention, Attrition Prevention, Customer Churn, Customer Data Platform (CDP), Enterprise Data Architecture, Machine Learning (ML).