The document discusses a presentation by CCG, a consulting firm, about using predictive modeling and data analytics to improve student retention rates at universities. It describes how retention modeling works by analyzing historical student data to identify characteristics that correlate with students dropping out and using that to build a model to predict which current students are at risk. The benefits mentioned include focusing retention efforts on at-risk students and adapting predictions as outcomes improve over time. The presentation demonstrates CCG's retention modeling solution and discusses the types of student data needed to build such a model.