Improvement in undergraduate retention and progression is a priority at many US postsecondary institutions. A number of institutions address this issue by identifying gateway courses (foundational courses in which a large number of students fail or withdraw) and concentrating on "fixing" them. This paper argues that may not be the best use of limited resources. No matter what we do, there will always be courses with too many students receiving grades of D, F and W simply because of the nature of their content and the preparation of the students who must take them. Our research suggests that student type and academic stage affect student success and that gateway courses (courses which block student progression) can be found at all undergraduate levels. Specifically, we have found that one can use binary logistic regression with student type, academic stage, cumulative GPA, and prior withdrawals as predictor variables to predict success in undergraduate courses at our institution. Moreover, relating predictions to observed DFW rates can highlight courses exceeding expectations, as well as those which fail to meet them, to support a more nuanced understanding of where and what attention is needed. In this paper, we describe our "gap analysis" procedure and illustrate the utility of such approach by examining issues surrounding success in online courses at our institution.