The purpose of this paper is to propose an alternative model to conventional higher education institution (HEI) rankings that can better communicate meaningful differentiation to prospective students. A five-step approach is followed to form clusters and classify HEIs. Cluster analysis is performed on two separate datasets containing (1) public HEIs and (2) private HEIs. For the final model, 42 variables were incorporated to group 761 private HEIs and, separately, 414 public HEIs. A five-cluster solution for each dataset is presented and described. Each cluster contains a description and a managerial recommendation. The application cluster analysis to group HEIs differs from the more popular but more problematic approach of ranking HEIs. Grouping resolves the problems that stem from ranking and provides possibly more useful information.