No significant body of research examines writing achievement and the specific skills and knowledge in the writing domain for postsecondary (college) students in the U.S., even though many at-risk students lack the prerequisite writing skills required to persist in their education. This paper addresses this gap through a novel "exploratory" study examining how automated writing evaluation (AWE) can inform our understanding of the relationship between postsecondary writing skill and broader indicators of college success. The exploratory study presented in this paper was conducted using test-taker essays from a standardized writing assessment of postsecondary student learning outcomes. Findings showed that for the essays, AWE features were found to be predictors of "broader outcomes" measures: college success indicators and learning outcomes measures. Study findings expose AWE's potential to support educational analytics -- i.e., relationships between writing skill and broader outcomes -- moving AWE beyond writing assessment and instructional use cases. [This paper was published in: "Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications" (p.101-108). Association for Computational Linguistics, 2017.]