Several analytic strategies exist for opening up the "black box" to reveal more about what drives policy and program impacts. This article focuses on one of these strategies: the Analysis of Symmetrically-Predicted Endogenous Subgroups (ASPES). ASPES uses exogenous baseline data to identify endogenously-defined subgroups, keeping the analysis of some postrandomization choice, event, or milestone grounded in the strength of experimental design. Building on lessons from prior applications of ASPES and also adding some new analyses, this article focuses on four specific practical considerations: first-stage prediction success, assumption credibility, data availability, and sample size. Discussion implies the optimal conditions for effective application of ASPES and points to future research that can enhance the overall tool kit of "what works" analyses.