Hierarchical Bayes is the standard for individual-level utilities, but it is often too slow to test the modeling decisions that go into it. This session shows how Empirical Bayes, a fast alternative of HB, can search model specifications, tune estimation settings, and warm-start HB to cut burn-in. Across completed CBC and MaxDiff studies, we report where EB-based decisions transfer to full HB, where they do not, and how much project time this approach can save.