Although colleges and universities interested in the most cost-effective way of diversifying their entering class or meeting enrollment targets have an array of analytical techniques to choose from, issues of accuracy, scope, and usability continue to slow their widespread use. To address these issues, this article develops an institutional enrollment probability model using logistic regression that explores the use of an alternative measure of average price sensitivity, one which produces and takes into account each individual student's price sensitivity. When the model was applied to the more than 13,000 students admitted over a four-year period to a private university located in the southwestern United States, the resulting average price sensitivity was significantly lower than that found in the empirical literature, which is hardly surprising given the non-linearity of the logistic distribution. In the final section, the full set of student-specific price sensitivities is used to describe how financial aid and admissions professionals can work together to maximize the value of each additional scholarship dollar. (Contains 4 tables and 2 figures.)