Financial aid offices experience very tangible benefits from automated award packaging. Staff members spend less time manually calculating and entering award amounts and have more time to spend counseling students. Improving students’ (and parents’) understanding of their financial aid packages improves their ability to make informed decisions about college. There is also the benefit to the financial aid operation of reducing the errors that inevitably result from manual processes. These are significant tactical improvements in a critical enrollment function.
In addition, many institutions use auto-packaging as a test exercise to estimate the impact of changes in awarding policies on the aid budget, or to understand potential losses in funding resulting from expected changes in federal or state aid formulas. The pool of admitted or enrolling students can be packaged under one set of policies and then re-packaged under a different set of policies to project resulting increases or decreases in aid. But senior leaders who might be reviewing the results of such a “test run” sometimes forget that auto-packaging is very different from predictive modeling. Auto-packaging can’t tell you anything about changes in probability of enrollment – that is, yield – that are likely to result from changes in aid policies.
To understand how changes in aid would affect enrollment behavior, statistical modeling is necessary. A predictive enrollment model assesses the impact that numerous variables (including aid) have on probability of enrollment (or re-enrollment, in the case of retention models). Once a valid model has been developed, it is possible to simulate changes in awarding by re-writing awarding rules and then analyzing how that change impacts probability of enrollment. This approach not only will show how aid expenditures might change as a result of a change in policy, but also will show how enrollments, net tuition revenue, and class profile may be affected. In contrast, using automatic packaging to re-package a group of students may be useful in estimating how much a change might add to or reduce the institutional aid budget, ASSUMING THERE ARE NO CHANGES IN ENROLLMENT BEHAVIOR (AGAIN, YIELD) AS A RESULT. If this caveat is not understood, relying on auto-packaging “test runs” can lead to unexpected and unintended results in the fall.
To truly estimate the impact of awarding changes requires the capability of predictive modeling applied to several years of admissions and financial aid data.
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About the author: Enrollment Management Consultant Bill Berg comes to S&K from Furman University, where he most recently served as Vice President for Enrollment. His leadership in enrollment management included overseeing offices of admissions, financial aid, planning and institutional research, and student employment. During this period, Bill successfully implemented a strategic plan designed to increase enrollment, increase selectivity, and decrease financial aid expenditures. Prior to his role as Vice President for Enrollment, Bill served as the Director of Planning and Institutional Research. In that position he supported student recruitment and retention efforts with financial aid optimization studies, enrollment projections, and admissions and retention studies.
Before Furman University, Bill served in leadership positions at Rhodes College and DePauw University. He has been active in professional organizations such as the Higher Education Data Sharing (HEDS) consortium, College Board, Commission of Colleges – Southern Association of Colleges and Schools, and the National Association for College Admission Counseling.