Increase the accuracy of your Institution’s annual tuition and discount forecast through student enrollment, tuition, pricing, and discount drivers. Easily follow the matriculation of each cohort’s KPI’s and how manipulation of model drivers impacts your Institution’s 10-year strategic outlook. Understanding the financial impact of these drivers allows Institutions to make strategic decisions related to future enrollment generation, tuition pricing and discount factors. The Student Enrollment and Cohort model’s purpose is to forecast future cohort enrollment numbers by Full-Time Freshmen, 2nd Year Transfers, and 3rd Year Transfer students while providing instantaneous tuition and expense predictions from your key model input drivers. As enrollment assumptions are manipulated, the model also allows the opportunity to adjust tuition pricing to predict total tuition by each cohort on an annual basis.
This schedule displays each cohort’s enrollment numbers by cohort and waterfall view by year. The drop-down easily allows the view to change from full-time freshmen vs. transfer students.
The Enrollment Summary displays an Institution’s KPI’s, as well as a visual representation below that synchronizes with each respective line item and is adjustable by Full-Time Freshmen vs. Transfer Students.
Tuition % and Enrollment % adjustments from baseline assumptions allow a Total Tuition forecast to be adjusted for each year by each specific cohort.
Each Cohort’s annual tuition metrics are clearly identified in a clean bar chart view to show trends and how each cohort’s total tuition compares to one another.
A Key Model Inputs landing page allows assumptions to be made. From here, output and other reports are easily accessible through buttons to toggle between other dashboards.
The Enrollment/Cohort Model allows an Institution to enter a starting class size and adjust their growth rate to plan a cohort’s enrollment, as well as set and adjust numerous other variables such as Retention, 4-Year Graduation Rates, Auxiliary Impact, and Percentage of Students on Leave. The model accurately forecasts enrollment with the ability to view a year over year waterfall approach and calculates functional costs, cumulative tuition impact, and the total 10-year bottom-line impact based on the number of assumptions a user sets.
Visualization of Key Metrics in Graphical Display
The Enrollment Management Summary feature allows a Higher Education Institution to view various metrics by Full Time Freshmen, 2nd Year Transfer Students, and 3rd Year Transfer Students with a graphical summary display. Metrics captured in this feature allows users to view the output their assumptions have on Entering Cohort size by year, Total Undergraduates Enrolled, their Annual Revenue Impact (including Auxiliary), as well as Staff and Overhead costs to show a bottom-line annual financial impact number. These key metrics can synchronize with various graphs and charts allowing the user to have a clean reporting view that updates on the fly as different outputs are selected.
Student Cohort Tuition Driver Analysis
The Cohort Tuition Analysis feature allows the adjustment to baseline assumptions around Tuition Pricing and Enrollment Size by each cohort in a given year. A percentage adjustment to either Enrollment or Annual Tuition Pricing can drive total tuition forecasts annually for each cohort. Model assumptions display a 6-year waterfall outlook for each cohort that easily models how much tuition is planned for each cohort over a given 6-year period with the ability to adjust tuition pricing or enrollment size up or down.
Forecasting Tuition, Fees, and Discount Rates
As users make assumptions around baseline Gross Tuition, Fees, and Discount Rates, the model allows growth rate adjustments to show a 10-year forecast for each metric and splits each assumption out by entering Full-Time Freshmen, 2nd Year Transfer Students, and 3rd Year Transfer Students. This allows an Institution to easily view and identify a 10-year strategic outlook for key drivers of their financial performance. The output by year can easily be analyzed by adding various year over year graphical summaries to identify trends or gaps in their model.
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