Institutions Should Use Analytics to Monitor and Improve Financial Health and Success

Data-driven decision-making is critical to achieving institutional success in higher education. In a previous blog, we talked about how analytics can help institutions attract the right-fit students and keep students on the path to graduation. In this post, we discuss measuring an organization’s financial health via understanding academic program contributions and using financial analytics.  

Academic Program Contribution

Measuring program contribution is a critical component of monitoring an institution’s overall financial health. Academic program analytics help institutions understand which offerings and programs are the best fit to enable long-term success in terms of keeping students happy, meeting mission objectives, and more. Organizations should not make any significant program decisions without a full understanding of program economics and the revenue and costs associated with programs.

Each program is unique. A liberal arts program is going to require a different set of courses than a health science or engineering program. Demand for programs differs by popularity, location, and return on investment. A singular structure for measuring costs and revenues across programs may not provide an accurate picture of financial contributions.

For example, a specialized, high-cost nursing program may seem a likely candidate to sunset if an institution wants to cut costs. Upon closer examination, however, data may point out that this is one institution’s most profitable programs. This may not be the same case elsewhere at a different institution.

Likewise, revenue per student credit hour varies from program to program, since students pay different tuitions depending on subsidies, scholarships, and grants. In some cases, programs associated with in-demand and competitive career paths like nursing or software development may generate more revenue because there are likely more full-time students and a waiting list to get into the program.

It’s also important to look at the costs of required courses within a program as well as courses taken outside of the specialized curriculum. Looking at a nursing program again, students are required to take English courses and anatomy classes, but English majors are not required to take an anatomy class, although they may choose to do so. Overall, English is a lower-cost course than anatomy due to the specialized nature of the latter. Schools need to include departmental and non-departmental course revenue and costs per credit hour in their analyses.

Financial Analytics

Financial analytics help institutions use data to manage resources and maximize success using the key performance indicators (KPIs) and ratios. Some of these KPIs include:

  • Operating results
  • Resource sufficiency and flexibility
  • Asset performance and management
  • Liquidity
  • Debt management

The Composite Financial Index (CFI) score is a well-recognized financial model in higher education that uses analytics to provide a quick look at the overall financial health of an institution at a single point in time. It includes four commonly used financial ratios:

  1. Primary reserve ratio, which measures financial flexibility
  2. Net operating revenues ratio, which looks at operating performance
  3. Return on net assets ratio, which measures asset performance
  4. Viability ratio that measures debt and liquidity

Using financial analytics, institutions can take the ratios, calculate their relative strength and weight, and add them together to generate the CFI score on a scale from -1 to 10. Generally, a CFI score of less than three indicates the need for serious attention. A score of greater than three is much more positive and indicates an opportunity for strategic investment.

Measuring financial health is extremely important in today’s dynamic higher education market. Data helps organizations identify patterns and opportunities for cutting costs and streamlining operations. When institutions make decisions based on facts and data, they can be much more agile and can adjust to market changes quickly.




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