Tuition Discounting

Can Predictive Analytics Optimize Unfunded Tuition Discounting?

May 22, 2020

The most recent NACUBO report confirms that the average tuition discount rate at private nonprofit institutions continues to march higher. The estimated rate for first-time undergraduates now stands at 52.6% for the 2019-20 academic year. Despite this decade-long trend, the report also indicates that over half of the surveyed institutions had flat or declining enrollment. This trend is widely seen as both detrimental and, in many ways, unsustainable.

Employing a potent combination of predictive analytics and what-if scenarios, institutions can better manage and optimize their aid offers. Think about how valuable it would be for you to forecast the impact of varying offers on a prospective student’s probability to enroll. It could be done prior to making an offer or even for an adjusted offer during final negotiations. This would enable valuable aid dollars to be allocated much more efficiently. Increasing offers for those who would likely attend anyway can be mitigated.  

A typical use case involves a list of prospects that have either applied or already been accepted. This list would be filtered to identify those students that best fit the criteria for meeting institutionally established enrollment goals. Instead of simply ranking that list by probability score and then acting on those with target potential, it is first run through a what-if scenario. The prospects are re-evaluated according to the change that a given amount of aid is predicted to have on their probability to enroll.

Those previously predicted with moderate propensity to enroll may now be much more likely, justifying an increased aid offer. Prospects for whom an increased offer has a negligible effect on the probability score can be further analyzed. The goal is to determine the degree to which lowering an offer becomes detrimental to matriculation chances. You now have the insights you need to make optimal decisions regarding financial aid packages.  

Combating runaway tuition discounting doesn’t require that you abandon the practice outright. A happy medium to manage it is possible using a data informed approach. Watch our webinar on applying predictive analytics to recruiting and retention to learn more.   

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