Why? Because it answers the questions ‘What should we do?’ and ‘What can I do to meet my goals?’
Predictive analytics alone only gets higher education institutions halfway to what is needed to truly advance your institution and its goals. It tells you what will occur, but what happens when that outcome isn’t what you need or what you expected?
You need something that will tell you specifically how to change that outcome.
That’s where prescriptive analytics comes into play for colleges and universities.
Keep reading below to find out more!
Prescriptive analytics uses continuous data to go beyond anticipating what will happen to explain why something will happen, and recommends actions to take.
These advanced analytics are all about providing higher ed. institutions advice on how to make something happen by prescribing a list of actions to guide you towards changing outcomes.
Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made.
For example, it’s April, and your enrollment office receives a prediction that its currently tracking below enrollment targets and you need to find out how to change this outcome between now and May 1.
During that window, your institution is immersed in the appeals process as well as planning to host prospective students for a campus day visit.
If you have a pool of prospective students that are undecided and the two options above, wouldn’t it be nice to know which action to take and its impact to make up that gap to hit enrollment targets?
With prescriptive analytics, you can understand if they’re more motivated by a campus visit or additional aid and why to prescribe the best action for each student. Taking these actions can exponentially increase the likelihood of them enrolling at your school.
Sounds great, right?
However, because prescriptive analytics is so incredibly powerful, it is essential to cut through the noise, to understand what it can do.
Find out what you need to know about prescriptive analytics below.
5 must know things about prescriptive analytics in higher education
1. Prescriptive analytics’ super power is explaining the ‘why’
Prescriptive analytics predicts not only what will happen, but also why it will happen. It provides recommendations regarding action that will take advantage of the predictions. This allows for data driven advice created by simulation and optimization.
2. Prescriptive analytics is applicable across higher education
Higher education institutions can use prescriptive analytics in all levels and functional groups within the organization.
Meaningful applications exist in marketing, recruitment, admissions decisions, financial aid award, student advising, academic planning, financial forecasting and executive planning.
And, can be utilized from the President down to individual recruiters and advisors.
3. Prescriptive analytics improves outcomes
Higher education institutions can use prescriptive analytics to improve the relationship between the student and the institution to change outcomes.
Colleges and universities using prescriptive analytics have been able to increase enrollment yield (see example above), optimize net tuition revenue, optimize financial aid, and improve retention or graduation rates.
By understanding what drives each student’s behavior and by how much, optimized plans can be created to meet goals.
4. Prescriptive analytics offers continuous, real-time advice
Prescriptive analytics runs 24 hours a day and continually processes new data as it becomes available to re-predict and re-prescribe solutions.
Taking into account results of prescribed actions is important in continuing to be accurate with predictions and advice.
5. Prescriptive analytics is hard
Prescriptive analytics relies on sophisticated analytics tools, techniques, and technology, like artificial intelligence, machine learning, heuristics, and algorithms, which makes it a costly challenge to implement and manage.
This begs the questions on whether it’s in your institution’s best interest to build a prescriptive analytics engine internally or to partner with a point solution, which you can read about here.