How Higher Ed Can Use Predictive Analytics for Student Recruitment and Retention

The use of predictive analytics has enabled colleges and universities to perform critical planning for things like enrollment and student retention, and also to understand trends that impact academics and student services. 

With the right mix of data points, schools are better equipped to make smart choices that will impact every aspect of the educational lifecycle. At a time when higher education administrators are anxious about enrollments and operational expenses, being able to predict how to allocate resources and funding can be a major benefit to surviving the first school year after COVID-19. 

Using data to make predictions is nothing new for innovative colleges. In fact, it’s becoming an increasingly useful tool for admissions planning and to help schools avoid the coming enrollment cliff. But in the midst of preparations to resume classes in the fall after months of dealing with the effects of COVID-19, many schools are wondering if anything is predictable in today’s environment. 

Trends among prospective and current students are in a constant state of change, which means that predictive models that worked in the past are no longer usable. With so many unknowns facing colleges and universities, it can sometimes seem that admissions, operations, and IT leaders are increasingly having to make decisions in the dark. Predictive analytics, however, can shed light on a challenging situation.

Benefit from predictive analytics  

To benefit from predictions, schools have to integrate vast amounts of data from various repositories, and then make decisions about how to allocate their organizational attention and resources. Armed with that information, they can make informed decisions that reduce operational friction and improve the acceleration and implementation of actions that impact goal-driven outcomes.  

Some critics are suggesting that predictive analytics are ill-equipped to support the current needs of colleges and universities. The argument is that available data about student intentions and needs have changed so much over the past few months that it can’t paint an accurate picture of what’s to come.  

But the whole purpose of using data to make decisions is predicated on when the data is used. And data changes continuously, so predictions are always based on new information that is informed by changing behaviors. At times of massive change, predictive analytics is probably at its most effective, and colleges and universities are going to benefit from using it as they plan for the fall of 2020 and beyond. 

Let's consider the promise of predictive analytics and look at what schools have been able to do with it:

• Create programs and frameworks that make student success a primary element of the school’s mission
• Enable a discipline of adaptability and flexibility that enable schools to meet changing needs as they arise
• Deploy technology effectively to solve operational issues
• Maintain quality as a core component of all student-directed efforts

Improve student outreach and retention

Even with so much change happening as a result of COVID-19, colleges and universities are on a never-ending cycle of recruiting, enrolling, and retaining students.  Irrespective of how those students are going to learn, be it through online classes, on-campus, or some hybrid version of the two, schools need to identify students who will be a good fit, convert them into students, and then retain them for the duration of their college experience. That process starts with data.

Communicating with prospective students has always been heavily informed by data. For starters, schools won’t be able to host as many students on-site, as travel restrictions are delaying or even canceling traditional recruiting events. That means that digital communication will become a particularly important channel for marketing efforts, and those efforts will need to be highly targeted to get the attention of prospects who have many options in front of them. 

Schools are able to reach potential students through paid digital media ads, direct mail, email nurturing, and other online formats. These efforts will likely provide schools with even more accurate data about students’ intentions because these channels also give them an avenue to collect data as well. As that collection of information grows, it starts to inform decision-making and creates the ability to start making predictions. 

Some examples of data sets that schools will be able to collect through direct communication include things like:

• Geographic interest: understanding where pockets of interested students live will help schools maximize their marketing spend by investing heavily in these areas. Conversely, they can reduce spending on areas where there is little interest.
• Financial aid: schools can ascertain the potential for students to fund their educations and the resulting financial aid requirements that need to be provided for students. This will be a huge benefit to economic planning.
• Hiring: admissions departments will be able to make predictions about the number of incoming students  which can be used by HR teams to scale up or down as needed. 

Similarly, as current students spend more time away from campus due to sheltering-in-place, it’s critical that schools maintain communication with them. Keeping students informed of upcoming changes and guiding them through an uncertain future will help them stay connected. It also is an important aspect of student retention.

Research has shown that when students are having their needs met and have actionable ways to achieve their goals, retention rates rise significantly and they stay on the path to completion. With data about students’ patterns, backgrounds, and behaviors, schools are identify specific areas where they can support a student who is at risk. Some of these include:

• Predict when a student is likely to drop out. When students’ grades decline or they continually take less demanding workloads, they may be on a path to leaving school. Intervention through academic counseling, tutoring, or other academic services can keep a student academically engaged and feeling connected to their school.
• Identify students who are facing financial challenges. When tuition bills are habitually late or if a student has a complex web of financial aid sources, algorithms can alert students to alternative sources of funding. Or there could be a process that signals the financial aid office to contact the student to initiate a meeting to explore options to fund their school experience.
• Alert when a student may be facing a mental or physical health risk. Health services information can be pulled together to identify students who may be at risk for certain types of health issues. Student services organizations can be marshaled to help the students take part in counseling and other medical and social services to ensure wellness. 

Students may not be interested in predictive data, but what colleges and universities do with that data is critical to students’ success. By employing predictive intelligence and analytics, schools can help students do more than simply navigate the four or more years of college. They have the tools for a meaningful experience that keeps them engaged through graduation.