Ask any high school senior applying for college—the pressure can be brutal. And while students worry if their SAT scores and extracurricular activities are enough to get them accepted, colleges and universities are facing an altogether different dilemma.

When the common app became popular around ten years ago, students were suddenly able to apply to more schools without expending much additional effort. As a result, many schools began to experience a rise in the number of applicants and a drop in the percentage of accepted applicants who enroll—a statistic which can hurt a college’s reputation.

Among four-year, private, not-for-profit colleges, that percentage fell to 34.5% in 2017 from 49% in 2003, according to a Wall Street Journal analysis of federal data. To keep their enrollment yield up, schools are focusing on finding the motivated students who are more likely to attend so they can fill their available slots.

It’s no surprise that schools, just like other businesses, are leveraging data and analytics to locate prospective students likely to enroll. Read on for three examples of how higher education is using data to optimize their marketing strategies and make the admissions process more efficient.

Determining Demonstrated Interest

In order to sort through the increasing number of applicants, competitive schools are taking advantage of online data to give them an edge in mitigating a low enrollment percentage.

Many schools now track the following about students online:

  • How quickly they open school emails and whether they click links
  • How long they spend on the school’s website
  • At what point in high school they began looking on the school’s website

The factors above are then used to determine a student’s demonstrated interest and how serious the student is about attending that particular college or university. Admissions officers consider a student’s level of interest during the decision-making process, increasing the likelihood of accepting students who will enroll.

Differentiating Recruitment Leads

College admissions officers are always looking for ways to grow the number of applications in the incoming year’s pipeline. Reaching out to students with the requisite SAT scores, for example, is one way to acquire leads, but it won’t help predict which students are more likely to enroll in the coming year.

Predictive analytics enable recruiters to make their efforts far more targeted. Lead scoring can help identify characteristics of students more likely to respond, enroll, and graduate from the program.

With this information in hand, recruiters can spend more of their mailing and outreach dollars on leads with a greater chance of enrolling—and feel confident they are using their time and marketing budget wisely.

Leveraging Other Data

If you want to know more about the kind of student who enrolls in—and will graduate from—a college or university, it pays to look at the current student body.

Many recruiters and admissions officers analyze the demographics of students who graduated on time with high satisfaction scores to determine what characteristics—particular majors, school activities, and GPAs—they can use to predict which applicants are likely to enroll.

For deeper analysis, schools can also use Dynamic Scoring to build data models based on:

  • The number and types of interactions students have with advisors
  • Third-party demographics
  • Geographic data

In addition, Dynamic Scoring enables schools to use progressively more complex data as it became available, painting a picture they can use to optimize their outreach efforts, whether it’s purchasing a list of students, creating a mailing list, or ranking their best enrollment prospects.

Across college campuses, finding students who are likely to enroll is a huge effort for recruitment admission staff. Thankfully, predictive analytics is making the process more precise and efficient than ever.

To learn more about how your college or university can use data to meet your recruitment and admission goals, read Improving Marketing Programs With Predictive Analytics.

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