Data and Coaching Combination Solves Retention Crisis

The average institution loses just shy of $10 million in revenue annually due to student attrition. In fact, nearly half of students who start college don’t leave with a degree in hand, causing them to miss out on valuable career and life-advancing opportunities.

Unfortunately, limited institutional resources dedicated to student reten­tion can make it hard to effectively scale successful intervention strategies. While more and more institutions are looking to technology to solve the issue, technology alone isn’t enough to thwart the retention crisis in higher education. To truly make an impact, colleges and universities need to look to predictive retention technol­ogy combined with proactive coaching interventions.

A data + coaching model empowers faculty, administrators, advisors, and oth­ers with necessary insights they can use to improve student success. Data serves as the trigger for support teams to not only reach out, but more importantly to establish meaningful and personal con­nections that keep students engaged and persisting to graduation.

Leverage big and small data insights

Your institution is likely full of valu­able data sources, including your SIS, LMS, CRM, and more. Each contains information that can help establish a big-picture understanding of your insti­tutional success, yet in isolation, each data source only provides a single piece of the puzzle. Your teams can connect with students in a truly meaningful way by utilizing big data to create predictive models and small data insights to empow­er coaches to intervene effectively with relevant and highly personalized support.

Big data interventions help your team discover correlations between student information and student behavior. This is typically based on a student’s histori­cal and personal profile, which is critical to identifying your at-risk students. This information is often gathered during the inquiry and admissions process. Big data modeling should incorporate prior academic data, life events, financial aid information, and registration data.

In addition, utilizing small data, or individual data points, can help provide your advisors and success coaches with context to create personalized interven­tion solutions for these students. Small data modeling can incorporate indicators such as academic performance, classroom attendance, and course management soft­ware usage, giving your support teams a snapshot of a student’s current progress and the information they need to person­alize intervention solutions for individual students.

How proactive are you?

According to the National Student Clearinghouse, one-third of students who drop out do so after only one term. That means that early action is the most important action. Waiting for signals that a student is struggling, such as midterm grades, can often mean it’s too late to make a difference.

The key is proactive support. Discov­ery calls early on (specifically to students identified as at-risk) can help your teams gather information to better assess learn­ing and extracurricular issues, as well as student objectives and goals. This allows your team to put an action plan into place and connect with students prior to them showing any signs of disengagement.

Connecting students with institutional academic resources right from the get-go is also advantageous. In fact, students are often unaware or unfamiliar with institu­tional support resources. When students know there is someone familiar in their corner, they are more comfortable reach­ing out themselves.

What does retention strategy say about your institution?

The integration of data and coach­ing can boost your retention rates and provide the kind of support your students need. When assessing your current reten­tion approach, ask yourself the following:

1. Does your retention strategy effectively incorporate enrollment, academic, and behavioral risk factors to help find struggling students?
2. Does your retention strategy empower your academic advisors and success coaches with meaningful data insights to act proactively?
3. Does your retention strategy effectively integrate and leverage institutional data sources, including SIS, LMS, CRM, and more?
4. Does your retention strategy provide insights to help improve your future recruitment strategy as well?
5. Does your retention strategy’s total cost (technology + human capital) provide you with a strong ROI?

While successfully graduating every student you worked so hard to enroll may be impossible, graduating more students is attainable with predictive retention technology combined with student success coaches. A data + coaching model makes information actionable. After all, data itself has little impact on the retention process without action, and coaches and advisors have less impact without information. When data and coaching work hand in hand, more students succeed.