Retaining students is one of the most critical issues facing colleges and universities today, yet few have put into place a comprehensive system that empowers them to effectively address student attrition. When an early-warning system identifies an at-risk student, it may very well be too late for the school to intervene successfully. To be truly effective, these systems require controls that can identify students likely to become at-risk. If the only ways to identify at-risk students are a drop in grade point average or attendance, the term “early-warning system” is a misnomer.
Many colleges and universities already have the data they need to create a true early-warning system. However, that data may live in silos across departments, campuses, and systems. Others already have a CRM system in place that could potentially aggregate that data and provide greater insight across the student lifecycle, but they continue to limit its application to recruitment and admissions.
What to Do?
First, beyond having the right technology and tools in place, institutions must realize that no two schools are alike: each has its own culture, environment, and mix of students. Second, the CRM system might not be flexible, making integrating with various data sources across the enterprise both a technical and potentially a political challenge. Finally, what would a school do with all that data if they managed to centralize it? How can they mold all the unique academic, environmental, and psychosocial data into a neat, meaningful report that ranks students’ likelihood of dropping out in a way that allows them to take action?
Faced with all these questions and variables, today’s institutions generally fall into one of five categories when it comes to implementing or updating early-warning systems.
1. Use an Ad Hoc Approach
Many institutions acknowledge that something needs to change to improve retention rates, but continue to take an ad hoc approach and do not use the data available to implement a strategy to guide their actions.
Example: A college or university realizes that students are dropping out of school between the third and fourth semesters. The school assumes this might be due to low morale, so they organize a pep rally and promote it by e-mail to students entering their fourth semester. However, they have no data to confirm this assumption.
2. Monitor CRM or SIS Data Only
These institutions recognize the value of data in their SIS or CRM systems for purposes beyond recruitment. They see the record of communications and support across call centers, help desks, and other traditional CRM outposts as potentially rich resources for recognizing retention issues early. Last-minute financial aid inquiries, housing issues, or even phone messages and e-mails that go unanswered become clues for identifying at-risk students.
While these institutions look beyond the traditional use of CRM or their SIS to address attrition, they still use a limited data set. Without a more holistic view of the student experience, retention efforts are reactive.
Example: If a student’s grade point average drops below 2.0 in the SIS, the institution reaches out to the student. By then it’s too late. The data that could have provided insight earlier might exist in another department or system. The student’s recruitment or help-desk records in the CRM system may have indicated that he/she had attended a work-study orientation, was having financial or housing issues, or requested a transcript be sent to another institution. There is no comprehensive system in place that pulls all these factors together early enough to identify a potential problem and proactively reach out to the student.
3. Add Elements from other Systems
These institutions use their CRM solution to aggregate data from other systems as part of their retention efforts.
Example: A school uses their CRM to collect attendance data, grades from the LMS, financial aid data from the SIS, or financial status from the bursar’s office. Combined with the traditional CRM metrics across help-desk and support services, the institution now has a broader and deeper view of students. The drawback here is that institutions often try to crunch these numbers manually. They filter numbers in Excel spreadsheets, Access databases, or small data warehouses, even though retention is a time-sensitive issue. While this process can broaden the span of data, it often fails to yield predictive analytics in time to be proactive.
4. Take a Business Intelligence Approach
These institutions apply business analytics to aggregated student data to address retention issues early.
Example: With the right queries, an institution might discover, for example, that students who live 10 miles or more from campus and who have missed three classes in the first month are five times more likely to drop out than those who live on campus. However, the university is still making an educated guess as to why. Layering in broader social and demographic data from outside the system might provide greater insight.
5. Include Psychosocial Factors
This advanced approach is more about understanding the student population as a whole and putting the pieces in place that could mitigate attrition. Market research or listening to students on social media, for example, can provide unique insight into the psychosocial factors that affect student performance.
Example: Consider the scenario where students who live 10 miles or more from campus and who have missed three classes in the first month are five times more likely to drop out than those that live on campus. Assume they represent 25 percent of the total student population. The school can look at this group to understand its demographics and determine their motivating factors. Would those students living 10 miles or more from campus want discounts on public transportation? Use shuttle services? Take more online classes if offered? Data inside and outside the system can be combined to form a 360-degree view of students and a truly proactive retention strategy.
Advancing Retention Capabilities
Of course, this all means asking a lot more of the institutional CRM system than in the past. The CRM system needs to be nimble enough to allow a school to easily build and launch a campaign and measure its effectiveness. Unfortunately, many CRM systems are still hardwired for recruitment or help-desk services. What’s more, integration of disparate systems on this scale can be complex and expensive, as well as a political challenge across departments. Enterprise-wide projects need strong champions and project management — not always available.
On the other hand, some institutions have adopted next-generation early-warning systems. Those schools have gone beyond reactive retention strategies to identify the root of attrition early, aggregating data from systems and departments, from inside and outside the system, to add up the little things that in isolation seem benign, but in total reveal students at risk. (For example, Indiana State University increased freshman student retention after making this move.)
A retention strategy within an institution is a continuum that should provide actionable data to support the retention program’s goals. It should center on access to, and understanding of, data. Taking a non-data-driven approach will lead to misdirected efforts and poor results. On the other hand, cost or resource limitations make it unlikely that institutions new to data-driven retention programs can support a full-fledged business intelligence approach initially. The key is to identify a retention champion who can support such efforts, understand what data exists in the organization, and then gather and use that data to make decisions, drive action, measure results, and modify activities accordingly. An organization should not strive to move to a specific step in the model described above, but rather improve how it uses data to improve retention. As an organization continues to grow its capabilities around using data, it will organically traverse the steps and should see increased retention of students in response to its targeted initiatives.
© 2013 Jason Soffer. The text of this EDUCAUSE Review Online blog is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 license.