The joys of constant vigilance: A case study of the student retention funnel

One of my campus partners copied me on an e-mail recently that reminded me why I so enjoy being a retention consultant for Noel-Levitz. The e-mail consisted of a 2011 fall cohort enrollment update. In mid-June this college’s retention director was providing an update to the goal for the fall 2011 cohort’s retention rate: about 17 percent of the fall 2011 cohort was not enrolled for the fall 2012 term. Also, she could account for those who were not enrolled—the list correlated highly with students who had been under review through this college’s early-alert system. Even better, the anticipated retention rate of more than 82 percent is significantly ahead of the retention goal for the cohort. This retention director also explained how much “melt” (the number of enrolled students who might withdraw) could occur and still have the cohort meet goal.

In a 102-word e-mail, this retention director encapsulated so many best practices that I had to share them in this blog. Let me tell you what I see from a chronological perspective. First, this college had a clearly documented retention goal for this particular cohort (I should note that this goal had been revised up a year earlier based on the performance of the previous cohort). The campus leaders who were the primary recipients of this e-mail knew the goal and had charged the retention director to mobilize the campus to meet the goal. She was also empowered to bring key campus constituents together to do the work. Clearly the president and cabinet had provided a strong foundation.

Second, a small group of carefully chosen staff had been meeting regularly throughout the academic year to monitor student progress, field early-alert reports, and intervene as necessary. The work of this group ensured that they were not surprised about the students who were not planning to return. There was two-way corroboration: the early-alert group (as well as mid-year academic performance and residence life indicators) had already predicted students not likely to return; the non-registered student list confirmed the quality of the observations of all those on campus who contributed to the expectations about which students would not return.

Third, the retention director had been monitoring the registration behaviors (for spring 2012 during the fall and fall 2012 during the spring) of this cohort since they matriculated in fall 2011. Preliminary persistence projections for spring 2012 had been good and had helped her predict a fall retention rate close to goal, but not this good! Then during the spring, efforts increased to follow up with students who should have been registering but did not. This monitoring and follow up was work, but it meant that there were no surprises. In fact, there were multiple opportunities for interventions.

Those are three big positives, yet there is still work to be done. Immediately following delivery of the e-mail report I described above, the vice president for enrollment and I both wrote back with the same question: do we have historical melt rates from this time of the year for previous cohorts? What can we realistically predict based on historical rates of enrolled students who do not actually return for the fall? The registrar reports that it is more common for non-registered students to register than it is for registered students to withdraw, but her testimony is anecdotal. No one has the historical data, but now we have a baseline. This time next year we will know better!

If you are a regular follower of this blog, you know Noel-Levitz retention professionals regularly reinforce the use of the continuing student retention funnel and some key metrics: persistence, progression, retention, completion, and graduation. This case study illustrates what can happen when a campus has clear goals for those key metrics and establishes action plans for making those goals come to fruition. Not every campus will reach every goal every year, but intentional monitoring of the metrics can lead to timely interventions and provide quality predictions that, when communicated across campus, can improve the ability of all campus employees to do their work.

Do you have an early-alert system in place, and if so, does it alleviate retention surprises? Do you have questions about how to establish a systematic, data-informed retention plan? Send me your questions and I will gladly share my insights with you.