Online Retention: The Tracking Model Matters

This post was written by Adam Binkerd, who served as executive director of student engagement at retention at Indiana Wesleyan University. I talked with Adam previously about student retention, and he also presented at our 2020 RNL National Conference. He was kind enough to share his expertise about online student retention in this following piece and talk with me in an episode of RNL@Home.

Measuring retention of online student populations seems to lag behind in comparison to other student populations. In discussions about setting up processes, the challenges and complications seem to be as numerous as the number of existing institutions. One consistent theme in these discussions is the reality that many institutions are trying to force-fit familiar tracking models in an attempt to understand their online student population. It’s a bit like trying to force a square peg in a round hole.

Whether it is key performance indicators (KPIs) tracked on a dashboard or objectives and key results (OKRs) written on a whiteboard, the powerful impact of metrics cannot be understated. However, it is important that the metrics are developed for the audience you are trying to measure.

Metrics have a positive influence on what gets done. Clearly the solution to retention issues then is more metrics, right? The authors of The 4 Disciplines of Execution asserted, “What gets measured gets done…but only for a while.” KPIs and OKRs are clear and quantifiable means of understanding a complex coordination of activities, but the trouble is they often do not connect with those working directly with customers at the front line.

I am a huge fan of metrics, but they tend to make numbers out of people, which can demotivate those who know the people. While working with Indiana Wesleyan University, I was regularly asked by peers, “What is a good model for tracking retention?” The university’s phenomenal success at increasing retention by reducing permanent withdraws by more than 26 percentage points in only 18 months was getting some attention.

My answer is simple. Whether you report retention by term, year, degree, or something else does not matter if you’re not making positive gains. The right model for tracking retention is one that fulfills the following four requirements:

Can be reviewed on demand. Many retention tracking models are established to only give results—gains or losses—typically after every term or school year. But this is shortsighted in light of the metric’s purpose. A rich tracking model for retention will keep the frontline workers and strategic leaders informed consistently enough for them to know which behaviors are working and which are not—and act accordingly.

Meaningful to learners. The first question asked of anyone reporting incredible persistence or retention gains should be, “How do you define persistence or retention?” Administrators need to quit playing the comparison game and realize that meaningful metrics should be based on their cycle, their culture, their community, and their goals. There is no universal definition. Your metrics should be something meaningful to your employees, the learners’ journey, and your institutional mission.

Insightful for support teams. Retention metrics are to your respective departments what value-propositions are to customers. The accounting department is not motivated to go the extra mile because of a metric related to students attending class—they have too many angry phone calls to be concerned about something like that. Nuance your metrics to be insightful and motivating to your respective support teams (i.e., advising, accounting, financial aid, faculty, etc.).

Challenging. The metrics need to be challenging and connected to the strategic plan. If you incorporate the three requirements above, your teams will rise to the occasion, but it all circles back to the metrics being easily available for tracking and decision making.

When running a race, it is not important that you have the exact same equipment worn by your opponent; it is important to have the right equipment for your team. Your tracking model will be unique, yes, but it should be predicated on the four elements above. If you cannot clearly identify these elements in your model, then I suggest that you might be trying to use someone else’s equipment for the race.

Watch the RNL@Home episode for an additional exploration of online retention

Adam Binkerd and I dive into online retention further during this episode of RNL@Home.