What happens when your primary enrolment forecaster gets sick? Or gets another job? Would your institution be left scrambling?
A few years back, my primary job included enrolment forecasting at a major university. It was among one of the best jobs I’ve ever had – great teammates, leaders who supported my growth, and lots of challenges I could sink my teeth into.
Enrolment forecasting itself was deeply fascinating. At the time we needed to not only forecast enrolments, but also compile estimates of tuition revenue based on a series of various tuition rate change scenarios for specific groups of students.
Of course, this introduced complexities. The model I inherited from a predecessor when I first started was made in Excel. Manual updates for each program in the model would be conducted each month during the forecasting cycle. To me, this process felt clunky and error prone. It became even more challenging once when we integrated tuition – it was common for our spreadsheet to lag for several minutes while performing calculations. We also ran into major version control challenges (I’m looking at you, “model Final final final amended.xlsx”).
It was very difficult to ask for help due of the complexity of this spreadsheet model. While my colleagues were all intelligent and lent a hand whenever they could, there wasn’t an effective way to pass the model back and forth. This proved more difficult when we tried to integrate our faculties into the planning process.
We then set out to modernize the model and store it on a more scalable platform. This involved a combination of an intake targets application, a model created in a programming language and its associated server, and visualizations created in Tableau. While this method largely helped with some of the major performance challenges of Excel, it unintentionally cemented that only a specialist with SQL skills would be able to successfully run the model from stem-to-stern, exacerbating the challenge of asking for help. In hindsight, this was a major oversight that assumed the office would always have someone with very niche skills.
From my conversations with Institutional Research Director’s and my own experience, finding ways to share the load can help teams collaborate on enrolment forecasting and reduce the risk of over-reliance on a single individual. I believe the following recommendations will help any institutional research team move towards this goal:
- Ensure cross-training on the entire process, from model building to maintenance, entering intakes, and summarizing results.
- Break the forecast down into smaller components so different people can succeed within different areas.
- Partner with IT (or an outside expert) to ensure that your platform supports your institutional needs.
- Build on the skills commonly found in your roles, rather than catered to any one individual’s skills. Alternately, identify skill gaps and provide training and opportunities to practice, or hire new staff to expand your team’s skillset.
How are you resourcing enrolment forecasting? Do you have the right tools in place for success? Does your team work together? Or does it truly rest on the shoulders of one person?