Andrew Drinkwater
In Parts 1 and 2 of this series, we discussed how targets are created and operationalized, where they begin and how they move through a process to approval and ultimate implementation by offices responsible for hitting the targets.
The process makes it sound so straightforward, right? In my experience, this can be a mirage. Today’s blog discusses the areas where institutions struggle to make targets a reality.
Over the years, I’ve seen lots of challenges getting the targets to work.
1. Subject Matter Expertise
Often, the people charged with determining the targets have little background suitable for the purpose. Ideally, targets should take into account market demand, institutional capacity, designs of future cohort (the size and shape of the institution), competition, and many other factors. Many people I speak with feel they don’t know how to set a high quality target that is informed by data and research. Instead, they end up using what they used last year, with some difference over time. Most years, this method will work. But it carries some risks:
- In times of change, old targets are no longer suitable.
- In programs that have consistently failed to meet their targets, it’s sort of pointless to continue to try targeting the same number. It’s like if I set a growth target at Plaid to triple our revenue every year, but every year we only grew 25%. It’s one thing to be ambitious, and another entirely to be completely out to lunch with reality. In this scenario, perhaps I’d be better to set a growth target of 40% - it will stretch my team to hit the goal, but it will be possible in a way that 3x probably isn’t.
- How do you carve out space for new or fast growing programs to hit their potential if no other program can lose any target space?
2. Lack of Scenarios
I still see a surprising number of institutions that do not use scenario-based planning. They run a single scenario, and that scenario gets updated between one and three times per year. While your target is fixed, there could be many ways to achieve it. For example, if your target is 1,000 students and you offer four programs, there are 41.4 billion different theoretical ways you could hit that target. Of course, many of these would collide with reality, but there will almost certainly be more than one way to achieve your target. Scenarios allow you to deeply understand the differences between these different methods of achieving your goal. It may well be that you have fewer students applying to Arts and more to Science than you expected. Different scenarios could allow you to see the impact on your overall targets, and combining these with scenario-based enrollment forecasts could allow you to see the long term impact of being above or below target by discipline.
3. Lack of Collaboration
I’ve always found the most robust enrollment plans involve input from a wide variety of stakeholders. Given my background in both central planning and decentralized academic units, I have an appreciation for perspectives that both bring to the process, and how working together can lead to better outcomes.
For example, a Dean may be aware of important conversations happening with a major donor in a way that an institutional research analyst may not be. The Dean may well know a major donation is just around the corner and only waiting on final media releases. If that donation happens, the Dean may need to grow a program way faster than historical precedent because they’ve finally cleared the physical constraints that had been holding them back.
Conversely, a Central planner is likely more aware of what is happening in the other faculties. They could be the first to alert the Dean of Arts that a substantial number of new students is going to be targeted in Business, and as a consequence ten more sections of English 100 need to be offered.
By building both processes and tools that allow different stakeholders to come together and bring their lens to the target setting process we can get more meaningful and accurate targets. Much like building IKEA furniture, having your stakeholders help determine the target may help them feel a sense of ownership and ultimately make them more likely to achieve the target.
4. Losing the Forest for the Trees
It’s easy to get bogged down in the minutiae of targets and lose sight of the big picture.
At one university that I have worked with, there was such a strong culture of making sure that targets were achieved that every faculty made sure they were a few students over target. The problem, in turn, is that the institution now has to figure out how to serve more students than it actually needed to. If you’re an institution funded by block grant, you seldom get more money for these students and have to do more with less.
Furthermore, getting stuck in the details can cause you to lose sight of the long-term plan. When done well, a multi-year target plan allows you to reshape your institution to serve your ideal students. But if you are only focusing on whether you are achieving the current cycle target at a granular level, you may lose sight of how you’re moving towards the longer term goal. Even if the numbers aren’t quite perfect, an overall trend in the right direction is good.
5. Losing the Trees for the Forest
Conversely, some lose the trees for the forest. In this paradigm, people consider the big picture but ignore the details.
For instance, if an institution offers programs with hard capacity constraints, such as Trades programs, it could be very problematic to exceed a target. From a central perspective at a large institution, being over or under by a few students is no big deal. In the Trades, being over by even a single student may force you to add an additional section, and you may not be able to find an instructor.
Another example is cohort programs. Often these programs have very intentional cohorts created – they bring in students from different disciplines, backgrounds, and so on. It doesn’t work well to admit just any student, you need to admit a specific student.
Finally, even though I just cautioned against getting stuck in the details, the details can matter. While missing a target in a single term isn’t much of a problem, if it becomes habitual it may be an issue. If a small program is consistently under target by 10 students (which probably wouldn’t matter much to central administration), it’s possible that a few years out that program may no longer be able to afford their faculty members. Remember, in a university setting students tend to stick around for 4-6 years. In that time, missing a target by 10 students per year could mean a total loss of 35-55 students in a single year. Depending on their tuition rates, that could be the difference of 1-2 faculty members.
6. Reconciling Data / Results
Enrollment professionals spend a shocking amount of their time reconciling data. In my own past working within institutions, I’d wager that I spent about half my time between putting together datasets on an ad-hoc basis and reconciling reports that our different stakeholders had.
Reconciling data often comes up in relation to targets in two places:
In relation to actual numbers of new intakes or overall students.
In relation to forecasted results of new intakes or overall students.
In the absence of having well thought out systems that automate the reconciling process, typically an institutional analyst ends up doing the reconciling.
Often, institutional analysts are highly educated and highly compensated. Their time should be spend on providing advanced insights and helping the institution determine next steps that could improve things. Instead, their time is spent making sure that spreadsheet A and B agree. This process costs institutions significant amounts of money and opportunities lost. It also can severely dampen morale for professionals who thought they were hired for analytics and spend their time instead on making sure spreadsheets match.
7. Data Updates
When determine targets, it can be helpful to review what targets were in previous years and how they compared to actuals.
However, at many institutions the process of updating to a new year is incredibly labour intensive.
At an institution I worked with, the “rollover” process started in early summer, and usually took a full month to complete. At the start of a new cycle, we’d overwrite any previous data (and usually lose the insights embedded within it), and update the remaining inputs once per month through the cycle.
The process took so long that we were seldom able to look at the targets themselves and provide advice on how they should be changed.
8. Your stakeholders don’t get along
Sometimes in universities, especially those with internal performance driven funding models, your stakeholders won’t all get along. For instance, the Dean of Arts may be livid that the Dean of Business is “stealing” their Economics students, and will do anything in their power to block Business getting things they want.
In some institutions, trust has been lost between Institutional Research and the faculties. The most common reason I’ve seen is that faculties or senior administration have lost faith in the department providing accurate data. They may be seen as gatekeeping, or past mistakes may have reduced their credibility. Reputations take a very long time to build, and only moments to fall apart.
9. It’s “Their” Fault
Just like how many of us believe we are better than the average driver, it’s very common in institutions to hear that some other program or unit is responsible for the decline of the institution.
Without a robust enrollment data culture, it can be easy to get lost in the perception that others are not pulling their weight. With a data culture, it can be possible to read too much into the details – over-indexing on point in time changes and losing the bigger picture.
10. Scheduling and Bureaucracy
At big institutions, it can be very difficult to get the right stakeholders together at the right time in the process. While hopefully the Provost can compel Deans to clear their schedule and attend, this isn’t always the case.
What ends up happening is a drawn out process that becomes rigid because there’s no way to be nimble. The meeting with the Engineering Dean happens in September, but the Science Dean couldn’t make it work until November. By this point, it may be too late to properly reflect that Science is planning to reduce seats in Calculus, while Engineering is planning to add more students that are required to complete Calculus.
The process of getting targets approved also typically involves multiple committees. At one institution I’ve worked with, there were departmental committees, faculty committees, an enrollment committee, an enrollment steering committee, Senate committees for undergraduate / graduate studies, eventually Senate, and occasionally the Board of Governors (for new programs) involved in the approval chain. While important for collegial governance, processes like these can make it difficult to adapt targets when new market or program realities emerge.
Stay tuned for the final post in the series: how predictive modelling and artificial intelligence can empower enrollment planners.
Ready to try collaborative enrolment forecasting?
Further reading on organizational target setting and stakeholders: Gagné, M. (2018), From Strategy to Action: Transforming Organizational Goals into Organizational Behavior. International Journal of Management Reviews, 20: S83-S104. https://doi.org/10.1111/ijmr.12159
Greve, H.R. and Teh, D. (2018), Goal Selection Internally and Externally: A Behavioral Theory of Institutionalization. International Journal of Management Reviews, 20: S19-S38. https://doi.org/10.1111/ijmr.12138
Nonet GA, Gössling T, Van Tulder R, Bryson JM. Multi-stakeholder Engagement for the Sustainable Development Goals: Introduction to the Special Issue. J Bus Ethics. 2022;180(4):945-957. doi: 10.1007/s10551-022-05192-0. Epub 2022 Sep 1. PMID: 36065323; PMCID: PMC9435417.
Science Based Targets Network (2024). Stakeholder engagement and science-based targets. (Version 1.0)