Enrollment Initiatives: Analysts Belong at that Table

There is a conceptual realm, and a technical one. Too often, people think these are distinct departments, with distinct conversations. Some of the best results we’ve seen, though, come from cases where the big-picture thinkers team up with the folks who dig into the data. And team is the operative word, because the big-picture thinkers (administration, for convention’s sake), and the data-divers (analysts) have a lot to tell each other.

Plenty of benefits come from that collaboration, but one I most recently witnessed (again) is how valuable the analyst perspective is at the “initiative” or “strategic plan” level. As the Higher-Education market becomes  more competitive over application numbers and admitted-student yield- and it is- colleges are creating strategic plans to ensure their seats are filled. During that process, being able to refer to someone who has been working with the data, and witnessing trends (or who can look into trends that may have gone unnoticed) is critical.

Perfect example: Increasing applications, increasing enrollment. This became most poignantly apparent with coverage this past summer about Drexel’s decision to limit their “fast app” offerings. Their article is here, but the long and short of it is that fast and easy applications were increasing application numbers, and decreasing yield rates. This is a case where an enrollment initiative had nearly the opposite of its intended effect.

More to the point, how did they discover this? They looked at the data. This doesn’t have to be a retroactive process though. Your institution’s analysts know what’s going on, and with their perspective, or rather, with an understanding of the data, you can predict how your initiatives will really impact your reality.

Thought experiment: Are you planning on recruiting a slightly different profile of applicant in the coming cohorts? How do you expect that to impact your enrollment figures?


There’s a very good chance you have an idea, and it’s probably based on trends you’ve witnessed. Your data can tell you much more than “increase” or “decrease” though. In a circumstance like Drexel’s, it might have told you that the subgroup of applicants you’re planning to increase enrolls just half as often as your general population (or less!). When your analyst gives you that kind of report, you can do a better job of informing expectations, and maybe even use the data to structure the initiative accordingly.

So, how are you supporting your policy decisions with data? What have been some of the obstacles to using data more interactively in your decisions?