Last week, an enrollment manager from a small liberal arts college in the Southwest told me she was going to start recruiting in New York City.
“Why the Big Apple?” I asked, intrigued.
Her reply was a little alarming: She figured the students there might like to get out of the cold hustle and bustle and attend college in a quaint warm town. This kind of “reasoning” is pretty common, which is a shame because it can also be pretty costly. Targeting and penetrating a new market is a high-dollar, high-stakes decision.
However, for many enrollment managers (EMs), “normal” local markets that reliably filled seats in the past no longer generate sufficient application volume, so decisions about new markets are sometimes driven by hunches and hearsay, instead of data and analytics. For most EMs I talk to, there are two core questions behind this guessing game.
“How do I know which students are most likely to enroll at my school?” and “How do I know where those students are?”
In a world where your job is harder than ever and resources are scarce, the good news is predictive models can help you design an effective (and cost-effective) targeting strategy.
Big data can reveal new market opportunities
Ten years ago, EMs (including myself), could just follow growing high school graduation rates or a localized surge in test scores. You didn’t need analytics to find new students.
Fast forward to today and many colleges are running basic analyses to identify pools of students who fit their desired profile, usually honing in on grade point averages, proximity, and the like. These analyses are typically based on historical enrollment data and basic student academic information. Though helpful, the view afforded by these analyses is still too limited to inform the decision to enter a new market, given the complexity, costs, and tradeoffs involved.
Big data—defined by Inside Higher Ed as the never-been-seen types and scale of data now available—is needed to guide colleges toward markets that will produce favorable results. Because the necessary data is often isolated and not shared, and because data science talent can be scarce and costly, this type of predictive modeling is difficult for most colleges to do with any regularity or at scale. However, it can be a powerful tool to make cost-effective, evidenced-based new market decisions.
Case study: Predictive modeling leads to 19 new enrollments
One of our clients, a university in the Northeast, was already recruiting nationally but needed to expand into additional markets to meet enrollment goals.
To help them, we first evaluated the university against approximately 350 schools to identify shared trends among student characteristics, demographics, and application patterns. We then drew on a national consumer database, among other data sources, to locate students with a high probability of being interested in our client.
The model we developed rank-ordered 21,900 U.S. zip codes—nearly all of the U.S. zip codes that had 10 or more students available to contact. The resulting map of the ranked zip codes is below, while the area highlighted in the circle represents the focus area recommended.
The highest scoring markets exhibited favorable demographic and behavioral characteristics, revealing students with a high likelihood to apply.
Is moving into a new market the right strategy for you?
Even powered by proper analytics, recruiting in new markets is an expensive endeavor. It requires hiring staff to travel to or eventually reside in the market to meet with high school counselors and attend college fairs. And it often takes several years to establish your brand even with the best outreach and staff presence.
Before making the decision to go into a new market, consider asking yourself these questions: Have we exhausted our primary markets, or are there more or different students we could target? Are there smarter, more digital-savvy recruitment marketing techniques we could deploy to produce better results? Are there high-touch yield strategies that might relieve some of the burden of our search efforts?
In my experience, enrollment offices struggle to establish formal mechanisms to stay connected to staff who are traveling to new markets and are closest to the nuances and needs of the students in those areas. That’s a shame because recruiters learn a lot while on the road shaking hands at college fairs and hearing directly from prospective students how their brand is perceived in that area—market intelligence that, unfortunately, chief enrollment officers rarely hear because their feedback loops to recruiters are weak or non-existent. If you do enter a new market, I’d encourage you to solicit frequent feedback from recruiters.
Enrollment analytics can help you identify strong new markets, but local knowledge plays a critical role in whether your efforts in those locations actually yield more students. In today’s world of student shortages and scarce resources, enrollment offices must rely on accurate predictive models and proven best practices, not hunches and hearsay.