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Actionable Analytics for Enrollment Managers: How to Stop Worrying About (and Love) the Data

The EvoLLLution | Actionable Analytics for Enrollment Managers: How to Stop Worrying About (and Love) the Data
In an era of limited resources, leaders leverage data to determine where best to direct their efforts and use that same data to get buy-in for new approaches and ideas.

Enrollment managers live in a world of negative data points—declining numbers of high school graduates; a skew in the composition of high school graduates towards families with constrained incomes and perhaps no history of attending college; excess capacity in many institutions; and new variables (like Early FAFSA), which add a measure of unpredictability.

When you’re in the glass-half-empty mode, you are Sisyphus pushing that boulder up a hill. When your glass is half-full, it’s dog-eat-dog competition where the only way your institution “wins” is for others to lose. Isn’t that an appealing description of how we provide access to higher education, one of our most cherished public goods?

No doubt, these are challenging times to lead an enrollment team. In addition to negative external factors, there are internal pressures as well. Demands and expectations are high; it may not be enough just to fill the class (which at many places is a Herculean task!). Presidents and trustees expect us to produce more net revenue, and it would be nice if we could also increase academic profile plus diversity of all kinds. All of these wants come at a time when budget constraints make new operational resources unlikely.

So what does data mining have to do with achieving enrollment success? Some would argue that, in our current market, it means everything.

Enrollment managers have moved beyond the experiential and anecdotal to extract meaningful patterns and correlations in their large data set. Our goal is to find patterns of student behavior that allow us to maximize our interactions to yield the results we are seeking. What are our meaningful market segments? Can we better target students and communicate with them more effectively? What does filing a FAFSA really mean? What’s the likelihood of enrollment when a student visits multiple times?

The place to start is making better use of data to set realistic goals and drive consistent growth. If you’re a novice, I recommend taking a hard look at your current resources and assess your expenditures. Your goal is to identify which investments drive success and which don’t. Colleges have a tendency to do what they’ve always done, or to hold an event/print a brochure/change the website, because someone saw it at another institution and thinks we should do it, too. When times were good, there was less imperative to look at things more carefully. Spending money without knowing the efficacy is wasteful and inhibits innovation. To free up money to try new things, we need to know which old things aren’t working.

So how do you identify new opportunities to pave your institutional path to success? Success is, of course, a fungible model that will be defined differently from campus to campus. Regardless of the definition, these tough times require new analytic leadership skills.

The best path to success is entrepreneurial thinking rooted in meticulous data mining. (By the way, if anyone has created the perfect regression analysis to predict “if you do these things and he/she does those things, then he/she will enroll” please let me know!) Since my Tinkerbell wand is low on pixie dust, I use data-driven analysis to allocate our precious human and financial capital.

In general, you’ll want to determine what to benchmark. Inquiries? Applications? GPA? Campus visits? FAFSA filer? Who enrolled? Who persisted to graduation? Your campus has all of this information, so your primary goal is to identify what you are trying to measure and how you will establish values for these criteria. This will lead you to understanding what characteristics are associated with enrollment behaviors, and how those differ from those that did not.

I start with the basics and measure the behaviors or patterns of behavior that are highly correlated with enrollment. I benchmark all of the initial contacts we have with prospective students to see which provide the greatest return on investment. Perhaps carefully targeted high school visits bring highly qualified students into your pool. And perhaps some of your direct mail/email campaigns are not yielding great results. When I look at that data over a couple of years, some clear patterns emerge and I feel confident moving decisively to reallocate resources. Once staff see the data, it is easier to move them away from “we’ve always done it this way” to “we can let it go.” And these patterns of success are not one-size-fits-all; what works at one institution may be an abysmal failure at another, so approach the analysis with an open mind.

After you evaluate initial contacts, take a look at all of your additional interactions. It could be that college fairs are a terrible first-inquiry source but that they do a great job as a subsequent interaction in yielding students. Identifying these successes and flops gives you confidence going forward. What really matters are activities that advance your goals: enrollment headcount and quality, net revenue and demographic priorities. Allocate what you need to fund ongoing mission-critical activities, eliminate underperforming allocations, and redirect your newly available funds to pursue new initiatives. When you work on future budget requests, your ability to tie potential new investments to demonstrable outcomes is your key to success.

This is just one example of how you may take admission data and turn it into actionable intelligence for strategic planning, budget allocation, and operational efficiency. Start small, get your institutional researcher to help you, look for easy wins, and apply your practical experience to what the numbers appear to tell you. You’ll gain confidence as you proceed, and you’ll find more opportunities to leverage data for success.

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