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What Does Your Data Have to Say About the Next Admissions Cycle?

james cousins
senior statistical analyst

The higher-ed admissions cycle is long and unpredictable and full of pressure. And as soon as it ends, it starts right up again. While there’s no silver-bullet solution that tells you exactly what the new cycle holds for you, there are many things you can learn from your data—by listening to it.

In other words, what your data says can instill fresh confidence as you roll out the new recruiting strategy.

The reason your data matters is because all the countless decisions you make—what lists to purchase, what communications to send, what fairs and schools to visit—are drivers of outcomes. Your data is the single best indicator of what’s working and what isn’t. And by asking some simple questions and referring to the data in order to answer them, you’re going to be able to do more of what’s working, less of what’s not.

Here’s an example:

Say you want to purchase lists at the prospecting stage. You already know this entails several choices. How many names do you buy? How do you target them? Do you keep or drop your old lists? There’s no sound way to make those decisions without exploring their value—without digging beyond the surface level. That is, for any given list, you should ask: How many applications do you receive? How many admits from those lists ultimately enroll?

Further down the pipeline, if you have thousands of admits, most likely you’re communicating with them. After encouraging them, for instance, to attend admitted student days, can you measure the impact of the event? If you’re calling up each one, is there a “best” place to start that effort?

Data can help you identify which of your candidates are “on the fence” so that you can plan to call them sooner or personalize your outreach. And while we know it’s hard enough to find the time to reach out at all, it’s important to pause and “listen to the data” (via predictive modeling, simple trend analysis, or otherwise) as a way to optimize that time you spend.

Not to suggest that your decisions are based exclusively in response to what your data tells you, but if you are planning to reevaluate decisions, data is always a good reference point.

Nor does it demand a huge effort. Yes, you can build a predictive model and provide student-level likelihood scores, but consider beginning at the other end of the spectrum. Simply pulling the data on student visits to weigh the costs against yields in matriculation can prove decisively whether the expense is justified or not! This and so many other quick and easy questions can lead to simple, actionable answers.

Here at Rapid Insight, we’re all about including data in the conversation, and we know there’s no one-size-fits-all solution. That’s why our platform allows you to stay at the 30,000-foot-level, create repeatable reports to your exact design, and to drill-down—and easily build—predictive models to tailor outreach and forecast critical expectations of your upcoming class.

If you missed our recent webinar on how to leverage your data for the new admissions cycle, check out the recording!