In 2002, Oakland Athletics baseball team general manager Billy Beane wanted to improve the team’s standing, but with limited financial resources he had little hope of attracting big name players.
He dug into the data and found players who had great stats, but who, for a variety of reasons, weren’t very appealing to other scouts. But after recruiting his choice team, he was met with resistance from his coaching and scouting staff, and the team was forced to play the traditional way rather than focusing on their strengths. Finally, after some struggle, the team re-aligned itself to play as the data directed. Soon, the team was on the longest winning streak in American League history.
I’m sure you’ve all seen (or read) Moneyball, so this story is probably not news to you. But what it shows is that data are only worth collecting if you know how to use it.
Higher education is rapidly changing.
The typical student is no longer 18 years old and fresh out of high school. Rather, non-traditional student enrollment has increased to 75 percent of the total student population, and is projected to climb faster than ‘traditional’ 18-22 year old student enrollment.
Non-traditional students often have little in common with their 18 to 22 year old counterparts. Non-traditional students may work while attending school, study part-time or online, or support a family.
What makes these students even more unique is that fact that they are accustomed to making major purchases and behave more like consumers than students ever have in the past.
This experience with major purchases has a significant impact on the way they choose their institution. They research online, they ask questions, and they expect highly personalized service. There is no doubt this presents challenges. Though they are often considered a single demographic, non-traditional students actually represent a very diverse group. How can a university provide the level of personalization and service required when each student wants something different?
Here’s where the data come in.
Data are valuable when it comes to attracting and retaining students, safeguarding institutional security, and much more. While some outlets published news stories earlier in the year suggesting big data offered false promises, it is a bit premature to abandon a data collecting strategy. Let me explain.
And in higher ed, one of the best ways to use data are as a means of getting to know your students. As nice as it would be, you can’t have staff-wide meet and greets with every prospective and current student. But if you gather the right data, you can get pretty close.
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In today’s digital age it’s feasible to gather information on students’ interests, preferences and purchase intent as well as academic, financial and communication history. As a result, support staff can provide more valuable service and marketing staff can provide more meaningful communications strategies. In fact, by using student profiles that are complete and accurate, institutions stand to increase revenue by 66 percent.
There are fewer traditional students than ever before. This presents a big opportunity—especially for extension schools, online divisions, and other units that serve non-traditional students. But it also presents a hurdle. Non-traditional students are a hard group to make generalizations about, which can make it difficult to serve them as a single entity.
Yet by asking the right questions and collecting the right information you can get to know your students, not just as a group, but as individuals.
Shaul Kuper is president and CEO of Destiny Solutions, a company that provides business software for non-traditional divisions of leading higher education institutions, including Penn State World Campus, Stanford Center for Professional Development and eCornell.
Agreed, Shaul. Students expect us to leverage data to meet their needs.
Nationally, Noel-Levitz survey data says that their #1 need to better access to required courses at registration. This is a huge problem that students continually highlight but few institutions currently prioritize. +
Our experience is that decision-support data can guide scheduling so that existing faculty resources can meet more of the students’ required course needs.