Getting students to follow through isn’t easy. Whether applying for financial aid, registering for classes, or paying bills, they can seem frustratingly indifferent to requirements and deadlines. And this truism is only exacerbated by the stresses
of the pandemic.
So, how can you encourage students to take steps toward their academic goals?
The University of Texas Permian Basin (UTPB) is utilizing data science and behavioral economics to “nudge” students via three channels with the right message at the right time.
“We’re always talking about data and information, but what do we do with the data we’re talking about?” said PJ Woolston, Vice President of Enrollment Management at UTPB.
“It’s O.K. to look at dashboards, but data has to be actionable,” said Michael Chavez, Executive Director of Strategic Analytics at UTPB. “Higher education institutions are data-rich, but are we utilizing data science to reach
students?”
One message, three platforms
As an example of data-driven interventions, Chavez and Woolston described a three-prong communication campaign that nudges students via multiple channels over a discrete time period (such as one week).
Using data analytics, they determined the best times to reach out to students (in this case, Sundays and Tuesdays), to deploy the following:
First comes a text message, because texts tend to have high open and read rates.
Second follows an email with the same image and language as the text, leading to higher open rates.
Third, a web page widget that appears when the student logs on to the portal.
Data mining
The marketing strategies of these campaigns have been proven to increase enrollment, according to Chavez. They include:
“Abandoned cart recovery” (nudging students to complete actions they have initiated). Similar to an Amazon reminder that “you left something
in your cart,” students receive follow-up messages if they have, for example, added a class but not “checked out,” or if they have an unpaid bill.
“Market basket analysis” (nudging students to take a complimentary action). Similar to Netflix’s suggestions
“you might also like…” or “customers like you also bought…,” students see messages suggesting classes related to those which they’ve taken.
Learn more about these tools in Chavez’s SEMQ article “Data Science Playbook.”
Less labor, more results
“Since COVID, we’ve seen increasing numbers of students with outstanding balances,”said Woolston. “Now we are using the data to develop a new approach to getting students out of that situation and through the funnel in a less labor
intensive and more sustainable way.”
Woolston and Chavez will share examples and practical guidance for data mining in their session at the virtual AACRAO SEM Conference, October 28-30, 2020. Learn more and register now.
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