Embedding Analytics into your Enrollment Growth Strategy

ABOUT THIS EPISODE

Andrew Hannah, Adjunct Professor of Entrepreneurship and Analytics at University of Pittsburgh, and Co-Founder and President of Othot joins the podcast to talk about the demographic cliff coming for higher education in 2025, and how big data modeling might be the pathway for institutions to help circumvent it.

You're listening to enrollment growth university from Helix Education, the best professional development podcast for higher education leaders looking to grow enrollment at their college or university. Whether you're looking for fresh enrollment growth techniques and strategies or tools and resources, you've come to the right place. Let's get into the show. Welcome back to enrollment growth university, a proud member of the connect e D U podcast network. I'm Eric Olsen with Helix Education and we're here today with Andrew Hannah, adjunct professor of Entrepreneurship and analytics at University of Pittsburgh and Co founder and President of Oh thought. Andy. Welcome to the show. Thanks, Eric. Wonderful to be here. Thanks so much for inviting me. Wonderful to have you here and really excited to talk with you today about embedding analytics into your enrollment growth strategy. But before we dig into that, can you give the listeners a little back on both the University of Pittsburgh and Oh thoughts and your roles there? Sure. Well, I primarily focus on teaching business analytics at the undergraduate and the graduate level, and we are focused on curriculum that enables our students to get to be more analytics when they graduate and find jobs that match that skill set. And at Oh thought, we use predictive and prescriptive analytics to help our institutions and role more students, students that fit their mission and ones that are going to be successful in the programs of that institution. And can't wait to dive into the how in this conversation. But to kick us off, Andy, can you just quickly remind us of the common thinking, the common p O v behind the upcoming demographic cliff? What do people think, assume, is coming for US in and why? Yeah, you know, it's a great question because as I'm out there talking to various enrollment...

...professionals and cabinet members at universities, a lot of the comments that came back is, well, you know, we've seen this before and it's true. We've seen birth thirst from the nineteen seventies and nineteen eighties that were predicted to show declines and enrollment eighteen years later and we actually even saw some growth during those time periods. But that was because we had an increasing number of high school graduates, we were accepting more students into college and we also had a significantly increasing number of women going into college than ever before. So all those factors counteracted the prior birth thirds. But we're sort of out of those normal recovery modes and what we're now into is that that recession that we saw in two thousand and eight is going to cause a ten fifteen percent decline in two thousand later on top of that covid so not only are we having less students in role now, but we have less births. So we don't see any recovery that's coming from this. So that the cliff is real and it's going to hit almost every institution in some way. So if the demographics aren't going to change our approach, our game has to. So let's dig into some ways to help avoid the inevitable for our particular institutions, all states not being equal here in demographically. Talk about what institutions in states like Connecticut, who were already ranked forty eight out of fifty in terms of population growth, are doing to help counteract this cliff? Yeah, and I might offer first that the cliff is actually worse for states like Connecticut where there happens to be a community of very high brand name institutions as well, institutions with resources who aren't...

...going to. We're going to be able to combat that decline with resources to to pull more students in. So not only will will smaller schools and regional schools feel that that decline, but they'll also lose because those bigger schools are going to pull more students towards them. So it's a it's a complicating factor that makes it very difficult, which means that we have to we have to change our strategy. We have to think about making sure we have the right programs that are going to attract students up tomorrow, but we also have to focus on what additional students might be coming to our institutions. So think about that as more adult learners, as more individuals who maybe have some college degree and have stopped out and or dropped out. We want to bring them back. So we have to the population, we have to think about differently, but, most importantly, we have to use the technologies that are available to us in order to understand the individual and how in their behaviors and whether or not they're good fits for institutions. And if we can find those individuals and we can learn about them through data and analytics, we can increase the probability that we can pull them into our institution? Yeah, let's dig into the details on it. What it looks like to find new students, new right fit students, through this big data modeling approach? How does this data modeling of our existing student body actually help us find more likely prospects and not only identified them, but how do we engage with them? Yeah, great questions. So, you know, people use the terminology look alike a lot. So I want to find students who look like the students that currently have.

And it is true the students that attend are your institution, our institutions. They tend to have be of a certain type of students, you know, whether it's academic level or interest in maybe ACC sports or you know, there are personas that tend to be attracted to certain universities. However, that concept of marketing at that segment level is dying, and the reason is that the student of today they want personalized information, they want to be they want to have an experience that is unique to them, and you can see this in the way that they're interacting with media, the way that they listen to their music through spotify, the way they shop, the way they read news. It's all personalized. So if we want to interact with that student, we have to have the tools necessary to understand or behavior who they are as individuals. And so what these models can do is say not only what student looks like, one of our students look like, so that we can find more, but who's behaving like, who's hitting the website in certain ways, you know who's interacting through visits or through conversation or however might be, and use those models then to go test the the population of potential and roll leads and see who is behaving like those who come to our university. Yeah, and can you help us walk through the specifics here? So assumedly we have more information. The more our students get into the funnel, the longer they persist and retain with our institution, the more details we have about their academic record and you know their demographic profile. How do you utilize that information to find more students, you know, across Kinnectic it that you think with not only...

...you know, be good fits for your institution, but ones that you have a high likelihood to be a good match for? Yeah, sure, one of the things that we have to first think about is the population that we are attracting. So if we are thinking about adult learners as an example, you know there are techniques that we can use by tapping into consumer databases, as an example, and we can survey that population to understand their propensity for additional education or the you know, their thoughts in terms of modality or the types of education that they're looking for. So what we can do is we can put these models on top of the survey, on top of all that consumer data, and we can effectively match that up through these algorithms, to the types of students that enroll at our universities. So we can tap into techniques that have been used by political campaigns, by commercial entities like met US on facebook, and we can connect into that broader population of adult students into those who fit into our institution. It's a little bit trickier when we're dealing with undergraduates because there's different laws associated with the use of data and what we would do in that case is that you would work with a typical supplier of prospects and, using the same types of analytic models, be able to match who's in that that broader population or to actually make selections based off of that, those algorithms and find the right people to market too, so that we can, you know, we can use our resources in the best way to get the best field. And, let's say we have that matchback audience of of students that we really want to make sure here about us. We really want to make sure here our value proposition, because at all data glands it...

...looks like they might be receptive based on the data that comes back to us. Do we have address information? Do we have I P information? Are Are we relegated to pretty much direct mail only to reach these folks? Are we able to advertise digitally the to these folks? What are our channel options to reaching this net new audience? Yeah, they're they're limitless. So the you know, it's depending on how sophisticated the institution is. Each segment of the population, whether their undergraduates or their adult learners, they receive information in different ways. Some, you know, email is slowly becoming very much less favorable. The you know, direct mail is actually very strategic when we're marketing to parents of undergraduates, which we can also build models for, or we can use social media platforms like live ramp as an example, where we take data, define the individual, where they live on social media and then we can start to market to them on whether it's linked in or it's Tiktok, or it's facebook or wherever it is. It is virtually limitless. The key is, how do we learn about how the individual receives information and then target that individual in the appropriate way? That's another sophisticated algorithm that we you know, those are the types of algorithms that we develop. That takes that guesswork out of the way that we can help you find the right channel for the right groups of individuals. Yeah, you mentioned these different groups in different students, student types, all of which have, we have, different likelihoods and propensities to be able to find date information based on their age. As we think about this reality of okay, there's less eighteen year olds than ever before starting in based on that two thousand and eight recession, remind us how else to think about okay? That grew is shrinking. But other what other groups...

...should should we be targeting? What other groups should we be focused on making sure that we have a good story and on ramp four, how should we be thinking about how to replace these different student types? I mean there's the typical ways that we often think about. Is there? Are there more? Is there a bigger population related to international students? Or perhaps, you know, we can have a further reach with online programs, so we can boost up our enrollment that way. There's no doubt lots of opportunities like that. But you know, there there's still a very large portion of individuals who want additional education. That may not be a traditional degree education. It might be up skilling, it might be taking specific courses to help change career paths without having to get a degree, or it might be individuals who had to stop for one reason or another, and that we want to argut them all. All these are rich areas for us to look at as potential as potential students. And the one the one thing, the one area I wanted to point out to people that I've been looking at recently is over the past two years, we know the undergraduate population or new first time students has dropped by, you know, depending on whose testamential looking at, let's just say ten PC. We were expected to grow about three percent in terms of first time student enrollment over that time period before covid existed. So there are a lot of individuals who never went to college right so they went into the workforce or they did something else. So there's a group of individuals who, let's call them late starters, that we really need to focus on and target as potential students for our for our programs, and it's really, really great stuff. Finally, leave us with some next steps of advice for institutions. Listening, hearing...

...some of the strategies that you're working on and going boy, I don't think we're doing much or any of these. They want to better incorporate analytical modeling into their enrollment growth strategies. How should they think about that challenge? Yeah, I'll go back to life that are a little bit earlier. First off is that we can't avoid what's going to happen in terms of first time students, especially that the client is here and it's going to continue, and unless we have a very strong brand name with lots of resources, we all are going to be competing for a smaller group of students, and the only way to to get better at that is to improve our precision, meaning let's use data, analytics and insights as the language for decision making, and that means using techniques like Amazon uses in terms of their business model, which is predicting, being the likelihood that somebody is going to buy a product, and then offering a prescription or something that's going to increase the probability that they buy. We need to use the same techniques at the university level, understanding the likelihood that an individual will enroll in our institution and what what types of assets do we have, our prescriptions that will maximize the probability that they will roll in our institution. So I would say that's number one, is using predictive and prescriptive analytics and number two is using AI models to find those individuals out in the world that are going to be attracted to our institution. So it's identification of those individuals to get them into our funnel through these models and then using those predictions and prescriptions to increase the probability of enrollment. Andy, thank you so much for your time and your great thoughts today. What's the best place for listeners to reach out if they have any follow up questions? Eric, first let me say appreciate so much being involved with the with the podcast and...

...yes, absolutely happy to answer any questions directly that you can always reach out to me at Linkedin. If you type in Andy Hannah and University of Pittsburgh Liaison, you're gonna find me. And if you want to direct email address, that would be a Hannah. That's a H A and then a h at liaison e Du Dot Com. Awesome, Andy, thanks so much for joining us today. Eric, it's my pleasure. Thank you. Attracting today's new post traditional learners means adopting new enrollment strategies. Helix educations data driven, enterprise wide approach to enrollment growth is uniquely helping colleges and universities thrive in this new education landscape, and Helix has just published the second edition of their enrollment growth playbook with brand new content on how institutions can solve today's most pressing enrollment growth challenges. Downloaded today for free at Helix Education Dot Com. Slash playbook. You've been listening to enrollment growth university from Helix Education. To ensure that you never miss an episode, subscribe to the show on Itunes or your favorite podcast player. Thank you so much for listening. Until next time,.

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