Here's the transcript of our conversation:
RVB: 00:02 Hello everyone. My name is Rik, Rik Van Bruggen from Neo and here I am recording another episode of the Graphistania podcast with someone who's joining me all the way from the Boston area, in Waltham, Massachusetts, where I used to have my stomping grounds as well. This is Brock Tibert joining us. Hi, Brock.
BT: 00:21 Hi, Rik. How are you?
RVB: 00:22 I'm very well. Thanks for joining us, making the time. It's really cool. Hey, Brock, you know how we try to structure these podcasts a little bit, right? So why don't you try and introduce yourself a little bit so that people can know what you do, and how you relate to the wonderful world of graphs?
BT: 00:39 Sure, well first of all, thanks for having me. It's great to be on this podcast. Currently I'm employed in the higher education space here in the United States. I work at Bentley University as the Executive Director of Enrollment Systems and Analytics. I know it's a really long title, but basically my job is-- I'm responsible for the division that works with admissions and financial aid. We work on the recruitment of students, the marketing, making sure that we land our class from the strategicals that we have as a university, but also making sure that we stay on budget. I've worked in higher ed for about 11 years but I'm really excited about how I can apply graphs. I've seen a lot of different applications as I work through that in my job, and how I can apply them to various problems that I have here at my job, as well as in the higher ed space.
RVB: 01:31 Oh wow, okay. So Bentley University, it's a private university I suppose, right?
BT: 01:36 It is. It's a private business-specific school, which working at a school the has a singular focus, or a niche if you will, presents some interesting challenges but I think that's where-- you know, you think about graphs and how people are interested in certain things and their relationships, and it really kind of lends itself to that. So it's kind of an interesting use case.
RVB: 01:57 Yeah, that can be very cool. So, how did you get into the wonderful world of graphs? What's your relationship to Neo4j? How did it all start?
BT: 02:07 Sure, so a long time ago when the Netflix Prize came out and I know it was solved I think using matrix factorization and things like that, I started to really get into recommendations and thinking about how recommendation engines can solve a lot of problems, or at least try to help with some of the problems that I face in my job, whether it's recruiting students or trying to think about marketing content that's relevant. I started to think through how you could shape recommendation engines in that way. And I actually came across the blog post not too long after that contest, where someone worked through how to do that with a Yelp data set and Neo4j. And just basically from that post on, I've been hooked. Neo's accessible. You've talked a lot about on this podcast and on various other blogs that graph way of thinking, and it just totally helped my learning curve really relate to graphs and how things are related and how you can leverage that to help solve problems.
RVB: 03:08 That's really cool and it introduces my second topic a little bit. Why is it so useful for your daily business challenges? How does it help?
BT: 03:21 Sure. Right now, I think higher ed and the marketing and trying to land your class and enrollments and all that, it's getting really complex. A lot of schools are now employing things that have gone on in other marketing, in other industries for years. We're kind of, in a way, higher ed's kind of catching up. In doing so, we're generating lots and lots of data. As we start to really start to leverage CRMs, something that higher education is only starting to really do, we're generating like I said lots of information and we can learn a lot about our prospective students or even our current students for that matter. Whether it's things that they're interested in, if you use email engagement as a proxy for interest, or things that they might view on the website, things that they tell you via surveys, visiting campus, things you learn from them then, you can start to really get out and pull out that interest graph, right? Those things that this student is interested in, this major. This student has clicked on content related to financial aid or various groups on campus. You can start to really tease out that interest, and start to say, "Okay, well how can I separate my school Bentley from another school-- well you want to provide relevant content right? So that's kind of what I'm working on a little bit. I'm trying to leverage Neo4j and that interest graph to say, "What can we do to really market differently and stand out from the crowd?"
RVB: 04:47 Wow, that's super cool. It's really almost going towards those sophisticated recommendation engines that people use in retail and stuff like that, right? It's similar to that.
BT: 04:58 Yeah, that's the idea. I might not be recommending a movie but I might be thinking about, "Okay well in our content, what should we be putting in a newsletter? Should we be putting things about study abroad because the student would be more likely to want to travel overseas during their time here? Or maybe sports? They're more interested in athletics. The idea is you want to provide relevant content. And to me graphs and recommendation engines are a really easy way and accessible way to do that.
RVB: 05:25 Are you using any of the standard recommendation techniques there like collaborative filtering or any of those types of things?
BT: 05:36 So right now what I'm trying to do is, I finally had about a good year, year and a half of that data collection, where I can actually start to think through how I would solve this for our problem moving forward. What I'm thinking about doing is looking at similar click behaviours. So, we record a lot of information like someone visits our campus or they requested information on the web, but by leveraging our CRM system and all that data we have in terms of email engagement and what are they engaging and clicking on, I want to start to look at, "Well, find similar groups of students. What are they interested in?" And then start to think about the marketing content for future perspective pools and recommend, to your point, are you interested in study abroad? And do it through collaborative filtering but also graph clustering and things like that.
RVB: 06:26 Well, that all sounds very much like the Enrollment Nerdery that you promote on your blog, right?
BT: 06:32 Yes, I'm totally obsessed with higher ed.
RVB: 06:35 It's really cool and on your blog you also had some great articles. I only read a couple of them on prototyping and linking back to R and those types of things, really cool stuff.
BT: 06:48 Thank you. Nicole's package has been great. The RNeo4J package. I mean it's totally phenomenal and it makes accessing Neo4J via R, something that is my language of choice, really really easy.
RVB: 07:01 Super. Cool, so maybe one more question, Brock? Where do you think it's going? What does the future hold for you and your use of Neo4J at Bentley, but also in general, and the industry. How do you think about that?
BT: 07:17 Yeah I think I kind of talked a little bit earlier, moving forward I think graphs have a natural place in this higher ed recruitment space, this enrollment management. I think it will make things easier for us as we start to collect all this information about students and families, this student is related to this person, or this student is an alumni from this school, or this student is interested in that school, and also majors in interest like I said and I think graphs really will help us institutions and higher ed in general with a lot of the problems we solve whether on the recruitment side of things, what students are interested in our school, or even further down that enrollment path, once they're actually enrolled at a school, what courses should they be taking? What courses will they maybe have trouble with so you can intervene to make sure that you retain the students which is a huge problem right now in higher ed. So I think there's a lot of natural use cases for graphs in higher ed in general, and I'm excited to see how that plays out, and I'm just trying to work through a lot of the problems that I have and try to promote that as much as I can to help people that are in my shoes at other institutions to think that way.
RVB: 08:28 Super nice. It's really cool that you that do that. I really appreciate it. Brock, we try to keep these podcasts nice and snappy and digestible, so I think I'll wrap up here, but I wanted to thank you again for coming on the line and doing this episode with me. I wish you tons of luck and then success with all your wonderful experiments and business apps.
BT: 08:52 Well thanks Rik, I really appreciate it.
RVB: 08:54 Thanks man, bye bye.
BT: 08:55 Bye Bye.
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