How to Use AI to Analyze Your Data and Get More out of Your Higher Ed CRM

Full Transcript

[00:00:00] Zach Busekrus: What you're painting artist is this beautiful vision of like, Hey, what happens in the future is that a lot of that friction just gets like reduced, right? Because of these, these tools.

[00:00:32] Welcome to the Higher Ed Marketers Guide to Chat, G P t and Generative ai, a special podcast series brought to you by Enroll I and Element 4 51, and hosted by Artis Kadu, founder and c e O of Element 4 51. And yours truly, Zach Buzi Cruz from Enroll I. Over the next four weeks, we're taking a deep dive into the past, the present, and the future of the role that artificial intelligence plays in higher education, marketing, and student.

[00:00:58] In episode one, you'll [00:01:00] get a crash course on what chay p t is and why higher ed marketers and enrollment managers should care about this revolutionary tool. In episode two, you'll join Artis and I for a live brainstorm on how marketers and admissions professionals can use CHAY p t to generate innovative campaign ideas and increase operational ef.

[00:01:19] In episode three, Artis and I are joined by JC Bia, element Board member and the Chief Data Officer at Vayner Media for a conversation on the history of generative AI and how the broader advertising space is using AI to promote their products and services. And finally, in episode four, Artis and I are joined by Elements Chief Technology Officer Petar Gvi for a conversation on how Element is using AI to build one of the industry's most powerful and user-friendly cr.

[00:01:48] On the market. Alright, without further ado, welcome to the Higher Ed Marketers Guide to Chat, j p t and Generative ai. Alright everybody, we [00:02:00] are live artists. Petar, how are you guys doing today? Hello, Zack. Doing great. Doing great, doing great. Artis and I are doing great because it's nice and early where we are.

[00:02:10] Petar is in Indonesia right now, and he's like, it's like 11:00 PM his time right now as we're recording this. So I don't know, like are you drinking coffee the right now, Petar to stay awake or like do you need any stimulant? Uh, 

[00:02:22] Petar Djordjevic: yeah, yeah. But uh, it was like two hours ago, so Okay. Just, you know, just focused and, uh, after this, 

[00:02:30] Zach Busekrus: Going to sleep.

[00:02:31] So you have, yeah, you have your coffee at 9:00 PM while the rest of us have our coffee at 9:00 AM Uh, if, if not sig, you know, way before that . True, 

[00:02:40] Petar Djordjevic: true. Probably not healthy, but uh, it's just a special occasion. . 

[00:02:45] Zach Busekrus: Uh, I like it. Um, well folks, welcome back to, uh, episode four in this four part, uh, podcast series that edify and element have.

[00:02:54] Up, uh, together on, as a quick refresher, if you're just joining us for this episode, we're doing a deep dive [00:03:00] into AI and chat, G p t in particular and how it impacts and, uh, will continue to impact higher education specifically in the context of higher education, marketing and enrollment management. So this is a, a powerful like jam packed series.

[00:03:14] Last episode we had JC Bon. Who's doing some incredible work, um, at Vayner Media and who also sits on the Board of Element. Uh, the episode before that, Artis and I actually do some screen sharing where we go into chat G p t itself and queue up several prompts. And so this is a, a really dynamic series. So if you haven't listened to the first three episodes, scrolling down to the show notes below and we'll have all of.

[00:03:35] Those episodes linked there. But this episode again, kind of wraps our series here. And what we wanted to do and why we wanted to bring Petar into this conversation is we wanna talk a little bit more practically and specifically about how Chad, G b t and its underlying, um, mechanisms will be used in the context of higher education CRMs right in, in, in the tools that you all.

[00:03:57] Every day to attract, [00:04:00] engage, uh, enroll and, um, and retain your students. So I'm ex super excited, PATAR to have to have you on. And again, just super thankful for you making the time to have this conversation with Artis. And I thank 

[00:04:12] Petar Djordjevic: you Zach, thank you for this opportunity, what we are building and uh, how we are thinking about ai.

[00:04:17] And I think there's some, there's some cool stuff in there 

[00:04:20] Zach Busekrus: while. Yeah, I, I couldn't agree more and I thought it would be nice. Uh, and a nice way to just kind of kickstart this conversation, PATAR, is if you could just share a little bit. What are, how is AI already kind of used in the context of most, uh, CRMs?

[00:04:35] Like already, like ai I think sounds, it can sound like this very, like, abstract, you know, the robots taking over the world kind of thing. But the reality is most of us interact with AI every single day already with the tools that we use. So could you just give us like a quick overview of some of the things that we're probably all used to doing in our CRMs that AI is, is, is helping facilitate those interac.

[00:04:59] Petar Djordjevic: Sure, sure. [00:05:00] So one, one good analogy that I heard when I was, uh, researching a a while back is, uh, don't think like ai, uh, if you were like incorporating it, that it'll, uh, instantly change your flow and don't think it'll, like, make you immediately smarter. Hmm. Think that like you just got like a thousand hands or like thousand people working for you and trying to help you to automate certain, certain tasks.

[00:05:20] So, uh, if you think about that, CRMs before used it because there's like multiple generations, right? Yeah. As, as time goes, the innovation is, and especially in the AI space in the last couple of years, it's, it's going really rapidly. Uh, but before, uh, you most, most commonly you will see in AI being used in cases of, for example, fraud detection.

[00:05:43] That was a really common scenario. Uh, Uh, detecting duplicates, right? Especially in the CRM space, right? Because you are, your CRMs are mostly, you're dumping your data from different sources and there might be a lot of duplicates. So that, it was really good that, that detecting that. I mean, humans will be able to do that as well, but you'll be able to encode [00:06:00] that kind of knowledge in it.

[00:06:01] Hmm. Um. . That was, uh, one more use case that's quite interesting as well, was predictive analytics, right? Trying to predict what's next based on certain actions that that users or students or depending on what, what, what's your actor? Um, and most common that everybody knows of, right? Recommendation systems.

[00:06:19] Mm-hmm. Right? You see how that in Netflix, you have that in your HBO O Max or different streaming platforms, so that, that that's what you've, you've most probably seen and used DA daily. Um, Amazon, right. Shopping cart. What, what to buy next based on what you did before. So that it's, it's quite, it's not something new, right?

[00:06:39] It's been here for a long time. Yeah. But now there's a lot of hype because this latest generation of innovation that has happened, uh, makes it really easy to use really powerful machine learning models to power your day-to-day. Uh, and then I think you will see that this, a lot of companies now will start incorporating it.

[00:06:57] Yeah. So, so you can expect, you can. [00:07:00] Tools and products to, to have this incorporated more easily. Yeah. While 

[00:07:04] Zach Busekrus: going forward. On that note, actually, a question that I have is like, like marketing automation, right? Is, is a term that we've all used for, for years, right? And this idea of like if then logic and like we talked about like the decision tree last episode, right?

[00:07:19] Is is that sort of like a, also just a very like practical. Like, it's, it's not a human that's going in and sending that confirmation email, right? There's workflow logic that's built that after somebody comes in the R S P to your open house, they're gonna get a triggered notification saying, Hey, PATAR, you're, you know, you're, we've saved you a seat right?

[00:07:36] To our open house. So, mm-hmm. that is, that is also right, like a, a form of AI at play, correct. Or, or is that technically something else? , 

[00:07:47] Ardis Kadiu: we can get very 

[00:07:47] Zach Busekrus: technical on that. Tire's like shaking his head like, ah, I dunno. those 

[00:07:50] Ardis Kadiu: terms. We . Um, at the end of the day, like we, you know, we can call something machine learning ai, we can call it automation, but, [00:08:00] um, the outcome and the kind of the goal for the outcome is very similar.

[00:08:03] We wanna automate something, we wanna have this helpers do things that we traditionally do manually. And when you think about it is that the machines are really good at detecting pattern. Um, so yes, the automation part can be done in a more simplistic way. However, when you think about AI is its ability to look at massive amount of data and, uh, in the CRM space or specifically in the tools that you're using every day.

[00:08:30] It's looking at all the activity and the actions that you're taking and trying to predict. What are the patterns there? So it can give you, like Pat said, better recommendations or kind of, um, give you a, a different view of a particular email or a particular, um, kind of piece of content depending on your prior history.

[00:08:51] And that's the things that we have been focusing on the past and in element. Traditionally it's been around activity, information or behavioral [00:09:00] ai or behavioral, uh, machine learning or data. Yeah, analytics, like the behavior is super important when you deal with CRMs because, , you know, you're capturing things like addresses and and so on and so forth, but it's the behavior that really makes a difference in how people interact with your brand and how they interact with you as a school or as a program that is going to say, Hey, you're more likely to open my next email, or more likely to actually receive my call, and you're a better fit than somebody who has not interacted.

[00:09:33] So that's the simple notion of kind of what we've been over the past five 

[00:09:37] Zach Busekrus: years. So let me throw out this scenario and, and you all, you both tell me if this is like a, a helpful way to think about the, the distinction between marketing automation as we might know it today, and what. When we're talking about AI and its applications in the context of crm, what that might mean if not today, like in, in the near future.

[00:09:55] So let's say, right, a prospective student, they go and we'll just stick with this event marketing analogy. They [00:10:00] go and they R S P to an open house event, right? Marketing automation as we know it, right? There'll be one trigger communication. It might include one or two, you know, variable tokens, right? In the context of the email, it'll, it'll pull in patter's name and also his like program of interest, right?

[00:10:15] In in the confirmation email, but it's basically going to be the same email that everybody who signs up for this particular event is going to get. Whereas as AI continues to be infused into, into these tools, what we could see is we could see a confirmation email, everyone's signing up for the same event, but the confirmation email that folks get could be wildly different in terms of semantics, in, in terms of tone and style based off of what the machine has learned about how interacts with content versus how PET interacts with content.

[00:10:44] Is that, is that right guys? It is definitely. 

[00:10:48] Petar Djordjevic: Cool. Cool. Definit. 

[00:10:50] Ardis Kadiu: Yeah, that's, that's exactly, and, and one of the things that you can think about that is, uh, you know, a hundred hands Yeah. Helping you . [00:11:00] I, I keep coming back to that analogy. Um, well, think about it this way. If I were to respond. You know, individually to that student or to that email, if I were to write that email, um, I would learn a little bit more about Zach and kind of know it so quick.

[00:11:15] Okay. Zach is in this location, he's interested in this program, and then I would write an email that. Um, it kind of puts all those pieces together to convey that, Hey, this event is gonna be very important and thank you for coming, and we appreciate you traveling this much. Yeah, because you're at this distance.

[00:11:32] So all of those things that make it really personal, well guess what? The machine is gonna be able to generate that at scale rather than. You know, having the more templatized. Yeah. And, 

[00:11:44] Petar Djordjevic: and uh, when you mentioned like, because you know the student and you might know his interests, you can pull extra resources that you have, summarize them and give that extra like to, to help convert.

[00:11:54] Right. Hey, you know, read this, this might be important before you come to an event. Yeah. Uh, and this can be personalized [00:12:00] depending on a student at a really, really large scale. And that's, and that, that's that part, right? Because how can you achieve that? Uh, with just decision trees? Uh, yeah. You'll still get to a certain limited number of.

[00:12:11] Cases that you can support because it does not scale, uh, humanly possible to, to maintain that. Otherwise, you only to hire a thousand 

[00:12:18] Zach Busekrus: hands.

[00:12:22] Hey guys, Zach here. I want to quickly interrupt this conversation to invite you to join me at Element 4 51 s Engage Summit. On June 27th and 28th in Raleigh, North Carolina, when it comes to the student experience, we know that you want to be a trusted guide from recruiting all the way to graduation.

[00:12:40] Well, the Engage Summit brings the best minds in higher ed together to give you the strategy and tools that you need to create a cohesive student experience. From start to finish, explore the latest technologies, increase your skillset, and gain insight into today's students to deliver the most powerful and personalized digital engagement experience every [00:13:00] step of the way.

[00:13:01] This is not your standard ed tech user conference. This is a dynamic, inspirational, and empowering event for all higher ed marketers and admissions professionals. I'll be presenting at this year's event, along with some of your favorite Higher Ed LinkedIn and Twitter follows. You can learn more about this event and register for it at Engage dot element four 50 one.com.

[00:13:21] Oh, and you can get $50 off your registration when you use the discount code in roll five 50. That's in Enroll five 50 at checkout. So go ahead, check. RSVP, engage dot element four 50 one.com. Looking forward to seeing you all there. Do you all think that there's going to be some sort of like, uh, interim step, like I'm thinking, right?

[00:13:43] Some tools do this with like sequences or like, you know, um, uh, automation flows in, in your CRM where. It's the, it's an attempt to be a little bit more personalized. It's basically a template, but then before you hit send, right, it'll say, Hey, Zack, we're gonna send this email to, to patar. [00:14:00] Uh, you know, do you wanna customize anything real quickly before you know, or do you wanna just enroll him in this sequence and, and then, you know, hit go and he'll get this automated, uh, Set of communications, right?

[00:14:08] So basically it's a, it's a templated like three email series, but you have the ability to very quickly throw in one line of personalization if you want, before you enroll your contact in this workflow. Right? So that's pretty standard. A lot of CRMs have, have stuff like this. Do you all think that they'll be this, the step where CRMs will basically like.

[00:14:27] Suggest Hey, we're, you know, um, uh, Zach, uh, here are three versions of an email that we could send to Patar right now, uh, as to, to confirm his, like registration for this event. Like, will there be some more interaction with folks at least initially? Cause I would imagine, like, if, if I have oodles of data on Patar, like that's great and I can, I can pull that from, but if I only have a little bit of data, it's gonna be hard to sort.

[00:14:51] Differentiate in, in a meaningful way, especially if, if Petar has never sent me an email with like a bunch of emojis, right? He might be an emoji kind of guy, but if [00:15:00] there's been limited correspondence, right? I, I don't know that I'm gonna get that and I don't know that there's gonna be enough data for, for, um, these tools to kind of like pull from, so, What, I guess what, what are like the interim steps to getting to a point where someone, you know, 50 people rcp to, uh, an event and they get 50 different email responses.

[00:15:19] Like, what, what, what do we think is gonna happen first? 

[00:15:22] Petar Djordjevic: So thinking about this, uh, one thing that you mentioned, uh, interim steps and I'll, I'll first start, go to the end and then we'll work my back my way. , uh, when you talk about generating the emails and what's a really cool feature that, that will be of possible?

[00:15:38] Uh, and this all again depends on the platform Sure. And how the platform is coded. But, uh, uh, these things will be able to generate entire workflows for you. Uh, you won't need to even have, uh, Human intervention to create these steps. So that's, that's the, like the, that's the future, right? That's like, hey, you, you know the student, you know his context and you get [00:16:00] the workflow specific to that student because it's not, it, it, it's not just generating text.

[00:16:05] It can generate text. They can be interpreted into actions and you can, using those actions, create resources, which are like your workflow steps or emails or, so that's like the, the vision, right? Because in the end you'll just have data and you will. Algorithms that can, you know, do something to convert that student or yeah, optimize for something, right?

[00:16:24] And it can measure success and feed, like, have a feedback loop and get better into, into predicting that. Uh, so, uh, you're talking about intermediate steps. What if. It does not have enough context, right? Yeah, it does not have enough information. Uh, good thing is that these, these models, uh, so there's, there's a caveat.

[00:16:43] These models have a problem, uh, where currently there's something they call hallucination. Hallucination is, and you saw, probably saw that in J G P D, uh, it can generate information that's not accurate. It tries to. Just figure things out from Yeah. Entire internet, right? Internet. And it tries [00:17:00] to generate some text.

[00:17:01] Yeah. But you can say, Hey, don't do that. Because when you are, uh, instructing it to do something, uh, sending that prompt that, that's what they call it, like prompting the, the, the model. You can say it. Hey, don't go outside of this context of this data. And if you don't have, if you don't have enough information, Now don't try to figure things out.

[00:17:18] Just, you know, say, I don't know. And you would be able to get that as a result, as the output of that little black, black box. It can tell you, I don't have enough information, which can then be a signal which you, uh, you can code into and be like, okay, we need to prompt the user, interact, give it more data, decide what to do.

[00:17:35] So there will be scenarios where it has enough data. Yeah. You just leave it, do you do its work or you, you, you intervene? Of course, there needs to be a lot of testing. And so, so you get, you know, you see that, do you trust this process or Right. You can just, you know, blindfold it, let, let, let it, let it run.

[00:17:52] But, uh, yeah, uh, I, I think initially, of course, while training it, you'll intervene, but in the end it'll just work [00:18:00] and, uh, it can probably set up your whole. . I think at least that's, that's the promise. 

[00:18:06] Zach Busekrus: Yeah. Yeah. Go ahead, Artis. And then I, I have a follow up to that. I 

[00:18:09] Ardis Kadiu: just want to say that this is the interim step.

[00:18:11] This is kind of what we're working on right now, right? So we have the large language models that can generate stuff for you, but you have to be really good at telling it exactly what to do. Uh, so you don't get, um, unexpected results. And that's gonna be the, the majority, it's gonna be this fine tuning.

[00:18:28] And, and how you build the prompts, and that's a lot of the work that's happening in products right now. Are around taking that technology and putting the additional layer on top of it that is going to get you the output and the result. And there's a lot of really cool, um, from a technical perspective, really cool and challenging tasks that's happening with, with chaining prompts, the way that prompts are don the feedback loops that are happening.

[00:18:56] But you need to have a system that's able to handle all that stuff and [00:19:00] able. Inject add additional context depending on, uh, user actions. Like chatbots for example, it's like, yes, you can answer a question, but it's gonna give you garbage if you don't, you know, if it doesn't, you know it's gonna generate something for you, but it doesn't mean that it's true.

[00:19:16] So that's where the systems are in place and that's where the PR on the product side, we have to be good at figuring out what the context is, who the user is, how to grab additional context in. So that's the intermediate step right now. Um, that a lot of the work is going towards. It's like 

[00:19:31] Zach Busekrus: in the product itself.

[00:19:32] Yeah. No, that's super, super interesting. And like when you were saying, um, Petter, when you were talking about sort of like the, also sort of like the constraints almost that you could give these tools of like, hey, only look at like this data, right? Um, to, to help make, you know, uh, uh, make, make, to facilitate your answer or whatever.

[00:19:50] What's super interesting about that, um, and I, I hadn't thought about this before, but like where we're, where we're probably also going is like, Let's only look at [00:20:00] prospects around these particular like programs, right? Like let's say you're, you're working with a graduate program, right? And you don't, you don't necessarily want to analyze sort of every graduate prospect, uh, and every graduate per prospective student, because depending on the, the program that they might be interested in, these individuals, these, these personas, right?

[00:20:19] They, they might act very differently, right? But it's like, but if you only want to pull from, Hey, what is all, you know, what, what history do we. Of people that are interested in an MBA program, right? And they're from these particular like, you know, regions of the world. And let's only pull from that historical, uh, context, right, to inform this workflow that we want to create versus let's pull from all the data that we have across every program, uh, you know, within the school of business to to write these workflows and.

[00:20:50] That is super interesting because maybe that's also sort of this, this medium step of, before getting to, we were talking about this, uh, in our, our last episode, we were talking about sort of like a segment of [00:21:00] one, right? Like, and that sort of being like the ultimate goal of, of how to best leverage these tools is how do you deliver the best possible experience down to the individual, right?

[00:21:09] To promote your, your product or, or your service. And that's maybe where we're going, but perhaps also this, this interim step is being able. Do a thorough and very quick analysis, and then serve up recommendations of how best to meet prospects from a very, very specific sort of like niche program as opposed to prospects a little bit more, more generally.

[00:21:31] I don't know. So I don't know if you guys a agree with that, but I, I, I feel like that that is a super interesting insight. Like right now, if, if folks are gonna do any of this analysis, it's, it's a pretty manual process, right? Like it's really hard to kind of dig into and, and ascertain trends from how prospects, uh, interact a, again, at, at a program specific level versus at a school level, let alone just as a, as, as a graduate school in general.

[00:21:56] Our whole 

[00:21:56] Ardis Kadiu: app is basically built to, to do some of that [00:22:00] automation for you. Right? So that's what, that's what the AI that we've been building over the past five years does. It's actually works around the segmentation and, and detecting behavior and, and bubbling that up so you can segment that. The part that we don't have yet is the, well, what do you do?

[00:22:16] Like, what's the generation part? Mm-hmm. , and that's where schools have. Like we see a huge gap right now is that everybody's really busy. On the other side, we have the technology, we are, we're bubbling up all these segments, but the schools don't have the capacity, the techno, the, the, the manpower, the know-how, the marketing to, to get and, and put communications and to reach outs that are personal to these students.

[00:22:41] Yeah. And that's where we are. So that's the exciting part. The exciting part is that now we can kind of close the loop. Yeah. Interesting. We can, we can identify those people and then we can take action to generate content that is, um, done much easier. [00:23:00] So that's, that's pretty exciting. 

[00:23:01] Zach Busekrus: Yeah. No, very. Would you, would you add anything that to, uh, to that patar?

[00:23:05] Petar Djordjevic: Uh, I would like to add, uh, so what I see that's, that's really, really interesting here. Um, and as I just mentioned, that's something that, that we saw over the past few years. people either are too busy as well, but sometimes people are just not marketing savvy. Right? Yeah. They, they, or they're not writers me personally as well.

[00:23:26] Right. If I were to write an email, uh, or a marketing email and need to attract attention and focus and be something funny and interesting, right? I, I can be hard sometimes. You're just, yeah, you just want to get. Started, and then maybe you can play around after that. Yeah. So these tools will, uh, try to save you 90% of time.

[00:23:42] It'll try to get you there. You'll still, you can still interact and put your persona into that. But, uh, how I envision this, it'll be a big time saver, like all over the place. You still need a platform. You'll still need a platform to have something to use, to learn, to use. Yeah. But it should get you 90% of the weight there.

[00:23:59] And [00:24:00] then the rest 10% is for you to, to react. Um, as artist mentioned. , we got, we, we got the data. Uh, we can surface that. Uh, and now is we just need a way to surface reactivity. Yeah. Ability to react up to onto that. Uh, and to that will be easy, but it, it still needs to be a, a person's choice, right? You want to do something autonomous.

[00:24:19] You need to tell them, Hey, you know, do you think this is what we think for, from what we seen? And these are the options that you can, you should do . Yeah. And this is what we expect to see as the results of those options, right? What, what, what are the possible outcomes? Yeah. And then they can drive their business.

[00:24:32] Right? So, so that's 

[00:24:34] Zach Busekrus: promising, right? Promis. Yeah, it's super, it's super interesting because like, like what you were, I think both just getting at a little while ago is like, who cares if you can do all this segmenting and you can do all this versioning and you can, you know, leverage all these incredible insights that, that like a tool like Element offers, who cares if you stuff to do the work of like, Then creating 17 different versions of that email, right?

[00:24:55] It's like you have the insight, you know, like, Hey, school, you know, if just write [00:25:00] in this particular way to this individual. Go, go, come on. Like we, this is, you wanna do personalized communications, this is personalized communications. And they agree a thousand percent with you. But they're like, but artists, I'm, you know, one person or a petter, like I have one and a half staff.

[00:25:14] Like, how could I possibly write s. Versions of an email for an open house, like, no way Jose. But these tools, if they can do 90% of that lift, and if I just have to go in and like review and just very quickly make sure like, you know, hey, this looks great, or let me add, you know, change a little bit of, of missed context here and that takes me an hour for 17, you know, uh, emails.

[00:25:35] You sure as heck better believe. I'm gonna spend that hour doing that because the potential outcome of these. Highly personalized to these. These, these segments is so much greater than any outcome with just one version of an email. Blasted to all of these segments might be. . 

[00:25:51] Petar Djordjevic: And, and you can also think about the following scenario.

[00:25:53] There's also a lot of, uh, turnover happening in higher ed. Yeah, yeah. Uh, there's a, a lot of, a lot of, lot of knowledge that's being [00:26:00] lost. Uh, when, when you new employees come, uh, all the planes go, ah, interesting. But all, all of that knowledge is probably in some documents it's available, you know, in certain knowledge bases on f FAQs, on websites, but people still need to ingest them.

[00:26:13] And these writers who, whoever will go there to write those emails and, you know, put some university specific information in them, they still need to learn. Right. People need to, and every university might be different. Uh, with these tools, uh, they're able to ingest a lot of content and, and depending on a topic, they can semantically find that meaning and generate content on a certain topic, and then you just go in there and adjust the detail or a tone.

[00:26:37] So new p people coming to the job can be productive day one. They can send out emails, day one, even not knowing a lot about university because they can use all that knowledge that's already built in. So that means that. , like losing people, right? If they leave, uh, you won't lose the knowledge. It's still encoded in.

[00:26:56] Because the, the AI will, will keep it, retain it, and we'll still keep it, [00:27:00] giving you the suggestions. 

[00:27:03] Zach Busekrus: Hey, I'll Zack here from Enroll I, if you like this podcast, chances are you'll like other enroll I shows too. Our podcast network is growing by the month and we've got a plethora of marketing admissions and higher ed technology shows that are jam, jam-packed with stories, ideas, and f.

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[00:27:47] That is a really great insight because like I, that, that's so. I, I, I wasn't even thinking about, right? It's like, yeah, like what is all that historical context that is lost? Or, or another example too that just came to mind is imagine, right? Being able to go into your crm, like you go into [00:28:00] Element, right?

[00:28:00] And there's a little chat window or whatever, and you just say like, Hey, what was the, uh, average open rate and clickthrough rate of all the emails that we sent for Last Falls open house, and you just click. , right? And then immediately you get a response of you, the average open rate was 32%, and the average click through rate was, you know, 7% or whatever it is, right?

[00:28:17] And then if you, if you're new to the job, you'd be like, all right, so this is the benchmark, this is the baseline. I'm gonna try to like beat this this time around and like you'll be able to know very, very quickly rather than having to go scroll through and you realize no one followed the naming convention.

[00:28:32] It's impossible to know what this email was tied to. Someone hit, clone, clone, clone, clone 17 times, and it's the same name of the email and like, it's just impossible to, to discern sort of like, Uh, you know, campaign specific data unless people, you know, religiously followed the, the setup processes, which most people don't.

[00:28:50] So that is a fantastic insight pattern and like something that is super, super interesting to consider again, in, in sort of this realm of like savings of time. I'd love to [00:29:00] hear if you guys are willing to share, uh, a little bit about, specifically to, to element, right? You guys are clearly thinking through these things and on the forefront of these conversations, which is again, one of the reasons why we're having this, this four episode series is cuz Artis was like, Hey, We're doing a lot of great stuff here.

[00:29:14] Like we want to, we wanna share this with the greater higher ed community. Right? And I was like, of course. Like, let's, let's do this. Right. And you guys have always, you guys are always seen as sort of a, you know, progressive, innovative kind of player in this space. And so it's, it's very appropriate that you guys are leading this conversation as well.

[00:29:28] I, so I am curious, like what, within the context of elements specifically, are you guys thinking through? What are some things that you're working on? What can you share with us about this, this secret? Black AI box that folks might be able to get excited about. Uh, 

[00:29:44] Ardis Kadiu: we've talked about this in the prior conversation.

[00:29:46] So it's around personalization, right? So we're, we're working on person AI around personalization, uh, efficiency, and also access. So those three things are super important, right? So personalization, we talked about, hey, how can we, [00:30:00] uh, derive subject lines? How can we derive content that is very personal to that?

[00:30:04] Um, some of our email, um, and SMS and kind of campaign tools in element and landing pages, like we already have that foundation and now we're working on, um, kind of generating content on the fly that is personal to that individual that's going, uh, that email is going out to, and also making recommendations around better subject lines better.

[00:30:25] So Petter can kind of talk a little bit more about. , but it's around this recommendation making things easier. Uh, we had introduced like a year, you know, a couple years ago we introduced this whole notion of a campaign in a box or automated campaigns. Yeah. With packs and, and, and that was, yeah, that was the vision, right?

[00:30:41] It's like, hey, you have a template of this thing and then you just inject a couple of things that specific to the school and you'll build something for you. Now imagine that and, and taking that, and now even. Personalizing everything around that particular communication or that pack that is, that, that looks and feels very [00:31:00] personal to the school and doesn't look like a template anymore.

[00:31:03] So our foundational work there is now gonna get better and better by adding the, uh, you know, the, the AI part to it. Uh, the other thing that we are thinking about is that we're thinking about the, the human, the human interaction or the end user interaction with element, because ultimately, um, we feel like that element is, is a tool that is helping schools to connect better with students.

[00:31:27] Right? And it's all about that connection and, and students, um, being able to succeed in, in kind of getting a degree or. , um, you know, uh, getting enrolled to the school or even graduating and, and kind of communicating better with that. In that sense, um, we've built, like, we, we focus element a lot on the, um, the communication part or the experience of that end user with, with the school and the tool.

[00:31:53] Yeah. So when you think about that, like we've built, you know, great tools there, but as we move forward, [00:32:00] You need a unique com, you know, you need, um, something that's a lot easier to interact with. And chatbots are really that, yes, that tool, but now they have the ability to, um, to, to provide context in those, in those communications.

[00:32:15] So that's, uh, something that we're focusing our, our conversations tool. It's a multi-channel, you know, two-way, uh, communication. Um, kind of platform, uh, chat, live chat, two-way, sms, all of that inbox, but now add on the ability to, um, automatically have the, the knowledge base and have the communication and interaction with that student be driven by.

[00:32:42] AI answers, right? So the student has this one pane of glass, which is that chat bot. And now just like you said, they're asking questions. They're doing work in there. They can, um, sign up for events. They can sign up, they can, they can even complete, you know, applications. [00:33:00] They can, um, ask for, for, for grades.

[00:33:03] They can do all of that thing through this one pane of glass. And behind it is all of the complexity that goes. A tool like a crm because we have the context of everyth. So that's the vision, right? The vision is that simplicity should be key to everything that we're doing and we're simplifying the content generation, but then we're also simplifying the, the user interaction and that experience with the students.

[00:33:30] Yeah. So Peter can guide, dive a little bit deeper on some of that because I'm sure you have other questions 

[00:33:35] Zach Busekrus: as well. Diving deep there. Cuz I think what artists just, just painted is like sort of this, this, uh, beautiful image of. What everyone in higher ed dreams of right, is like the, the simplification of a system that is quite bureaucratic and, and honestly quite complicated and, and confusing, right?

[00:33:52] Like as an industry, the processes that most institutions have, uh, in order to inquire, apply, and ultimately [00:34:00] enroll, it's just. It, they're, they're, they're historically, they, they historically have been full of friction. Right? And so, like the, what you're painting artists is this beautiful vision of like, hey, what happens in the future is that a lot of that friction just gets, like reduced, right?

[00:34:11] Because of these, these tools. So, so pet, like, talk to us a little bit more about this vision that artist painted and from a technical perspective, like how is this gonna happen? 

[00:34:20] Petar Djordjevic: Right. So, uh, the main important thing is first you need to have a platform to solve that problem, even without. Right. AI is a, is a tool that you use.

[00:34:29] Uh, it's not, it's not your solution, right? So we got the platform, right? So now we're thinking, okay, what, what are we offering? Right? We have our messenger, we have our, our our, our, our chat that our customers use. Let's, that is our artist mentioned. That's a really powerful, uh, powerful delivery mechanism, right?

[00:34:47] It's something, something that we initially, when we were in designing it, we were thinking like, ah-huh, this is really. This can be a powerhouse of applications. It, it's not just a single chat. We can power different widgets, popups that can show up on the page and [00:35:00] reach the experience for the students. Uh, so that, that was a vision even before ai.

[00:35:04] Now without, with all these things and how they progressed, uh, it's just. Escalated and we, we got, uh, really inspired. So, uh, how we're thinking in is AI will be used to remove the decision trees, right? That, that's the problem, right? Because you cannot encode them. You need a lot of people, a lot of knowledge, and that that's configuration.

[00:35:23] Nobody wants to work with configuration, right? It quickly goes out, goes outta hand. So AI will be used to remove the configuration part. Uh, the, the whole idea is it's, it should be as simple as turn on and it understands your processes and is able. Right. So if you're talking about, uh, for example, a, uh, chat bot, for example.

[00:35:40] Yeah. Uh, you're, uh, you need, 

[00:35:43] Ardis Kadiu: yeah, that is, that is something that we're working on right now. Yeah. What's 

[00:35:46] Petar Djordjevic: the perfect user experience, uh, onboarding, uh, chat bott right now? What, what, what other people right now doing, they need to encode all the different knowledge, uh, write all of that. Right? Then probably set up decision trees, you know, Hey, if we detect this intent that goes to this et c.[00:36:00] 

[00:36:00] but what, what, what, what's better? Imagine if you just point to your F FAQ document or F FAQ page on the website somewhere, and you just, the, the chat bot knows all about that. Yep. As if a person read all of that and knows all that and is able to formulate responses, summarize them, answer in different tones, and even adapt to the student.

[00:36:18] That's what's possible. Now. That's what we are striving now to, to implement actually. Uh, so that, that's. Uh, second is like that, that's again, that, that is a helper to pro to boost productivity, right? You don't need to answer now, commonly, uh, ask questions and yep. You don't need to answer the things that happened off hours, because probably 90% of them will probably be resolved by the bot itself.

[00:36:39] But, uh, so now we think, how can we get, uh, further, like what's further? Uh, can you not interact at all? Right? Can, can you maybe just leave the bot, do everything and do it as, as if you had a person employed, but at a massive. So you would need to, uh, bot would need to understand the intent and be able to react on that intent with actions.

[00:36:58] But again, those actions need, still need to be [00:37:00] built in the system. System need to support it. But, uh, but this the current industry trend, right? The bots are not the bots, sorry. The machine learning models are able to do that. They can translate, uh, certain intent to an. , and then you can hook that up onto your system that can then go further.

[00:37:17] So that, that, what does that mean? That means that a student can go in and ask for their application status, right? Yeah. And they can, they can, they can get that immediately, right? Uh, they can register for an event. They, right. What is able to, uh, capture inputs, understand them, give that, give that to the system, and system can call that and send it somewhere.

[00:37:35] Right. So that's, That's, those are the little nitty gritty details of, of what, 

[00:37:40] Ardis Kadiu: how we are thinking of doing this. Peter makes a good, a good point, but sometimes, like you can pontificate all day long and say, oh, this is gonna be it. But when we talk about like the, the things that he's mentioning, um, the QA stuff, like we're talking like weeks and then like short term, um, medium term, long term.

[00:37:58] When we talk about that, it's like [00:38:00] weeks, a couple of months, and then six months is our long. So, you know, over the next six months, like all the things that we're talking about, they're either, you know, being tested right now in production or in kind of a test kind of phase. We figure out the technology and um, and all those pieces.

[00:38:17] So over the next six months, there's gonna be a, a huge change of all of these tools being now, uh, attached to reduce that, that 

[00:38:28] Zach Busekrus: work there. That's so incredible. And. Uh, one of the things, uh, petter that you said, um, which, which I think is like a really important thing for everyone to remember here, right?

[00:38:37] Is like this idea of being able to, uh, interact as a prospective student with a chat bott. And it's like one interface and you, you can ask it whatever you want to get what you need, right? Which is essentially what Che g p T is, right? Like that is, that is what, why it's exactly in the world by storm is like it's one interface.

[00:38:51] You get whatever you need from that. And the reality of the situation is like, even for the people listening right now that are like, oh, this. Yeah, like the, we'll see. We'll see [00:39:00] about these, these tools and these things, blah, blah, blah. There is an entire generat. Right now of your future students who are being introduced to information in the context of chat, g p t.

[00:39:12] So you like, you, you, this is not like a, okay, maybe this is like a luxury good. This, this is, this is a necessity because your future prospects, the people that you're, you're gonna want to attract your programs. They're not gonna search in, in the traditional, you know, way that. I searched for school, right?

[00:39:29] Or you guys might have searched for school even. Like, I think about the people who are 10, 15, 20 years older than me. You know, they, they all, they all did. They all like understand like, uh, hierarchy and like navigation and like when they go to a website, like they go to the nav and they go to the sub nav and they find the information that they're looking for because that's how they were used to content.

[00:39:46] Being organized. I like never go and, and sort through like an nav bar. I go to the search bar. I always go and try to find the search bar so I can quickly look for what, you know, find what I'm looking for on that website, right? This next generation of prospective students, they're [00:40:00] not even gonna search.

[00:40:01] They're not even gonna go to the search bar. They're gonna expect this interface that they can very quickly and easily. Interact with in the the tone and the style that they prefer. And they're gonna expect a really quality answer. And if they don't get that, they're not gonna have the patience to stick around.

[00:40:17] They're gonna go find something else. And like unfortunately, or fortunately, depending on how you look at it, that is just the reality. 

[00:40:25] Petar Djordjevic: Definitely. 

[00:40:26] Ardis Kadiu: Yeah. Yep. Um, One last thing I wanted to add there. That it's, it's, it's, it's been really difficult in the past is multi-language translations on terms of q and a and.

[00:40:38] So access to information, um, we get asked every single day by our partners, Hey, can we have multiple? Like we, we've, we've done translations and we've done internationalization on our applications and all that. However, writing the content is, is a problem, right? Yeah. So they have the content in English, and now how do you provide [00:41:00] that same answer in, in, uh, you know, Spanish or French or whatever, whatever that language is, Chinese.

[00:41:07] So, um, as these international students are coming in, they're asking those questions now, would it be really nice to answer, to take that same knowledge base and, and then be able to give that answer in the native language that that person is interacting with? So that is, that is a huge opportunity that is going to be ex, you know, that is going to be delivered.

[00:41:31] Um, be, we are getting that for free. Yeah. Basically because of this large language models. And we're basing our AI on, um, our conversational AI on the same model that chat G p t is built on. Which is the, is it Da Vinci three? Yes. Petter. Can you remind me? This is probably getting too technical, but it's in the open ai, uh, it's the open ai, um, uh, model for that.

[00:41:56] The same one that chat g p t uses. Um, [00:42:00] so you get translation for free. So now you can say, gimme this answer in Spanish and you'll actually translate right that same answer to you in Spanish. So you don't need to have multilingual, um, knowledge bases anymore, right? Translate your website because that's all gonna be done by Right.

[00:42:19] And 

[00:42:19] Petar Djordjevic: that's the, the models, yeah, that's the, the, the second component, right? As you mentioned, like there, there, there still needs to be a language, uh, knowledge base, right? That content still needs to be somewhere and can be written in a single language, right? Mm-hmm. , but the bot needs to be able to search it.

[00:42:31] Right? Uh, and that, that's also some, one of the features that, that machine learning models give us is that semantic search. Yeah. People are, people are mostly familiar. Keyword searches. You search something, it needs to match on a certain keyword that's impossible. Right. To, to match different languages.

[00:42:46] Mm-hmm. like you have, like us, but if you think about semantec, what it does, it translates the meaning and is able to encode that meaning and search by meaning. I mean, I, we can go into details how it actually works, but, uh, 

[00:42:57] Ardis Kadiu: but yeah. Yeah. I mean, it's very [00:43:00] generic right now. Right? When you think about chat, g p t, um, you can go in there and say, when, when, when is the school closed?

[00:43:07] Right. It doesn't know that. Because it doesn't have the context, it just has general information, so it's gonna give you an answer that is not even correct. So we gotta tell it. It's like, Hey, if you don't know it, don't give us an answer. But now what we're able to do is we're able to give it that context, but in order to pull that context from hundreds of thousands of, of, of pieces of text, that might be your website.

[00:43:33] Fafsa, you know, like government data, you know, all of these things that are very specific to higher education. They might be proprietary. Now you need to be able to search to give that context because you can only give it so much context, right? You can only allow so much context. So you need to be able to give it the right piece of context.

 

[00:43:51] So semantic search around that context becomes super important, right? And that's kind of what pattern is talking about, is that you need to be able to. Find [00:44:00] that content, um, that is relevant to the question that's being asked, and then inject it to, to the prompt so then you can get a, um, the right information and the bot can, can kind of give you the right information.

[00:44:15] So there's, there's a lot of steps that go into 

[00:44:17] Petar Djordjevic: that and think about all the variations, like the, the same. The question can be in different languages. Yes. The content, the content can be in different languages and also the answer should be in a different language and different. And all of that is now possible to be, to be accomplished quite easily using these machine learning models.

[00:44:33] So that's the, that's the, 

[00:44:35] Zach Busekrus: wow. It is a wow on this note too, right? Like a, as an industry you're like, like higher ed considers itself to be a very accessible industry. Right? Exactly. And accessibility is incredibly important to, to higher education as, as it should be. And also, like higher ed has a very, most higher education institutions have a relatively diverse quote unquote, customer base.

[00:44:56] Right? Like not. Organization or company has [00:45:00] as dramatic of a quote unquote, uh, customer base as higher education institutions do. Right? Most higher education institutions have several people, right, like several meaningful, a meaningful percentage of their students coming from other countries and not just one other country, but countries all around the world.

[00:45:16] And so if for no other reason to get excited and pay attention to what's happening right now in. Schools should care purely for the accessibility, uh, components that AI can offer. Even if they, they're having issues with everything else, fine, ignore all those other things. But what you guys are both just with, uh, what you guys are both hitting at right here is so, so important that it actually makes accessibility.

[00:45:41] Possible in a way that historically hasn't, hasn't been able to be possible unless you could afford to have staff who were well equipped to translate every single communication into every possible language that your prospective students, uh, might affiliate with. 

[00:45:57] Ardis Kadiu: And the student can answer, can ask [00:46:00] questions in their own native language rather than figure out how to ask it in English, and then you can translate it or looking for information.

[00:46:07] So it's, it's, uh, 

[00:46:08] Zach Busekrus: yeah. Wow. I mean, I, I am so excited, guys, for what you all are doing at Element here, and I think that you all kind of being the, the pioneers in this space of figuring out, right, how do we take all these things that we hear about, chat, pt, open ai, you know, the future of, of machine learning.

[00:46:24] These, these, again, buzzworthy terms that everyone's talking. How do we like work these into a, a meaningful context that serves us as higher education marketers and admissions professionals, uh, in, in more concrete ways. And so I'm just very thankful for all the work, all the time and energy and r and d and money that you guys are spending to try to figure all this stuff out.

[00:46:42] It's incredibly important for not just element partners, but uh, the greater, higher, higher education community to be able to look to as, as an example of people kind of doing this work, uh, for folks that do want to learn more or kind of just stay up to date on. , what you guys are building. Uh, what's, what's the best way [00:47:00] for them to, to kind of like, get in touch?

[00:47:03] Ardis Kadiu: Just go to our website, element four 50 one.com and then, um, you know, you can kind of look through our resources. We have a lot of resources there and we are posting, uh, blog posts all the time. Um, we also have a. A conference, uh, in, in, at the end of June called the Engage, uh, summit. Uh, and that's where we're going to, uh, make available a lot of this newer technology that, that we're talking about, obviously before then as well.

[00:47:31] But that will be a really good place to, uh, come in, build community. You don't have to be an Element customer, you know. Um, uh, it, it's here in Raleigh, North Carolina, and, uh, it's, it's a place where you can get a lot of ideas. I know, Zach, you were one of the participants, one of the speakers last year at that conference, and everybody loved your, um, loved your session around, uh, email marketing and kind of what drives students.

[00:47:58] And so that was, that was a [00:48:00] popular one, but it's, it's a, it's a great place to. 

[00:48:03] Zach Busekrus: Yeah, I, I can't recommend, uh, the event enough. I think it, it's really cool to see you guys taking this position in this space of being obviously a software provider, but creating an event that's not just a user conference.

[00:48:14] There's, you know, several others kind of like in the space that create essentially like user conferences, but it's cool to have you guys. Uh, pioneering again, uh, an event that's not just an element user conference, but really sort of a, a broader sort of, uh, educational event for people who are interested in understanding sort of the future of marketing and technology and, and admissions, uh, in higher ed.

[00:48:34] So, can't recommend the, uh, the event enough. And again, we'll have a link in the show notes below. Um, so you can scroll down and just head on over to the event landing page too, if you want more information there. But Petter artists, thank you both so. For your time. Thank you for the, the, again, the innovation you guys are doing in, in this space.

[00:48:50] Super grateful and and thankful to, to element for helping make this whole mini podcast series, uh, possible. And again, if you're just tuning into this episode and you haven't listened to the [00:49:00] previous three episodes, they're, they're fire, they're fire episodes. Let's scroll down to the show notes. Click on episodes one, two, and three and, and enjoy.

[00:49:08] Um, but thank you gentlemen for your. Thank you, Zach. It's a 

[00:49:11] Ardis Kadiu: pleasure. Thank you, Zach. It was a blast.

[00:49:23] Zach Busekrus: Hey y'all, Zach here from Enrollify. I hope you enjoyed this episode of the Enrollify podcast. If you like this episode, do us a huge favor and hit that follow and subscribe button below. Furthermore, if you've got just two minutes to spare, we would greatly appreciate you reading a rating and a review of this show on Apple.

[00:49:39] Our podcast network is growing by the month, and we've got a plethora of marketing admissions and higher ed technology shows that are jam-packed with stories, ideas, and frameworks that are all designed to empower you to become a better higher ed professional. But Enrollify is far more than just a podcast network.

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