Revolutionizing Customer Engagement: C1’s AI and Omni-Channel Vision

Curious about how a small Avaya partner transformed into a major player in the contact center space? Join us as we chat with Mark, the CTO of C1, discussing their remarkable journey and how their philosophy of being "agnostic with preference" ensures the best-fit solutions for clients. Mark elaborates on the importance of R&D, managed services, and a comprehensive lifecycle approach in revolutionizing customer experiences, sharing insights into their transition from TDM voice to omni-channel communication.

Discover the groundbreaking impact of generative AI on customer experience, highlighted through their innovative platform, Ellie. Mark reveals how Ellie tackles the challenge of AI-ready data, with tools like Fabric building and managing data lakes to operationalize AI effectively. We dive into the critical role of linguistics and ethical considerations in AI development, and share a compelling story of predicting customer churn using AI to transcribe and analyze call recordings. Don't miss this deep dive into the future of customer experience shaped by technological innovations.

More at https://linktr.ee/EvanKirstel

Transcript

Speaker 1: 0:02

Hey everybody, fascinating chat lined up today with a global powerhouse and customer experience at C1. Mark. How are you? I'm doing very well. How about you? I'm doing great Dog days of summer here, but nice to connect. And for those who may not be familiar with C1, give us an overview, a little bit about the history, your role at C1 and your mission.

Speaker 2: 0:27

Yeah, sounds good. Yeah, c1, formerly known as ConvergeOne, has been around for about 32 years, started really kind of in the 90s as an Avaya partner NACR, if you're a knacker as the term goes and really kind of hit steam 2014, 2015, when we started doing a lot of acquisitions. So we grew from about a 200 million company to 1.8 billion in about eight, nine years. You know, mostly through inorganic growth, but really kind of focused mainly on how do we dominate the contact center space, but also knowing that without infrastructure, security, data center and some of that, we're missing part of the puzzle. So it wasn't that our acquisitions were 100% all around, just contact center and collab and UC, it was really infrastructure and security as well.

Speaker 2: 1:19

I'm the CTO here and a little bit different than most. I think. If you were to say what is ConvergeOne, I think you know most would say they're, you know, sometimes a VAR, but more a solution provider, solution integrator, really getting into. You know managed services, a lot of pro services. 60% of our revenue comes from services. So you know we're very heavy in that and what that really tells me is that you know we're very heavy in that and what that really tells me is that you know, we're not out there just to sell something to a customer. We're really there to bring a solution to a customer and in the contact center space that's radically important because just buying technology doesn't do that much for them. So my team really leads up all the R&D, so everything that we've done around products, services, solutions, things that we would, you know, kind of brand under our intellectual property it really is under me. So a lot of R&D development, you know, software and quality people.

Speaker 1: 2:16

Fantastic and yeah, you don't hear about your typical bars, resellers, msps, having R&D or indeed having a CTO. That sounds like quite a unique value proposition. So tell us about that. What's your unique perspective on this market and how do you kind of differentiate yourself versus all the resellers out there?

Speaker 2: 2:38

Yeah, that's a great question and it's something that we run into and kind of have to walk customers through, because I think if you look at who we're compared against, most of them are fairly reseller. You know they have a very small amount of services at best some, probably not a lot of managed. But you know we heavily have invested in, you know, the life cycle with our customers, mainly because it, you know, in a contact center world as an example, selling software, if it's a one and done, you're not part of the solution, you're not part of the. You know really what the. You know what our customers are trying to get to their customers patients, students.

Speaker 2: 3:15

So really, our focus on how do we deliver, you know the pro, the managed and as well as cloud solutions out there, cloud services, as well as cloud solutions out there cloud services really is key to how, you know, we can really be effective and, you know, be the thought leader with our customers rather than just kind of. You know, I think what a lot of our competitors are doing is listening to their vendor and bringing it forward. We have a little tagline here at C1 that we call agnostic with preference, meaning that when we go in and talk to a customer. Yes, we're very well-versed. We're usually partner number one or two of anybody in the top 50. And because of that, we do have the ability to bring the right solution to our customers, instead of always bringing in the manufacturer hammer and treating everything like the manufacturer now right.

Speaker 1: 4:01

Yes, that's been a relationship that's changing and evolving, and maybe talk about innovation, some of your own solutions that you innovate around and things that you might be developing. How are you looking to impact and truly add value to clients?

Speaker 2: 4:19

Yeah, it's an interesting journey because if you go back, you know, let's say, to the 90s in Contact Center, it was exciting to have a digital phone before voice over IP. Right, it was a lot of technology, but it was, you know, a lot of. You know how do we solve things for customers in a different way, or how do we make the technology do something more. So if you go all the way back to the 90s, it was a voice world, it was TDM voice. What would we be able to do with our customers with voice? That might be interesting, but as that has evolved, we got into multi-channel, and multi-channel was bringing in digital instead of just voice, and we had know, we had email, we had some SMS back when we had to pay for SMS, and, you know, web chat was barely a thing, and so you know what has evolved over the time. Though, which is interesting is omni channel kind of came onto the scene, right, and you know omni channel by you know some definitions is, you know, if I had something in my shopping cart and I went to call a call center agent, the call center agent would know that I have something in my shopping cart which sounds really awesome on the napkin. I could draw that for you a hundred different ways to Sunday, but that's the most massive distance between technologies and it mainly gets to the point that if you know something about me as a consumer, logged in on a website in a retail fashion and have a shopping cart, that technology doesn't exist in the contact center world. So how do you blend them together?

Speaker 2: 5:49

And what most people do is they blend it together at the agent desktop and our philosophy and our thought around that was that's kind of too late and if you're doing that, you're you know you're getting the most expensive part of the contact center involved.

Speaker 2: 6:01

You're you know the routing and everything behind. That just makes it that that's just a foregone conclusion and we said, well, why don't we change that? So we created software about five, six years ago to really solve that called we call it C1 Fabric, and Fabric is really kind of three things it's a data lake, it's an integration, fabric is really where that comes from and it's all the management tools to make all that work. So we can kind of overlay anybody's contact center technology knowing that there's going to be a CRM or an EHR, you know any other form of data store. Right, cause that's where. That's where the customer is right. The only thing that's going to be in the contact center is a phone number, an email address, an SMS number. That's the ingress point of knowing where they came from. But to us it's how do we put all that together so you can have that omni-channel experience?

Speaker 1: 6:56

Fantastic. As a CTO, you're tracking all the key market trends. Is it all about AI and Gen AI, or are there other trends around transforming agent experiences, customer experiences? You must be following quite a lot at the moment generative AI and nothing else.

Speaker 2: 7:23

You know, if you know, anybody's played around with, obviously, all the tools out there. It was an interesting year of education and understanding. You know, both self-educating and then helping educate others on really what it is and what it can do. It's interesting because it's, you know, it was our ability to really kind of join in in that space in a different way, because AI is nothing new to us. We were doing natural language processing, natural language understanding, back in the 90s.

Speaker 2: 7:49

Conversational AI has been around for quite a while. We've been using pretty much every element of that, whether it's Dialogflow as a toolkit or your live person of Amo core. You know all of those as well. But what Gen AI really kind of pivoted us on is now we have a capability of using both Fabric as well as what we have. Our AI is called Ellie. I can talk more about that if we want to get into that. But Ellie really is, you know, a form of AI that is a platform or a service that's really delivered around, that AI that really is both a data platform and an AI platform.

Speaker 2: 8:28

Because the thing we noted in 23 very quickly was, you know and Gartner defined this too in the fall at Symposium, that 4% of CIOs have AI or said they have AI-ready data. So 96% of our customers don't have AI-ready data. So why am I talking to them about AI? It's fun and it's cool and it would be something fancy to talk about, but I can't apply it to them. So that's where we said look, with Fabric we have the capability to build a data lake for you, manage it, have all the integration points. Because, by the way, the second thing that Gartner also found was those that are the most advanced in AI lack integration or lack integration capabilities, or even if they can integrate, they don't know how to operationalize it. Right, it's, you know, to stitch two APIs together, put some software in the middle. I can do that proof concept, you can do a demo lab, but to get it into production, where you're cranking two million calls a month through it with zero downtime and five nines and all that, that's the hard part, right? So, as we look to bring Ellie to market, it is a Gen AI platform supporting conversational AI. Obviously, we've got all the other forms of AI and ML to do different things, but our purpose really, around it you kind of see behind me it's elevating the connected human experience.

Speaker 2: 9:48

That's where Ellie comes from, the elevator and, as we brought Ellie to life last year, the first job we say that Ellie has jobs, not use cases the first job that LA did was our open enrollment, so it was a chat bot and it went in and would answer questions on, you know, what should I sign up for? What does this mean? Did I miss anything? What if my 25-year-old child turns 26 mid-year? What do I do? And you know everything that we could throw at it, which is a perfect litmus test for us to understand. Ok, how does this really work? How do we deal with the major problems of hallucinations, toxicity, wrong answers, all that?

Speaker 2: 10:29

And so what we've done is is, from about a year ago to today, really been working on what we call HX, the human experience experience. And, you know, having Ellie as a platform has allowed us to, you know, really take on any kind of use case with any you know, whether it's a healthcare, with our customers finance, and really bring you know that use case to life in a way that really works. And that's, I think, the big missing piece with generative AI right now is that everybody thought I could just pull it out of the box, I could apply it, I could throw some documentation at it. Now it's going to give me all these great answers because ChatGBT gives you an answer right. Every time you ask a question it gives you an answer. But again, if you're asking a question that you don't know the answer to, do you even know if it's a fact and all that. So a lot of our work has been around that.

Speaker 2: 11:17

But, um, the other things that we've been doing that I think are kind of revolutionary is really again in that human experience piece, thinking about ellie speaking as a human.

Speaker 2: 11:28

So we think of a lot about ellie's dialect or ellie's um discourse markers are called it's, it's how many ums and ahs and you know you put in so we're able to really tune it and we can tune it, you know, per customer, we can tune it per phone number, call, we could turn it, you know, based on kind of anything. So the thing that could be interesting is to really think about and and we, we played around this. This is a weird one. But um, having um, like we can record my voice for 30 seconds and turn ellie into me so I I could be.

Speaker 2: 12:00

So does mark want to speak to Mark, that's a weird one, but that's what we're kind of trying to figure out is is how do we, you know, make it so human to you know? And if you don't know, alan Turing, you should. But the Turing test, you know, are, are we, you know, are we getting to a point that we could actually pass that because we don't know if we're talking to a human or a computer? So that's a lot of what Ellie's kind of goal is and how we bring that forward.

Speaker 1: 12:28

Exciting stuff. Can't wait to see some real use cases as a caller or a customer. So you know you talk to CIOs, ctos and your peers all the time Some of the biggest companies in the world, ctos and your peers all the time some of the biggest companies in the world when it comes to adopting not just your technology, but AI, gen, ai in general. What are some of the biggest challenges and roadblocks that you're seeing and how do you see yourself overcoming those with your customers?

Speaker 2: 12:53

Yeah, trust is the first part, especially with the generative AI, that there's enough documentation out there and enough articles and things that we've seen where you know whether it's lawyers, you know using you know generative AI to make up case law or not. That they know they are they. You know they don't know Right, but or the Air Canada thing giving the wrong answer and now putting them in a pinch to you know, to adhere to what the bot said. Trust is obviously a big thing with generative AI With conversational it wasn't, but generative AI is really the trust piece.

Speaker 2: 13:29

Data is the biggest hurdle by far. I mean you can get past the trust by hey, let's do a pilot, let's get some out there. But the data is really the key and I've rarely found that when I walk into a customer and say, hey, where's your data lake, they're like, oh, it's over here, it's perfect, it's got all the data and it's all ready to go. Most of the time they're like, well, we've been working on it for three to five years, we've got about three to five years to go. So data is the second one. And the last one is really that integration piece Because, interestingly enough, when you're in one application, you use the database in that one application, but then when you go to this one, you're siloing all these data pieces all over the place. So people have a lot of data. They just haven't stitched it all together and put it into a data lake, and that's really what you know from an integration standpoint. We have the capability now to do that and plug it into our lake.

Speaker 1: 14:22

Fantastic approach. Maybe talk a little bit about the culture and leadership side of your team. On the techie side, you have tons of geeks like us on the team. Maybe talk about that culture of innovation and excellence on the tech side that you're helping nurture. What's your idea there?

Speaker 2: 14:41

on the tech side that you're helping nurture. What's your idea there? Yeah, all geeks and nerds we love them all as well. I think it's a difference, if you like, star Trek or Star Wars, where you kind of fit in all that. But yeah, it's interesting that my team is really comprised of, I think, some of the top developers in this industry that have been around for a long, long time 20, 30 years.

Speaker 2: 15:04

A lot of us have come from partners from the Avaya, cisco, genesis world where we were implementing very, very difficult solutions back in the day, having to integrate to a lot of different things. I mean, there isn't a contact center that doesn't have 19 pieces of technology, all you know, sitting in a desktop, right. So making that all work, making it all work a hundred percent of the time, that's, you know that's not easy. But you know, I would say in my team the thing that's interesting is we've opened GICC in Hyderabad, india, and so we're leveraging, you know, bringing folks on there, so growing that substantially. I think we've got to date, over 500 people there. That's mainly for support services, financial and all that. But as my team grows, we're really focused on that as really an innovation hub for us. But yeah, I mean across my team.

Speaker 2: 15:58

We've got an AI center of excellence team that is actually focused. They're not even developers, right. They're more focused on linguistics and how do people talk and how do we take LE and make LE do something that is highly valuable to both the caller and the callee. And in that we also now that generative AI is kind of top of mind, with everything we're having to worry about our governance, our roles and responsibility around generative AI, our security policies, internal as well as external. So that team really focuses a lot on not writing the code per se, but actually making sure that what we're doing from an AI perspective not writing the code per se, but actually making sure that what we're doing from an AI perspective bringing it to us and bringing it to market is is, you know, safe and responsible, ethical. You know the whole nine yards. And then you know, yeah, a lot of developers that have been with us for you know some of them 20, 25 years and so really, industry experts on. You know all the T-SAPIs, apis, tapis, all the things.

Speaker 1: 17:06

We love four and five letter acronyms in our space. Yes, very exciting.

Speaker 2: 17:10

You got to make them up. And the other thing that we have that I think is a little interesting is a real focus on data, but not data in kind of how most think about it. I mean, we've been focused since about 2012 on AI-ready data, which really means that it's not in a standard relational database form. It's not you know how we use it in standard applications and it's fit for purpose that we can either have unstructured unstructured with time series collecting it that way. So that way, when you think about you know machine learning jobs as an example, none of those work without time series because you have to have that.

Speaker 2: 17:58

You know what it looked like at any point on guard, which collects tremendous amounts of data, or our analytics product, which uses the same technology but, you know, might gather call recordings and ACD data and IVR data and bot data put it all together in one common data lake. You know that's. I think that's a massive difference that we have and we've thought about because we've invested in that. You know for 12 years that we have and we've thought about because we've invested in that for 12 years and thinking that way. That's where I see a lot of our customers kind of stuck is that they have a system with a relational database and that relational database is great for that one thing, but it doesn't relate to this database. So you can't just smash them together. You have to have some way to normalize and turn that into a common data lake. So that, to me, is, I think, a very unique thing we have on our team.

Speaker 1: 18:43

Fantastic. You work with pretty much who's who of blue chip tech companies. The S&P 500s of the world Care to call out any customer stories or maybe asking you to pick a favorite child. But, yeah, what are you seeing in terms of impact on the business? And you know, in a world of doing more with less, what are your customers telling you about what their needs are at the moment?

Speaker 2: 19:12

Well, I'll give you my shock and awe story. I always kind of give it's entitled to you people, and that's where everybody's like Whoa, but it was. It was a customer that came to us a few years ago and they were having a problem with just analytics, call analytics. They weren't able to understand, you know what was it really happening in their contact center, and so we said, okay, well, why don't we, why don't we start taking all your recordings, transcribe them, break them down, put them in our format, the way we think about it and really get to a point where I don't know what questions you're going to ask, because I don't think they know. Really. Everybody does quality monitoring. Right, your call is being recorded for quality purposes. A human being listens to it, scores 20 different questions. But we thought, well, why don't we do that differently, since we have this AI, we have automation, we've got all these tools, and so we started working with this customer and after it was probably about a year and a half, they just kept coming with use case after use case after use case, and the one that was really interesting is they said can you tell us who's about to leave us, who's about to churn, based on what they say versus what others say. That stayed.

Speaker 2: 20:23

So we pondered it for a while. We built a couple of models and then we finally got to one that worked really well and we started to try to identify phrases, because we break down every little phrase, so it's not the whole thing of what was said in the conversation. It's hi, my name's Bob. Nice to meet you, bob, my name's Mark, and you know it's all those little iterations Because we had that granular data and all the metadata we put around it. We were able to kind of look at okay, when we pulled the Salesforce data in, we were able to say, okay, these people left. Okay, what did they say? These people stayed. What did they say? Where's the anomalies, where's the things in between these that don't work? That's where the first phrase came out you people. So it was interesting that we were able to identify that and, of course, many other things down the road, but at the end of the day, that was actually noted as a 4% increase in their EBITDA.

Speaker 2: 21:11

So that's what I mean. Isn't that what everybody's after, right? Artificial intelligence. You know the artificial is lacking natural quality. That's not what people want. They want, as we talk about it augmented so we play demos we can give right now that you know the bot will kind of stop and ask you how you're doing and just have a conversation with you and it's random and it's weird, but you know it's very human, right, and you know that's where we think about that.

Speaker 2: 21:52

Actionable is what I just talked about, and augmented is really where a lot of the work that we're doing Ellie is coming out. So, like we just launched Ellie assist a couple of weeks ago and that's our way to to to play with this in a different way to say, look, you don't. It doesn't always have to be a human talking to a bot or to AI, maybe it's just the AI is in the middle and that's really what assist is is. You know a caller might be talking to an agent or knowledge worker and Ellie's in the middle feeding that agent or that. You know the worker side, you know what's going on, summarizing it, giving you know possible answers to things. You know really kind of in a way kind of a co-pilot way to think about it in a contact center world, pilot way to think about it in a in a contact center world.

Speaker 2: 22:39

Right, we're going to be coming out with generative AI analytics in the fall and and some new things and voice around that as well. So it's always kind of this evolution path that we're on, but again we're we're trying to solve pretty macro customer problems versus, you know, coming up with one one thin use case that might work for a few months and move on. Because, yeah, we don't know where generative AI is going to take us in, you know, three to five years, I think we can see maybe a year out. But we're super excited about technology, where it's going and you know.

Speaker 1: 23:09

Fantastic. Well, it's been fantastic getting a preview into the future which seems like it's arrived. So I'm not even going to ask about your vision, because it's kind of here today and just it sounds more like executing on that. What are you up for this summer? I guess you're probably not too many events in August, but I think you have a big analyst event in September. What's on your radar?

Speaker 2: 23:32

Yeah, yeah. So the wedding season's over, so we're done with all the tours, with all the all of our vendors, yeah, so we have an analyst event September 24th coming up, which we'll see you there, and Gartner Symposium is coming up in October. We're definitely going to be at that event. That was a great event for us last year. I think I think we talked to over twelve hundred people and you know, 1,200 to 10,000, that's 12%. Right, that's a good number. There had a good couple of speaking engagements that you know worked well. But, yeah, there'll be a couple more events that come, but that September 24th and, you know, the Gartner Symposium are two major ones coming up. And then, you know, we will keep releasing different versions of, you know, le software fabric. You know some of the things we're doing. So you'll see consistent announcements coming out about that.

Speaker 2: 24:24

You know not, we don't necessarily want to stick to a strict, you know, summer, winter, fall kind of thing, but you know, we know, this fall there's there's a few things that are coming up that we think are going to be pretty exciting. There's, there's a few things that are coming up that, uh, we think are going to be pretty exciting. Um, and Ellie voice. Um, it's going to be one of them. You know being able to get. You know different, uh different, dialects and voices and you know kind of you know getting that human, local. You know flavor to it.

Speaker 1: 24:48

Fantastic. Well, congrats on all the success onwards and upwards and, uh, enjoy the summer. It's a great time up in Minnesota as it is here in Boston, absolutely Time to thrive. So enjoy it. And thanks everyone for watching. Thanks, mark.

Speaker 2: 25:01

Thanks, evan, appreciate it. Take care.