Customerland
Customerland is a podcast about …. Customers. How to get more of them. How to keep them. What makes them tick. We talk to the experts, the technologies and occasionally, actual people – you know, customers – to find out what they’re all about.So if you’re a CX pro, a loyalty marketer, a brand owner, an agency planner … if you’re a CRM & personalization geek, if you’re a customer service / CSAT / NPS nerd – you finally have a home.
Customerland
Key Takeaways from Verint Engage 24
Unlock the future of customer experience with AI as we sit down with Dave Singer, Global Vice President of Go-To-Market Strategy at Verint. Learn how AI is evolving from theoretical concepts to producing tangible business outcomes, without the need for overhauling existing systems. Dave offers a unique perspective on deploying AI to generate immediate value and shares remarkable success stories, including significant cost savings and efficiency gains.
Journey with us through the cutting edge of customer service automation as we explore new communication channels like TikTok and the rising sophistication of customer inquiries. Through the lens of real-world examples like Volaris Airlines, discover how companies are leveraging vast data repositories to enhance both customer-facing and agent-assisting technologies. From basic bots to advanced AI solutions, Jason Valdina emphasizes the critical role of analytics in ensuring effective AI deployment and improved customer service.
Finally, we explore the revolutionary potential of AI in workforce management with Verint's latest innovations, such as the TimeFlex bot. Addressing the pressing issue of post-COVID agent burnout, we reveal how AI tools are optimizing complex scheduling tasks and improving work-life balance. Plus, hear about Verint's risk-free Genie Bot program that promises transformative business outcomes within 30 days. Don't miss our discussion on the strategic partnership between Verint and Five9, showcasing how their collaboration is setting new standards in the market. Join us for an insightful conversation that promises to reshape your understanding of AI's role in customer experience.
Customer Land is a podcast about customers how to get more of them, how to keep them, what makes them tick. We talk to the experts, the technologies and occasionally actual people. You know customers to find out what they're all about. So if you're a CX pro, a loyalty marketer, a brand owner an agency planner if you're a CRM and personalization geek.
Mike Giambattista:if you're a customer service, CSAT, NPS nerd, you finally have a home. I'm Mike Giambattista. Welcome to Customer Land. Dave Singer is Global Vice President Go-To-Market Strategy at Verint, and I was fortunate enough to be at an earlier presentation today where Dave announced a couple of really, really fascinating things that Verint is debuting. So, Dave, first of all, welcome. Thank you for doing this.
Dave Singer:Thanks for having me on Happy to be here. It's always great to come to these events and so many great people to talk to.
Mike Giambattista:So now that the cat's out of the bag, so to speak, and it's legal to talk about this stuff, just give us a high level of what came out this morning. Sure.
Dave Singer:So before I dive into the four things we talked about this morning, I want to set the stage a little bit. We've been talking about CXX automation for at least a year and that's really, we believe, the holy grail in the contact center of the ability to raise capacity and raise customer experience while lowering cost. There's always a balance between those. Sure, finally, you can do it all. And when you do that, it's not about some theoretical thing out there in the future. It's about using this to drive AI business outcomes. Now there's no need to wait anymore about getting these outcomes.
Mike Giambattista:Which, if I may, that's a big change in the narrative and the expectations, because I think it was you this morning who were saying that in so many of the conversations you have. How are you guys deploying AI? Are you using it? Yeah, we're kicking the tires, we're trying to figure it out.
Mike Giambattista:We got some tests for running and you know my big understanding, I think clearly from what you were saying. But also, you know, in the broader marketplace people are now starting to figure out what the business value is here and how quickly you can develop business outcomes absolutely.
Dave Singer:Um, I I talked to you, know a lot of customers and ask every customer so are you, you working with ai? Oh, yeah, yeah, of course, we are, of course, awesome. What are you working on? Oh, we've got some knowledge stuff working on, some wrap-up and summary stuff, great, great. So what kind of outcomes are you seeing, crickets?
Dave Singer:still working on crickets well, you know it's in the lab, yeah and you get into the sort of analysis, paralysis and that last mile of operationalizing is really hard and the approach a lot of um, a lot of vendors and a lot of customers take is well, to get to the AI, I have to replatform everything, I've got to move everything to the cloud and then 18 months later, after I do all this infrastructural work, then I can get to AI. Our approach is again we love you just the way. You are right, you've got infrastructure you love. You've got some stuff on-prem that you want to keep because it's working great, fantastic. Our approach with our open platform is you want to deploy our ai to connect to wherever you are today. So we believe every step should have value. There's no waiting to get there. So it's really about driving value now using the power of ai now, not a theoretical thing in the future after you move a bunch of other stuff.
Mike Giambattista:Which is now doable. It's doable. You know, the technology, as Varen has explained, has been in development here for a better part of a decade. Yeah, but I feel like, completely anecdotally, like so much, more of the conversations in the marketplace are look what I can now do with this thing. That was really a shiny object a year and a half ago, exactly.
Dave Singer:If you look at again I'm just looking at what Verint has been talking about for the last year. You know, a year ago we announced our open platform and the concept of bots, which one of our A's I wish I'd done it. Coined bots stands for business outcome today. Love it Right. We announced all this last year and then for the last chunk of time, all our announcements were like look at the great customer success in buying this. Like hey, it's being adopted by customer ABCD. And over the last four, four months and you saw on stage this morning all our conversations now are about look at the results our customers have achieved. You know, 10 million dollar savings, 16 million dollar saving four million dollars 70 million
Mike Giambattista:dollar savings right and that's in very short periods of time exactly so.
Dave Singer:It's not theoretical, it's about it's a real outcomes now, not experiments to drive outcomes next year.
Mike Giambattista:Yeah, that's a really big deal. So, without giving away any kind of secret sauce or saying things you shouldn't because they're just not quite public yet, I got to ask what are you looking for? What do you think? Not even where do you see the big announcements coming in the next six to 12 months.
Dave Singer:Sure, do you see the big announcements coming in the next six to 12 months? Sure, so Dave Singer's opinion on this. Everybody always asks me what's next? What's the next big breakthrough? So you know. Gen AI if you look back in August of 22, no one had heard of Gen AI outside of data scientists. December 22, that's all the world was talking about. And then the last year and a half has been about driving that benefit and it went from oh, there's OpenAI to oh, then there's BARD and there's Bedrock, and there's Strawberry and there's I lose track of all the names. In fact, it's funny. I just heard someone the other day say oh, that's just old school Gen AI. Like old school, it didn't start until two years ago.
Dave Singer:So I think Gen AI is going to continue to progress along that and that's going to hit the incrementalism phase in a little while. The quantum leaps only last so long. Quantum leaps only last so long. But one thing I talked about in our presentation this morning when we talked about our time flex the ability to bring schedule flexibility agents is Gen AI is not good for every problem. Gen AI is very good at language, not very good at math.
Dave Singer:Right is going to be some major breakthrough at the gen ai level, of breakthrough in the other dimension of ai around analytical capabilities, not just linguistic capabilities sometime in the next six or twelve months. And then we're going to see this explosion of capabilities around a whole class of problems where where gen ai hasn't solved for. So I think gen a, of course, can keep going. The the, the ability to you, for example, shameless plug. We talked about knowledge automation this morning. So the ability for using Gen-AI models to look across every piece of knowledge in an organization so they can give the right answers to call center agents now without rebuilding anything. It's incredible and we're going to see more of that. But there's a whole other class of problems around forecasting and scheduling and market launch analysis, and that's not in the Gen AI space. I think we're going to see some amazing breakthroughs in the next year around that other class of AI.
Mike Giambattista:So this may actually be an off-the-record question, because I don't know if it's answerable under these circumstances. If it's not, you can just look at me like I can't do it. There's so much of what Verint has developed, is developing, that's deployable outside the confines of what we call CX. There's so many capabilities that I just have to wonder is Verint looking beyond its current market strongholds at new places to deploy these amazing, amazing things?
Dave Singer:So I'll answer with what we said this morning, which is insights and automation is needed not just in the contact center but across the enterprise. So we definitely have our eyes on more than just our traditional contact center space. Gotcha Well said.
Mike Giambattista:Sorry, I had to ask the question but I haven't done it.
Dave Singer:Any follow-on question. I'd have to kill you after I answered so we'll leave it at that.
Mike Giambattista:And because this is an audio medium, you don't know that Dave showed up with a Captain America shield, so he's probably capable of killing me for asking those kinds of questions. Jason Valdina is Senior Director of Engagement and Channel Strategy at Verint. He is deep in the go-to-market side of things over there. Jason and I had the pleasure of speaking at last year's Engage event, but there's an awful lot going on this year. That wasn't even kind of a twinkle in the eye of.
Mike Giambattista:CX at that point. But, jason, thanks for joining me. I really appreciate it.
Jason Valdina:Yeah, of course, good to see you again.
Mike Giambattista:So just for context, can you give us just a little bit about what your role is at Verint?
Jason Valdina:Yeah, so as part of the go-to-market team, my particular focus is on kind of two key areas. One is our agent desktops, so the applications that agents are going to be using during their shift, and it's kind of the main surface that a lot of this AI and sort of agent efficiency technology will be surfaced, whether that's knowledge, case management, you know, transcriptions, summaries of interactions, all that stuff is, you know, kind of surfaced in our desktop. We obviously work with other desktops as well and I spent a lot of time kind of mostly, you know, adding new channels like TikTok we're about to launch this month.
Mike Giambattista:Really.
Jason Valdina:Or October. So we'll be supporting TikTok finally, which is great and that's just at customer demand and that always requires partnerships and new APIs to integrate with and all that stuff. So we've been on that journey for years with adding X slash, Twitter and WhatsApp and that whole corner of the world alongside more traditional things like chat or email and voice and the other thing. I work with my colleagues and go to market to think about different ways that we can automate those conversations, whether they're totally self-service or it's been handed over, but ways that AI can drive agent efficiency and help better outcomes for customers. So it's interesting. I feel like with our existing customer base and the prospects we talk to, the challenges are getting more and more exciting and I think there's more and more optimism. The questions that businesses are asking are kind of, I don't know, cooler than they used to be.
Mike Giambattista:Well, maybe not to sound demeaning at all, but they're probably a little bit more intelligent now, because the whole world was, I think, myself included and everybody I spoke with a year ago AI was the quintessential shiny object. This is going to change the world, if I only knew what it was.
Jason Valdina:That's right.
Jason Valdina:When you and I talked last year, that was about 15 months ago, 14 months ago in that context we had just started talking about, we had debuted a series of specialized bots and we've continued to do that over the past, you know, really 18 months, two years. But last year when we saw you, we had announced a whole new batch of specialized bots and we've continued to build that out to I think we're at 60 or 70 now. And you know and you're right, I think customers were asking more basic questions about what they could do and can't do. Hey, am I able to do this with two bots? Just the idea of two plus two could equal five didn't seem like something that anybody wanted to ask. So I think, yeah, the art of the possible, I think, has inspired a lot of our customer base and I think prospects are.
Jason Valdina:This is interesting because I think we're seeing more prospects that have had bad experiences, had made investments a few years ago and it just didn't go right whether that was internal. So one I'm thinking about right now their internal team. They were saying we're doing this in-house, we're going to do it, and there was nothing wrong with that. In fact, our technologies could be used by in-house teams. But they really went rogue and built it in-house and it just took forever and the outcome was lackluster and they were like, okay, we need to step it up and use better tools and we need guidance from a company like you guys. So I think that's exciting to see.
Mike Giambattista:So, before we hit the record button here, we were both talking about your unique corner of the world and what you see and hear out there. And again without naming names because there's no need to, but conceptually I'm thinking in your role as go-to-market here you're probably talking to people about their felt needs in a place that are kind of just it's the new ones Like, I didn't know I needed this, but now I kind of think there might be a solution for something that you know could accelerate whatever it happens to be for them in some big ways. So again, maybe high level, but what are some of the concepts and the problems and the themes that you're hearing out there?
Jason Valdina:The first thing I would say, Mike, is that companies that are working with us, by and large, have started with data. They've started with analytics. So, whether that's speech analytics, text analytics, quality and coaching all that stuff, they've been. Some of these companies have been developing these wells of interaction data customer interaction data for decades. In a few instances, that's the case. So the most exciting thing I think that I'm seeing is that those customers are coming to us and saying, all right, we get the AI thing and some of these bots I think we can really put to use. How do we know where to start? And the best thing is, like you have the most incredible data at your fingertips. Have you teased that data to tell you where to aim it? What do you mean? Well, look at your call data. Look at all the reams of calls that you've recorded over the years, Even newer customers. You've been doing this for two years. Look at that data and what does it tell you?
Jason Valdina:So one of the customers that I'm speaking with today is valeris airlines, who we have a unique story with them because we've worked with them for about seven years. That's not unique, but we we really started working with them for their first chatbot that's really how the relationship started and it was on facebook messenger and that time they have expanded to using our knowledge automation bot to aim that at agents now. So it's not about just the end customer, it's aiming it to their agents. But they're all using their own data. They're looking at the data and they said we found that we can only go so far with the customer facing self-service when it comes to agents. If it comes to agents, we know there's a couple of things that our agents will struggle with. Yeah, we've used that data to coach them and guide them in real time, but we want to do more and they know exactly what those use cases are and it's fascinating and every business has those weird edge cases.
Jason Valdina:This morning we were talking to a utility company and they you know you and I can relate to this like we have outages and sometimes the, the, the information that ai can give to a customer when they call or they chat with us, or something like that. It's only as good as our field technicians have actually put in right. So whatever we know from the field, a tree hit a pole and now the power is that whatever is that's as good as we can do. So we're actually focusing on trying to get the pipeline from the street.
Jason Valdina:Guys in a truck who are fixing something, getting that in a format that we can most quickly use. So what are you doing? We're trying to get that in text form so it could be spoken, it could be uttered through a chatbot of some sort. So that's really interesting to think that in text form so we can, it could be spoken, it could be uttered through, you know, a chat bot of some sort. So that's really interesting to think that companies are really struggling at that level to say how do we get that to be accurate and faster?
Mike Giambattista:well you know. So much of what was announced this morning are completely new ways of thinking about the problems. Yeah yeah, Like I'm thinking about a couple of the bots that you announced, the scheduling bot I forget the name of it. Yep, the TimeFlex bot, yeah, timeflex bot, like you know, to let AI, just to let it loose on all the complex scheduling of your staff, which could number in the thousands, and their unique moment-by-moment needs. Those are tasks that are just literally impossible to manage on a human level.
Jason Valdina:Just impossible and today they're managed through spreadsheets and stuff like that. Right, I mean not to say that there aren't scheduling and forecasting systems. Sure, we have some of those in market. We have competitors for that, but TimeFlex is a game changer. I never thought that I would be excited about scheduling. It doesn't seem like the most exciting thing, but when you think about agents and shift and it's going up, yeah, it's going up.
Mike Giambattista:And if you can deploy something like this, which gives these frontliners the ability to adjust on the fly and improve their work-life balance, and the results that you're already seeing, those are giant, giant things.
Jason Valdina:Yeah, I'm speaking with Robin robin garese from metra, metra g research, today and one of the reasons I was really excited about speaking with her she's done a lot of surveys of executives around agent experience, specifically around retention and churn. She is saying that since the you know the great excuse me the great resignation. Is saying that since the great resignation of COVID. She's saying that attrition rates went down, got more stable, and she said it's on the rise again and the biggest reason now is agent burnout. They're just doing a lot of repetitive tasks. There's more volume than ever and they're just getting burnt out. And also, I think the contact center is trying to be leaner and not have as many people there, and they're not laying people off necessarily, but AI is giving them the ability to not have to scale as much. So if that's not done right, agents are actually taking on more work. It's more stressful.
Jason Valdina:So time flex is interesting also because think about the supervisors. Normally there's kind of a ratio. As you hire more agents, you in turn need more supervisors and while companies aren't getting rid of their supervisors, they don't have to have that ratio all the way up the chain. If they hire four or 500 more agents, that doesn't mean that they absolutely need 10 more supervisors, because, to your point, a lot of the work they are doing besides coaching and getting involved and being an escalation path is just making sure that there's coverage. So if someone's so sick, right, we need somebody for the night shift, whatever that is. Having that be driven by AI just doesn't mean that supervisors go away.
Jason Valdina:It means that they're actually more focused on the quality that's being delivered. They can jump into conversations and coach in real time, and they still have to monitor forecasting and scheduling. But a lot of the grunt work there is automated you know it's that stuff is.
Mike Giambattista:It's a massive, massive change for a lot of these people.
Jason Valdina:It's a great example of applied ai, just to take stuff that I think the best quote of uh is around, like you know taking, taking, applying ai on the things that humans shouldn't have to do. Right, I think there's a lot of things that we really shouldn't be spending our time doing this.
Mike Giambattista:That nobody's really conceived of before. You really have to step away from your problems, from the things that you do, and wonder how could I be deployed, how could my team be deployed in? A different way. I think it might have been. Jamie this morning was talking about at one point one of your bots, and I can't remember Copilot. I don't know what it was, but you really don't need to go get more analysts to do the kind of work that this function can now provide.
Mike Giambattista:Like you know it doesn't just scaling insights doesn't always have to mean getting more analysts.
Jason Valdina:That's right.
Mike Giambattista:And again, I can't remember because I've got 10,000 bots floating through my head right now what?
Jason Valdina:it was called the data insights bot is the main one, and today we showed that working with text analytics Okay. So yeah, exciting stuff.
Mike Giambattista:Yeah, pretty amazing stuff. So what else are you hearing out there? What kinds of things you know for people who are in the space but can't attend here? You know they're going to hear bits and pieces about what Verint is up to right now and how it can be deployed. But you know you're in actual conversations with people about their actual needs. So can you kind of lump these conversations together in some main themes?
Jason Valdina:Yeah, I mean, I guess one theme off the top of my head that I've been thinking a lot about is this sort of infinite loop that I see customers in. Where they're at some point in that loop, and the theme seems to be they're either trying to do what we talked about a few minutes ago, which is look at their engagement data with fresh eyes for insight into where they can invest AI next, what's the next small thing I could do? What's some incremental change I can make and where is my best bet placed? Is that on the agent? Is it in self-service? Is it in some back office process? Where can I apply that stuff so it has meaningful value?
Daniel Ziv:And how do I prove that?
Jason Valdina:So they're looking at data to figure out where to apply it. They're looking for data to prove that that value was realized and they're looking for data to power that use case. So they're like we want to pick Polaris. We want to be able to. This is true. We want to be able to push SMS and WhatsApp notifications to customers when there's a gate change, something that happens to all of us if we fly. They particularly found this was a reason that people will contact what gate.
Jason Valdina:Am I, you know, to having to answer those questions. That means an agent has to go look that up. It takes a lot of time. That's frustrating. That's an agent has to go look that up. It takes a lot of time. That's frustrating. That's a really seemingly simple data point to get but according to the Valeris Airlines guys, depending on the airport and the systems they're using, that data is not easy to get and sometimes you know we have to. They had to do a lot of integrative work to get that data point reliably into a chat bot that can deliver that and push that message out. So I think thinking about data not only as insights, but the where does data?
Daniel Ziv:live. Where's the gate change number? What's the new gate number? You know?
Jason Valdina:that kind of stuff. Um, it's like the, the telco or the utility company I mentioned, like what's this, what's the reason for this outage and how long is it going to last? That's a people problem. Someone has to report that right. So I think these things are the theme. There is data, is there's more focus on data than ever. What can I learn from that data to figure out where I'm going to apply it? Can I prove that the way I'm applying AI is valuable and is the way I applied AI, requiring a data point that is hard to get and how do I get it? And a lot of that stuff's exciting. A lot of the integration work that I think we're starting to see is super cool.
Mike Giambattista:Do you think that's kind of a new way of thinking out there? It seems like it is to me. Like you know, this is a more mature phase of wait. There are tool sets out there that can do some pretty amazing stuff. Yeah, maybe I should be thinking about them.
Jason Valdina:The thing that's the freshest to me is the insight. This won't be new to you. I know you're sort of a broader thinker on CX and you focus on the marketing side as well. Customer experience professionals on the marketing side the growth side of businesses have had this data for a long time, but they've used it for different reasons. They've used it to target ads and to come up with specialized campaigns to segment their audience, and they've used the same data. But there was a real silo around who got that data? Where did it live?
Mike Giambattista:Oh, absolutely Right, it's under lock and key. Yeah, this is marketing data, and those walls are coming down.
Jason Valdina:Yeah, you know, I think when you saw Jamie this morning talking about and Dan talking about AI and data at the core of our platform, that's partially. Why is that? You know, we have certainly I mean personally the most exciting customers that we have are the ones where IT and the contact center are collaborating with marketing and sales.
Mike Giambattista:So that actually happens is what you're saying. I did say that it happens.
Jason Valdina:It's not happening a lot, but to me personally it's very exciting when you see that we had a user group this morning and one of our customers was talking about stuff that they're doing and I was like, are customers doing this stuff? I mean, she was talking complete customer journey. She was talking about having voice artists come in and record clips for their voice IVA bot so that it had a personality, and that was on the marketing side, but they want to use the same voiceovers for their, their customer care side and like just this holistic thinking that to be able to do that in large companies is sometimes really difficult well, even to know that marketing is doing, something you know well, yeah, true, and and the fact that it's now possible, because, think, two years ago that was you you couldn't think in those terms.
Mike Giambattista:Yeah, it wasn't feasible, it just wasn't so.
Jason Valdina:I mean, I mentioned valeris again Months ago. We started planning the agenda for this and we had to pick customers and customers had to accept quite a few months ago to say, yeah, we're on board with sharing our story or presenting, being interviewed, whatever it is, valeris told us about three weeks ago. They sent us slides they're going to use and they're so subtle about it. They said that we have reached a point now where and they're an international airline now they fly, you know, across all the americas. They also go, I think, to london and paris, but they're mostly a latin america, north america company. But they said we're at a point now where the cost of the contact center is offset by the revenue that agents generate, so they've opened up enough capacity for agents.
Mike Giambattista:That's the golden ticket. It's insane. It's the golden airline ticket.
Jason Valdina:They have a couple of steps. So they have invested very heavily. When I say heavily, a lot of iteration. Total amount of money I won't disclose, but it's not a lot, but there's a lot of iteration that went into it. So, over arguably, I mean it really is seven years total. But the past three years they've really doubled down and added a lot more things that their front-facing, customer-facing chatbot can do. So they've reduced, they've gotten their containment rate. It's now at 83%, so 70% of volume is coming to the contact center.
Mike Giambattista:That's insane. Yeah, that really is.
Jason Valdina:But when those conversations come in, they're using our knowledge automation bot so they're getting those answers faster and they're able and the business is being proactive, right. I'm telling you about notifications being sent out. One of the notification types they're sending out is, if a flight is overbooked, they're offering vouchers to those customers so they're more willing to accept it by just yep, yes, Right.
Jason Valdina:That's it. And so they've automated all this stuff and the net result is that agents have bandwidth in the right context to say hey, mike, I can upgrade you for an extra $20. Or do you have two bags? We can give you a 50% discount on your second bag. And they have a whole list of things they can offer and they can add to the reservation. And those tasks, according to them, have completely offset the actual cost of the context. That's wild, it's insane, yeah.
Jason Valdina:I was like why did you wait until three weeks before the event? Tell me that, and it's on one of their slides and I keep making the text bigger and they keep making it smaller.
Mike Giambattista:That's a really big deal. Yeah, love to hear more about it. Daniel Ziv is Vice President of AI and Analytics at Verint and, as you're about to hear, it's a big job with an awful lot going on. But first, daniel, thanks for joining me. I really appreciate it.
Gail Magdowski:Thank you, mike, pleasure to be here.
Mike Giambattista:So this morning we heard about the GenieBot and all that went into it, and for people who don't have the context that I now have because of this morning's presentation, it might just sound like another feature announcement. But this is far from that. This is something totally different and I don't want to spill the beans. I'd rather have you do that. So maybe first tell us about your role at Verint and then we can talk about this Genie bot, because it's pretty phenomenal.
Gail Magdowski:Absolutely yeah, we're all excited. So yeah, I run the AI and analytics go-to-market strategy at Varent. I've been with Varent since 2002, so it's been a while, but who's counting the years? I love my job. It's probably one of the most exciting years in my career at Varent.
Mike Giambattista:I can only imagine.
Gail Magdowski:Yeah. So there's so much going on and you know we're talking a lot about AI and analytics and how to embed it, and I think the key is really generating AI outcomes, which is really the theme of this event. So it's not about which model you're using or what your LLM, or it's really what outcomes you're trying to achieve, how we get you to those outcomes as quickly as possible, with the focus on now. You can get started now, no matter what your infrastructure is. There's a lot of different bots that you can embed. Some are embedded within the VARN platform, some are add-ons. You can add it in the cloud, even that. So I think that's the excitement is, people realize, wow, I can actually get some of this. Everybody's excited about AI, but they haven't really seen the outcomes from all their experiments. We're generating outcomes rather than just experimentation, and I think that's the theme for the GenieBot as well is how we're leveraging what customers are already doing and just supercharging it.
Mike Giambattista:Well, we were just talking, before we hit the record button, about some pretty phenomenal outcomes. We'll leave that until it's an official press release, but pretty big deal. Until it's an official press release, but pretty big deal. One of the big takeaways for me this morning, and even throughout the rest of this event, has been the immediacy with which these technologies can be deployed, the ease with which they can be deployed, but the immediacy with which they deliver results, and that's a shift in the whole AI narrative everywhere, I think. But you guys are actually doing it.
Gail Magdowski:We are and we have the outcomes to prove it. So we deployed this about a year ago when we launched this initiative, and we have actually a risk-free program where we're launching this within 30 days and if customers don't see the impact they're expecting, they don't have to pay. And we haven't had a single customer say we haven't seen the outcomes yet. So I think it's a good bet there and we're seeing the outcomes. We've been exceeding our expectations. We're talking about tens of millions of dollars in outcome. I just came from a session where I was blown away. We're talking about over a billion dollars of impact achieved within a year. So some of these companies are obviously large, but the impact is phenomenal.
Mike Giambattista:So this morning you talked about, and I think this was the official debut announcement for GenieBot and, as I mentioned, it could just be another feature announcement. But this isn't. This is something totally different. Can you explain it?
Gail Magdowski:Yeah, so first let me take it up a notch before we get down to Genie. We announced business analytics, so business analytics is critical. Ai has a lot to do with the insights you're driving and Gen AI can help accelerate that. And that's really our focus. And we realized that organizations different people in organizations need different kind of analytics. There's the executives and decision makers that need answers to quick questions, some visual reports. They want to naturally ask the question, have a conversation with their data, and we have Data Insights Bot that does exactly that. It provides your key KPIs, state of your business, quick answers. It's not a deep dive analysis, but it gives you the answers you need immediately with a very easy interface and a visual interface. Then we've heard a lot.
Gail Magdowski:There was another session I attended from a customer who has this whole data science team and they're extracting variant data, which represents about 70% of their behavioral data. And then there's other sources of data they have from other system and they're unifying all of that. They want to marry the data, mash it up, create their own dashboards, and we're very open to that. It was a great session because, yes, they're leveraging the variant platform, it's generating behavioral data, but they're also sharing it externally and they have a data science team that are using that and we have all the APIs to extract that data and again, outcomes to prove it, and that was also successful.
Gail Magdowski:Genie is in the third bucket, which is the business analyst. We're the market leader in speech analytics. It's deployed in over 80 languages and many, many hundreds, if not thousands, of customers using it globally and it has demonstrated amazing outcomes and it's almost like these outcomes are so great that people want to. They see it almost as a bottleneck. The session I came out of. There's a company that has 17 analysts. Their hour wire per analyst is $500,000 per analyst. Now we're doing two things. First of all, yes, they can hire more analysts because each analyst is generating great hour wire.
Gail Magdowski:But sometimes it's hard to find these analysts or train them, so it's not so easy. So it's still a bottleneck and the opportunity is great. They could do much more if they had a bigger. They started with three analysts. They keep growing that team but they can't grow it fast enough. So what we're doing is we're supercharging those analysts so the insights come much greater. This company does about seven to 10 call studies per analyst per year. We can take that by a factor of 10 at least, so the ROI could be $100 million.
Mike Giambattista:Just because the way that the AI is built, plus the fact that you've already been analyzing these kinds of things for the past decade, yeah.
Gail Magdowski:So what we're doing here is we're not launching that's why you might think, oh, it's just a feature no, we're not launching a whole new product suite. We're taking the suite, the platform, the data, the behavioral data, leveraging all that and embedding Gen AI in the fingertips of the employees. These analysts now these 17 become like 170. And that's like a totally different scale of insights and outcomes. Complete game changer, like 170. And that's like a totally different scale of insights and outcomes. And they don't have to learn new stuff. It's actually easier now to use the tool than it was before. But you still need these analysts. But they're just supercharged and I think there's a few elements of how this is unique. Happy to share more on that.
Gail Magdowski:So people ask me okay, but we already have this initiative. We're going to take their calls, we're going, uh, you know a transcription engine and we're gonna send it off to chat, gpt and those are. Those are cool experiments, I'm excited to see those. But but they don't generate outcomes, for several reasons. Um, first, for ai is only as good as the data you feed it, especially generative ai. Um, so it's uh, we, uh, the first thing, when we talk about, let's say, speech analytics or calls in the call center, you need to transcribe those.
Gail Magdowski:Baron has the most accurate model now for transcribing phone calls. Again, it's not the most transcript for videos or for YouTube clips. There's other engines that are better, but specifically for this domain, because we have that expertise for over two decades and we keep making it better and better, we launched our exact transcription bot, so we're getting the most accurate transcription. The raw data is as accurate as it can be. That's the first step.
Gail Magdowski:The second step, when you're using Gen AI, you have to give it the right subset of data for the question you're answering. So in the example we showed this morning, we talked about customer churn. So speech analytics, as it is today, identified very accurate customer churn calls and then I can interrogate and leverage Gen AI to ask questions about what's driving that churn, how we can prevent that churn. But I need to give the LLM the right data set. We can curate that data set. It's kind of like a rag process but we're doing it very, very accurately. And if you send a random set of calls you're not going to know what subset to send and that gets much more complex. So that's solved within the Varon platform. It's also providing very recent Our transcription works.
Gail Magdowski:We have a real-time version, but even in the offline version, within an hour you have the calls transcribed and ready to be sent, so I can ask questions about something that's happening now or this morning, whereas even a trained LLM will give me data from months ago or weeks ago, when it was last trained, so it won't have the answers to what's affecting the trend from this morning or from this week. So those are key elements, and there's one more element of verification Everybody's still a little worried about Gen AI hallucinations right.
Daniel Ziv:What if?
Gail Magdowski:the insight is not real and that is a real problem when you don't give it the right data. But even if you give, it the right data getting data from the outside right.
Gail Magdowski:So in this case we validate in the workflow. Within speech analytics we can click and dial all the way to the call. It brings me to the spot where somebody said, hey, I wanted to cancel and this was the reason it was a price increase. I can quickly validate that. So an analyst can go through, validate 20 calls and say, yeah, this insight is valid, and be comfortable sharing that with their executives and even export that to share it quickly. So I think those elements make Genie so exciting and why there's so much buzz around it. But it is part of a broader business analytics initiative that covers all the use cases. That's leveraging the unique variant behavioral data.
Mike Giambattista:So it begs probably another dozen questions which we don't have to get into the questions which we don't have to get into, but you know, one of those is that, because of its capabilities, that are, you know, real, genuine BI, which probably has application well outside of CX and EX. One is is that someplace that Verint's trying to go outside of you know, the kind of home realm where you operate so well right now? Would be one question, and I don't know if there's an answer for that yeah, so our focus is definitely CX automation, which EX is part of.
Gail Magdowski:So improving employee experience, improving customer experience, and that is a combination of doing that while reducing operating costs. So you're not just spending money to improve CX, but you're doing it actually becoming more efficient. I don't think we're expanding beyond that use case. But that use case, even though a lot of the data stems from the call center, the impact and the outcomes are enterprise wide. For example, customer churn is not just a call center issue. It's an enterprise issue. Retaining your customers or compliance or changing your pricing policy these go far outside the contact center. That's why it's so critical to get these insights quickly and be able to share them so the whole organization can leverage that.
Gail Magdowski:We do have solutions for the back office and for the branch, so we do go, but our focus is EX and CX and I think it's such a strategic focus and the impact is so great we don't need to expand beyond that. But I agree with you Gen AI does have other use cases and there's great companies out there, including hyperscalers, that will probably focus on those use cases and we focus on what we do best. Our data is specifically focused on CX and CX automation. Our applications and our bots are focused on that. They only do that well. So I don't think we're, at least now, expanding beyond that. But that is you know. If I see again the opportunity of how much impact this can have, we're talking tens and hundreds of millions of dollars, so there's no need for us to go beyond that.
Mike Giambattista:So we only have a few moments left, but you're the person I most wanted to ask this question to, just because of the nature of what you do. So last year, verint presented a handful of bots. I think it was the first kind of portfolio of bots that you announced. This year, it's not so much about quantity as it is about quality exactly what these things are doing, which is a very interesting evolution and maturity in the way this is all being presented and, I think, being received as well. So look out, another 12 months. What are we going to be talking about? What's going to be the most amazing thing on the agenda?
Gail Magdowski:at that point. First of all, I agree with you. First, when we started off, it was more bots, more bots. Let's get as many bots as we can. I think now our focus we have enough bots. We actually don't want to confuse, to have so many and people say which bots should I start with. It's really about the impact the bots generate, the outcomes they generate. So I don't necessarily need so many bots, as long as the bots generate the outcomes and that's what we're focusing on. I think next year we will just show. We started showing that this year. Look at the outcomes we're seeing customers report from these bots and there was 70 million, 10 million, double digit in millions. I think next year we'll, like I said, the example I just came out of.
Gail Magdowski:I was blown away because they're talking about something they did. I can't share too much because it is Sure, but they're talking about an impact of over a billion dollars. That is directly CX impact. This is not about, and it's not necessarily even about, cutting costs, but if we can get to those, to triple digit millions and billions and I think it can get there in some cases Some of these are large organizations, but I think that's going to be. The story is. This is much bigger than you thought, and you know what changes the discussion about timing and price. If I can save a billion dollars, then whether I'm paying you a million or a million and a half or 700K or 2 million, it doesn't matter If.
Daniel Ziv:I can save a billion.
Gail Magdowski:It's kind of like when you're investing in NVIDIA a year ago and somebody's saying well, the transaction fee is 20 bucks and you wouldn't argue well, I want it for 18. The point is let's just do this now because I don't want to miss out. If you wait another three months, that means another $30 million or $300 million. Then you don't want to wait. Then now becomes imperative and getting to the outcomes as quickly as possible becomes imperative. I think that's going to be. The theme is we're realizing these outcomes. So every day I'm not doing this. How much am I losing?
Gail Magdowski:versus let's do an RFP and evaluate 50 different solutions and do an experiment and wait a year. How much money have you lost in that year?
Mike Giambattista:When it's now practical, feasible, when it's practical feasible, proven.
Gail Magdowski:I think that's going to be the shift and we're already hearing this. When we introduced the geniuses, Like you know, they didn't even ask how much. It's just when.
Mike Giambattista:When can I get it?
Gail Magdowski:They stopped asking about the price. They didn't even ask how much. Just when? When can?
Mike Giambattista:I get it. They stopped asking about the price. Yeah, that's a big shift. Well, daniel, I really appreciate this. I know you have been running hard all day today and yesterday, so to get on your schedule is an honor. And again, just to reiterate, daniel Ziv is Vice President of AI and Analytics and is in the absolute thick of it in AI at Verint, and it's a real pleasure. Thanks is in the absolute thick of it in AI at Verint and it's a real pleasure.
Gail Magdowski:Thanks so much. Thank you, Mike. Thanks for coming here. I appreciate it and share the excitement of what's going on. It's exciting.
Mike Giambattista:Gail Magdowski is Director of Workforce Optimization, Quality and Special Services at MSC. I happen to know Gail a little bit from my last year's Verint Engage meeting, where Gail and her team won basically every award that came out because of their successes and their efforts and the work they've done to just build out a really, really complex system. So, Gail, thanks for doing this Really appreciate it.
Brad Ramsay:You're welcome, nice, to see you again.
Mike Giambattista:Thank you. So we don't want to go too deep in the weeds here, because there are a lot of weeds to discuss that you're deep in, but more on a high level. We've heard a lot about how how Verint is building out AI based capabilities and bots, but you know, rather than talking about what you've done and how you got there, maybe just how do you see using technologies like this in the future to build out what you're doing at MSC.
Brad Ramsay:Well, I've got to tell you, we build our foundation at MSC on people, process and technology, and you need all three the combination to be successful. And technology is a big one, because that's where the puck is going. It's what you do today. They've already built out, like you said, the bots. We're just catching up. You know, we have laid the foundation with all our applications because we want to do it right. We want to do it right in the first time around, but with the bots, I like how they have positioned it. You can get faster and stronger outcomes. But you don't have to take big steps and leaps. You can do it very, you know, in small implementations. They have 50 bots now.
Mike Giambattista:Right.
Brad Ramsay:I mean that's overwhelming and that they're not all customer facing. So I think at in the beginning is that well, how we're going to look at it is we might take that low-hanging fruit right, but then look at what can we get the most bang out of the buck for us? But they've come a long way in three years and now they're showcasing it. All their hard work and effort is now here. Now it's kind of more public. Yes, it's public.
Mike Giambattista:Yeah, we've heard a lot about the fact that they've been developing some of these technologies for a better part of a decade, I think, in some cases. So really interesting that since last year when Verint was debuting its first kind of round of custom bots. Now the bots are just kind of they're just another set of tools and we're talking more about the business outcomes. It's less about the shiny object although AI is a very shiny object and more about what you can do with it to build out businesses. You've been in your role I don't know how long, but it sounded based on the fact that you won all the awards last year.
Mike Giambattista:won all the awards last year, you clearly have a really good handle on how your business, which is very complicated, can develop out your customer-facing engagements. So, having seen and heard some of the things that are being debuted here, do any of those kind of spark your imagination of wait a second, we can, or maybe you already are using some of these things?
Brad Ramsay:So we're not, but yes, so my eyebrows yep. It sparked my interest yesterday during the executive summit Because where we want to go, it's just, it's more of a superpower, like for speech. You know the genie having that bot is going to help us act faster and have faster and stronger outcomes, and you don't need extra analysts to help do the work because of that bot.
Tim Richter:Right.
Brad Ramsay:There was also the quality bot with AQM. Aqm hasn't been exactly perfect for our organization. However, I believe that this bot can get us there on our roadmap. So again, you just have to take one step at a time because there is so many options, and then you just got to have your organization, your senior leadership, on board of what the roadmap is. And again I like the fact with the speech and text because again, roadmap is. And again I like the fact with the speech and text because it's again it's another listening post to make us make decisions where we need to make them.
Mike Giambattista:Right.
Brad Ramsay:When it comes to prioritizing, you know what should we attack, you know what is our pain points. You know how can we turn those, turn them around. I guess that's how I want to say it.
Mike Giambattista:I'm imagining that just again, because you've won all the awards, your executive team probably has a fair degree of confidence in your abilities, so when you're laying out the technology roadmap for a lot of companies, they're probably introducing these ideas as brand new, and the executive team has to kind of go well, how do we validate that? In your case, though, it seems like you've been validating these ideas all along.
Brad Ramsay:Well, the nice part is that within MSC it's just not about my team we collaborate with all our teams within MSC. You have to. When you're bringing in new technology, you want to make sure it's the right fit and that you're not in silos, especially with the customer experience right. So I have IT, I have the voice of the customer from the digital side of things, and if you collaborate and you all like we're all trying to understand these bots right, every single team in our line of business has their own ideas, but we bring them all together and we decide what makes perfect sense for MSC, because we want to be lock and step and we no longer want to work in silos based on technology. So that's where that when I was saying people processing technology we want to figure out how we can be the most efficient with the data that we have and then provide all of our stakeholders. You know we want to make decisions based on the lens of all stakeholders for MSC.
Mike Giambattista:Well, it's working.
Jason Valdina:It is.
Mike Giambattista:Apparently, from my standpoint it is. So congratulations again on all the awards. Really great speaking with you. Thank you, Just to reiterate, Gail Magdowski is Director of Workforce Optimization, Quality and Special Services at MSC. Thanks a million.
Brad Ramsay:Thank you.
Mike Giambattista:Brad Ramsey is SVP of US Sales at Connex Telecommunications and Brad and I you're coming in on the tail end of a conversation because we've already been talking for a good 20 minutes about the industry, about what Connex is up to and about some of the great things about coming from Canada. But, brad, thanks for joining me. I appreciate it.
John Bourne:Well, it's great to be here. Thanks for having me.
Mike Giambattista:So, just for context, for people who were not participating in the earlier conversation, tell us what Connex is all about.
John Bourne:Connex. We're a Canadian-based company. We're almost 30 years in existence were almost 30 years in existence. The original focus was around a lot of networking and on-premise what we now refer to as unified communications core telephony systems of IAB and over the years we've evolved into a systems integrator for customer experience, solutions and platforms and ecosystems.
Mike Giambattista:Customer experience, solutions and platforms and ecosystems. One of the interesting things about my work is you find out who the right people are to ask opinions about technology, and it turns out if you're in the know, you ask the systems integrators. Okay, I mean because you guys are tasked with actually connecting the things that are that are sold in the brochures. That's up to you. So, anyway, this will be a fun conversation because apparently you have that font of knowledge.
John Bourne:Well, I'd like to think so. When you're around as long as I have been, you see a lot and I've been in this industry for 38 years and the evolution has been tremendous. And I remember when the first voice over IP sort of hit the market and there was a lot of skepticism about voice over IP and Cisco at the time had to really do a hard sell. What crossed the chasm at that time is mobile phones have become very pervasive. So the concerns around landline technology versus voice over IP was, you know, quality of service. But by the time voice over IP was enterprise grade. The quality of service was an issue because people have become accustomed to mobile phones with, you know, cell coverage and lag and all that sort of stuff. So I've seen a lot and we've evolved to such tremendous capabilities now and you think of all this automation that we're going through and the real-time nature and in-the-moment nature is unbelievable in context of how telecom and interaction modules have sort of evolved over time.
Mike Giambattista:You know you talk about these big inflection points and you know the voice over IP being one of them in this space. But I think we're, you know I think we're in the midst of another big one here, because I remember being here last year at Verint Engage and Verint announced a whole slew of bots that had unique functions to help out in the CX world, and I can remember somebody who sort of pays attention to the space just being amazed. I mean, I think the overwhelming sentiment was just being amazed, wow, that's really cool. And then, but everything, my view, including a lot of people, has been like okay, now let's, let's forget being amazed, now let's get down to what it can do. What are the outcomes that this stuff produces?
Mike Giambattista:And I know that you know selling systems, integration services. You probably have a view to that. So you've heard what Verint is offering. You may even have a view to the stuff that I can't see. Yet it's not available to the public. But think about an inflection point in this industry and what we're going through. What do you see this looking like in a year or two or five?
John Bourne:Inflection point is a is a is a absolutely great word for it, because that's where we are at and what I love about the the variant approach and they have some unique opportunities here, given they're also a 30-year-old company and they've been so embedded in the customer service and customer experience ecosystem not only the confusion of what is available, but also the risk of sort of this open AI environment, that ecosystem that can lead to you know, we think of hallucinations and we think of all the risk that goes around with that what Verint has and they've done it really well and they've actually been in this environment for so many years.
John Bourne:When you think of analytics and speech analytics, they used to be this, you know they talked about actionable intelligence, ironically, which is AI, but we've evolved that now to artificial intelligence and they have a data set that's curated, that's safe, that's secure within a client's environment. So the opportunity to quickly and easily bring AI to life and bring automation and bots to life is somewhat unique, because you're a way that they can attain the same level of security and trust of data that a variant can have right now.
Mike Giambattista:And I don't know how a company that hasn't done that can do that retroactively. You can't just go back and rebuild your LLM from scratch.
John Bourne:You can't.
Mike Giambattista:It doesn't work that way. You can't. You kind of had to start this way.
John Bourne:I think, and even if you start today, there's a lot of work. I think the stats are 80-85% of data is unstructured. So first of all, you have to curate it, you have to structure it, you have to discern how trustworthy it is, and that's a big effort. I'm not sure, I think, customers are expecting that they can find AI they can turn on quickly. There's a foundational work that needs to be done that I'm not sure people appreciate quite yet.
Mike Giambattista:Right right, just a quick note. If it sounds like Brad really understands what Verint is all about, there's a good reason for that. Brad's got a deep history at Verint and recently started leading up business development and broader strategy at Connex. So just a little context there, because you speak with a certain amount of authority. It's a little unusual for an outsider. So you know you're growing Connex's business in the US as a Canadian company Not that that's, I can't. I don't see how that might even be a hurdle but you are also systems integrators for other companies out there, some that are even directly competitive. So you see the feature sets, you see the integratability to these things, the feature sets, you see the integratability to these things and you probably also see how the clients who are buying this technology or these technologies are thinking about what they're going to get out of them. And I'm really curious, when you talk to these people, what are their expectations and anticipations and and do you think they have shifted recently?
John Bourne:absolutely have shifted and the inflection point of ai and the the predominance of the conversation around ai is is is forcing that and and the executive level expectation around what ai, how, how companies are leveraging ai for either cost reduction or customer service. They are obviously trying to figure out how to get it right. There's a couple of interesting impetus for sort of investment at this time. Ai is obviously one of them. There's the migration to the cloud, which is a big, big shift in initiative and it's either end of life of legacy systems that have to be remediated because they're not supported, or it's cost saving, cost reduction and or taking advantage of innovation at a much more accelerated pace. You think of lifecycle management and now in the cloud or cloud-based solutions have sort of product development on a monthly or bi-weekly cycle and so people need that pace of innovation and that's what's driving customers to migrate to the cloud and that's what's customers to adapt AI at an accelerated pace. Those are the two big initiatives that I'm seeing and people are trying to figure out how to do that.
John Bourne:The one thing and yes, I was with Varent for many years the OpenCcast conversation, which started a few years ago. It was somewhat ahead of its time because a lot of these closed systems had a lot of success. But when you're ingesting AI and all of those elements, by necessity it is an open CCaaS environment and customers are. So you ask what customers are looking for. They are quickly coming to the conclusion that they need best-in-breed technology stacks within this customer engagement platforms. You may go to a Microsoft slash Nuance because they have the best natural language and security authentication platform. They may go to Google if they're wanting some sort of FAQ. They'll use a Genesis, for example, for their call routing. They may use Averint for their agent technology and call capture and workforce management type solutions. So these open platforms best of breed is what I'm seeing, and all the executives are trying to figure out how to knit that together.
Mike Giambattista:Right. So when you're in these conversations and they're trying to, they're probably relying on yourself and people like you for guidance on how they can actually, you know, build, rebuild these stacks and migrate from legacy capabilities and hurdles and, as you said, degradations, into the new stuff. So you know how does. This is probably a much bigger question than is answerable in this form, but you know how does that conversation go because you're talking about an enormous amount of variables that have to be addressed. Not just you know call mapping just can be really, but you know down to the APIs and how they're going to work. And you know, is that a job that Connex takes on or do you refer that out, or is it more of a kind of a consultative advisory function you provide there? How?
John Bourne:does that work? We can take on and we do consultatory advisory around it as well. It really starts with a roadmap and I've been pleasantly surprised at how much that has become part of the conversation, and I think it should have happened earlier. It hasn't, but it's been great and I talked to my colleagues in the industry and they're seeing the same sort of thing. Let's have these $10,000, $15,000 engagements come in and help us understand the priorities that we should go in this evolution, because it is a lot to bite off.
Mike Giambattista:It really is.
John Bourne:And it's a function of what the technology lifecycle is on existing. It is sort of what innovation and cost savings you can have on the other end of the equation. So how do you prioritize that in a way that's most effective?
Mike Giambattista:It's certainly an interesting moment in CX and related spaces and I think it's only going to get more interesting. Brad, thanks a million for the time and the conversation, looking forward to being able to do this again sometime.
John Bourne:Thanks, mike, I appreciate it.
Mike Giambattista:Tim Richter, who's Senior Director of Product Marketing at Five9, and John Bourne, svp of Global Channels and Alliances at Verint, for being here after a couple of long days at this show. It's been a fantastic and exciting show. One of the things that came out, though, was this big announcement about the partnership, so if you would, tim, maybe you could just kind of walk us through the announcement.
Daniel Ziv:Sure happy to. So we were really excited to issue the joint press release and announce a deepened partnership between Five9 and Varent, which, of course, has had a seven plus year relationship already. And what we've announced newly is a new native cloud integration between Five9's cloud and Varen's cloud, which allows customers to access the latest innovations within Varen's cloud and then also use the Intelligent CX platform from Five9. And we're particularly thrilled because it gives Five9 an opportunity to not just sell the Varen cloud solutions but also to have a big role in configuring and supporting the solutions as well. As we like to say, we've built a practice around Verint at 5.9, and it's a critical partner for our go-to-market.
Tim Richter:Yeah, I mean I'll say from outside, 5.9 is one of our most strategic partners, certainly CCaaS vendors. You know we've had this relationship from 2016, so it goes back a long way. We started down one path even before Verint had its own cloud. So in many respects, you know 5.9 has been offering Verint in the cloud, in their cloud, but now, because of all the innovation that we have in the platform, now we're a platform vendor. We have a platform-to-platform offering that we think is incredibly competitive in the market. It's very, very competitive in the high end and the enterprise end of the market, which I think the ones that we're seeing a lot of investment in business outcomes now, and I think the bots that we're bringing to the table together now are really going to give Five9 some major differentiation against their competitors.
Mike Giambattista:Well, fantastic Congratulations to you both. It's a big deal. We want to be a part of spreading the word. So again, high fives to everybody. Yes.
Tim Richter:Thank you.
Mike Giambattista:Thank you very much.
