In this episode
Jay Lee, Chief Marketing and Growth Officer at Five9, argues that the contact center is where a brand’s promises get tested and where the richest first-party data a company owns already sits. Six months into his fourth CMO role, with marketing, revenue strategy, and operations reporting to him, Lee explains why putting those teams under one leader shortens the cycle time to learning what customers actually think. He and Greg Kihlström cover why a bot, an agent, and an app giving three different answers reads as the company rather than a service defect, why AI will become the manifestation of a company’s brand, what marketers run into when they finally get service data, how to cut through “AI in the contact center” to a real use case, why average handle time rewards the wrong behavior, and what Five9 calls Humantic: AI and humans working the same interaction without the customer repeating themselves.
Key takeaways
- The only true competitive advantage is the ability to learn faster than the next company, and putting marketing, sales ops, rev ops, and partner ops under one leader shortens the loop between what goes out to market and what comes back.
- Customers get different answers across channels because each channel runs its own technology: the website chatbot is trained on playbooks, the service bot on the knowledge base, Agent Assist on something else again.
- AI will become the manifestation of a company’s brand, because every rule about how the machine is allowed to respond to a customer gets encapsulated in it, and what customers say about how that AI treated them is what defines the brand.
- Most marketers are not actually getting contact center data, and those who are may be getting a curated point of view rather than every interaction classified and reported.
- Start an AI project by asking why you are looking at it in the first place, then lead with data: map the patterns of what customers ask for, pick one well-understood use case, and expand from there.
- Marketing spends heavily to acquire customers and then the company spends to avoid talking to them, because the contact center is treated as an expense rather than the place the relationship grows.
- A verified resolution rate, paired with how long the customer stayed afterward, is the metric the industry is missing; a short handle time that ends in a six-week churn looks good on the old dashboard and bad on the real one.
- Automation leaves humans with only the complex, emotionally loaded cases, and holding agents to the same metrics as when easy password resets gave them a break is a mistake.
- A voice AI front door that understands the request semantically, routes it, and hands the context to the right human is the premium experience; Five9 calls the combination Humantic.
- Personal agents acting on customers’ behalf will be the contact center’s next dynamic, and Lee expects it to be the topic within three to six months, not a year.
Chapters
- 0:00 — Why the brand gets tested at the point of contact
- 1:35 — Jay Lee’s path: engineer, consultant, product marketer, fourth-time CMO
- 2:56 — Why marketing, revenue strategy, and operations report to one leader
- 5:01 — Three channels, three answers: AI as the manifestation of brand
- 7:58 — Service data: are marketers really getting it, and is it accurate?
- 10:08 — “AI in the contact center” means four different things; start with why
- 12:58 — Replacing handle time with verified resolution and retention
- 16:14 — Feeding service signal to product and engineering
- 19:09 — Humans get the hard cases; Humantic as the premium experience
- 22:45 — One year out: personal agents contacting brands for you
- 23:54 — How Jay stays agile
Why one leader over marketing, revenue strategy, and operations
Lee’s reason for the combined remit is cycle time. Marketing sets the external message and value proposition; sales ops, rev ops, marketing ops, and partner ops execute it with internal stakeholders. When all of those teams report into the same place, the feedback on whether pricing is working or enablement is any good comes back faster. He describes the CMO role itself as technical, analytical, and increasingly operational, and traces that to his own path from engineer to management consultant to product marketing before his first CMO job.
Why customers get three answers, and why that is the brand
Each channel a customer touches has historically been built on its own technology. The website chatbot is trained on playbooks and site content, the service bot on the knowledge base, and Agent Assist on a third corpus. Lee sees convergence coming: a single AI that holds every policy, rule, and behavior a company wants, the way JARVIS runs both the tower and the suit in Iron Man. His conclusion is that AI becomes the manifestation of the brand, because every rule about how the company lets the machine respond to a customer is encapsulated in it, and customers will describe the company by how that AI treated them. That makes understanding those response rules a CMO responsibility.
What goes wrong when marketing finally gets service data
Lee’s first answer is that most marketers are not getting it. For those who are, the question is accuracy: if whoever owns the data is sampling, picking and choosing, or summarizing, marketing receives the point of view that owner wants to project. The fix he describes is classifying every interaction so no conversation escapes being understood, then democratizing the output so marketing, product, and other teams can each look at it from their own angle.
How to get past “let’s do AI” in the contact center
Lee says the first question is why the team is looking at AI at all: a board mandate, a finance cost mandate, or more likely a compelling event where the contact center is running into situations it can no longer handle. His advice is not to stay on the sidelines, since contact center operators are risk-averse and tend to move slowly. Lead with data: understand everything coming into the contact center, map the patterns in what customers ask for, pick one well-understood use case that will work now, and expand from there.
Why handle time rewards the wrong thing
Marketing spends heavily to turn prospects into customers, and the minute they convert, the company spends to avoid talking to them. Lee traces that to the contact center being viewed as an expense, and expects customer interactions to explode, including agent-to-agent ones. The metrics he wants to see replace shorter handle time and higher containment are a verified resolution rate (did you solve what the customer wanted) and a longitudinal view of how long the customer stayed afterward. A fast call that leaves the customer unhappy and gone in six weeks looks good on handle time and bad on resolution and customer value.
Humantic: why the premium experience is AI and humans on the same interaction
Easy, routine work is already handled by AI, which leaves humans with the cases that need judgment and empathy, and makes their days harder without the mental break of simple tasks. Lee’s reframe is a voice AI agent fronting the contact center that understands the request semantically, sends simple cases down the fast path, and routes complex ones straight to the expert who handles that situation best, with the context from the AI conversation transferred so the customer never repeats themselves. Five9 calls this Humantic: human and agentic working together. Greg’s parallel is the hotel that recognizes a returning guest; being known is the premium experience regardless of whether a human or a machine does the recognizing.
FAQ
Why does Jay Lee say AI will become the manifestation of a company’s brand? Because every rule about how a company is willing to let the machine respond to a customer gets encapsulated in its AI, and customers will define the brand by how that AI treated them.
Why do customers get different answers from a chatbot, an agent, and an app? Each channel has historically run on its own technology and training data: the website chatbot on playbooks, the service bot on the knowledge base, and Agent Assist on something else again.
What metric does Jay Lee think should replace average handle time? A verified resolution rate, measuring whether the customer’s problem was actually solved, paired with how long the customer stayed with the company after the interaction.
How should a team start with AI in the contact center? Ask why you are looking at AI in the first place, then lead with data: map the patterns in what customers ask for, pick one well-understood use case, and expand from there.
What is Humantic? Five9’s term for human and agentic AI working together: a voice AI agent understands the request, routes simple cases to the fast path and complex ones to the right expert, and hands over the context so the customer does not repeat themselves.
What does Jay Lee expect to dominate the conversation a year from now? Personal agents that contact brands on a customer’s behalf, which he expects to become a major contact center dynamic within three to six months.
About Jay Lee
Jay H. Lee is the Chief Marketing and Growth Officer at Five9. A growth-focused leader in enterprise software and fintech, Jay brings more than 20 years of experience driving go-to-market transformation, scaling global marketing organizations, and delivering measurable business outcomes.
Prior to joining Five9, Jay served as Chief Marketing Officer at Icertis, where he led the company’s global marketing organization and helped position the business for its next phase of growth. Previously, Jay was the Chief Marketing Officer at Avalara. There, he was instrumental in more than doubling the company’s revenue from approximately $380 million to over $800 million by building and scaling a high-performing global marketing team. This achievement was a key contribution to Avalara’s $8.4 billion acquisition by Vista Equity Partners.
Earlier in his career, Jay held senior leadership roles at PayPal, American Express, and GE Capital, where he led initiatives across marketing, product, and business strategy. At PayPal, he led marketing for the Business Financing Solutions division, helping expand global lending and drive significant growth in originations.
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Transcript
[00:00:00] Greg Kihlström: Hi, I’m Greg Kihlström, host of The Agile Brand, and here’s a question for you. What if the truest expression of your brand isn’t in your campaigns at all, but in what happens the moment a customer asks for something your policy didn’t anticipate? Brands that stay agile are the ones that can adapt without fragmenting, and the place that gets tested hardest is the point of contact, where a promise either survives the handoff or it doesn’t. Today, we’re talking about what happens when the contact center stops being the place a brand goes to clean up problems and starts being the place it learns, decides, and grows. To help me discuss this topic, I’d like to welcome Jay Lee, Chief Marketing and Growth Officer at Five9. Jay, welcome to the show.
[00:01:22] Jay Lee: Greg, great to be here.
[00:01:24] Greg Kihlström: Looking forward to diving in. Definitely lots to cover here, and a timely topic. But before we do, why don’t you give a little background on yourself and your role at Five9?
[00:01:35] Jay Lee: I joined the company six months ago as the Chief Marketing and Growth Officer. It is my fourth CMO role now. I actually love the CMO role, so we could talk a little bit about that, as it’s changed so dramatically over the last 10 to 20 years. And I’m a little bit of your atypical CMO. I started off my career as an engineer, went to being a management consultant, did a couple of turns through product marketing and other things, and then ended up in the CMO role. And I will say I love it a lot because it’s technical, analytical, and more and more I think these days you’re gonna find it’s very operational. We can talk about that.
[00:02:10] Greg Kihlström: Definitely. And definitely lots changing, and even more recently with AI adoption and all that stuff too. So I think we’ll cover a few things here today. I wanna start though with something that you’ve referred to. You’ve said CMOs should view customer experience as a growth engine, not just a post-sale function. Your remit at Five9 puts marketing, revenue strategy, and operations under one leader. So when those three report to the same person, what becomes possible that wasn’t before, and maybe what are some challenges as well?
[00:02:56] Jay Lee: I’d like to believe that putting all these things together is the new fad, perhaps, in organizational design. But I’ll tell you the reason I think that we’re doing it here at Five9 is because of the cycle time to getting an understanding of what’s happening with the customer. If you think about it, marketing is what puts out the external message on a company, your value proposition against all the various audiences, and then you have sales ops, rev ops, marketing ops, partner ops, all of these operational teams execute that with their internal stakeholders to get that out to the market, right? And then having all of those teams report into the same area, getting the feedback that comes back from how it’s doing with their stakeholders, allows a company to learn a lot faster, to be honest with you. So you hear, is that pricing working? Was that enablement any good? And so forth. So I think that that’s why all these things are coming together. I have this saying that I like, Greg, which is, the only true competitive advantage that a company has is the ability to learn faster than the next. I can’t remember who said that. Somebody said it, and when they said it I said, “Gosh, that’s right,” especially these days. So the more quickly you can understand what’s happening with your customers and get it back into the business, I think the better. That’s why we have all these together here at Five9.
[00:04:07] Greg Kihlström: Well, and then I guess on the customer end of that too, so there’s convergence within the org of these roles. There’s also, customers are using the platform and the interface in front of them. So they might ask a bot a question, they might interface with an agent, they might interface with an app, and sometimes they get three maybe not completely different answers, but three versions, or sometimes completely different answers altogether. That’s usually treated as a service defect, but the customer gets confused, they get frustrated by a company where there’s lack of coherence there. How should a marketing leader think about owning that part of the process? ‘Cause in a sense, that’s the brand speaking, right?
[00:05:01] Jay Lee: So you couldn’t be more right about that. I’ll say this, I think that the reason that happens today is because, as you looked at any one of those teams, they’re using their own technology. The chatbot that you have on the front end of a website historically has been built around playbooks and material that’s on the website. Very different from what the service bot is using with the knowledge base, and that could even be different from what Agent Assist has been trained upon to help your contact center agents. So every single one of them, depending upon where the customer’s trying to connect with you, you’re probably gonna get a different answer. And I’ll tell you, the thing that I see slowly happening is with all of the technology and AI that you have out there today, you’re starting to see the convergence in all of this, right? A single AI that has an understanding of your entire company, because you can feed all of these policies, rules, behaviors that you wanna have all into one machine. If you think about it, I think of it as, you know how Iron Man has JARVIS? JARVIS runs the tower. JARVIS runs Iron Man’s suit. JARVIS knows everything. I think that’s gonna eventually end up happening, so you won’t get a different answer across different channels. I think that that’s slowly coming, and I think the role of marketing in there, it could be the chief marketing officer that helps the company pull these things together. It could be some other, but I think what you said is really true is this: I think AI, over time, is going to become the manifestation of a company’s brand. If you think about it, all of the rules around how a company is willing to let the machine respond to the customer is gonna be encapsulated in an AI, right? And so having a real understanding of all the situations around how AI is going to respond, I do think is a big role that the chief marketing officer has to understand because at the end of the day, what a customer says about you is what really is gonna define your brand, right? And they’re gonna say how that AI treated you, [laughs] you know what I mean? So that’s something that people really need to think about.
[00:07:01] Greg Kihlström: Well, I think that’s a great segue to the next thing I wanted to talk about, which is just the data that… what customers are saying. So the contact centers, for instance, have a ton of data, and I think marketers have wanted access to that. There’s often silos internally of getting access to the right data, the most useful data. I often use the use case of you don’t wanna market to a frustrated customer that just had a challenging customer service call, or things like that. So marketers have wanted this data for a while, but it’s getting easier to get access to that data. But when they finally get it, what challenges do they run into? It’s like the dog that catches the car or whatever. They get it, but what’s still a challenge even once they get it?
[00:07:58] Jay Lee: Well, at first thought I would actually say they’re probably not getting it, right? That’s just the reality. It’s so difficult to get this kind of stuff. But what I would say is, for those that are starting to get it, I think the question is, are you really getting an accurate picture, right? Because, not to say anything about all the internals that happen in a company, but the people who have that information, if it’s not really going through all of it, where you’re using every single interaction and creating a report, and instead you’re picking and choosing or you’re doing a sample set, you’re getting biased and whatever, it’s entirely possible that whoever owns that particular area is feeding back to the organization sort of what they wanna project. I hope you get what I’m sort of saying there. But I think what ends up happening, with AI especially, when you think about what Five9 can do for a customer base, is you’re gonna know everything, right? There’s no interaction that’s going to escape being able to be classified and understood and then turned into a real report. And I think the democratization of that information, so that marketing can get in there, product can get there, and so forth, everyone will want to be able to look at it from their own perspective and then figure out how they can improve their own different business. So I think that really it’s not quite gotten there yet, so probably the only thing that’s going wrong is you’re being given a point of view that may not be the one that you’re after, but I think that that’s gonna be changing quickly.
[00:09:25] Greg Kihlström: Well, and parallel to that, just the more general data conversation, is the AI conversation, which to me always leads to what do you mean when you say AI, right? AI in the contact center can be a lot of different things. It can be an if-this-then-that rule. It can be a model that scores churn risk. It can be agentic. So how do you recommend that teams have the right conversations about… Let’s not just do AI, right? How do we get to the heart of what they really both want but also need?
[00:10:08] Jay Lee: I think that the real question is why are they looking at it in the first place, right? And we’ve all heard these stories. It’s something that was a mandate from the board, you gotta have an AI strategy, or it could be a mandate from finance, you gotta reduce your costs. But likely, more likely than anything else, I think it’s, they’ve had longstanding problems in their contact center. And there’s some kind of compelling event, I’ll call it that. In a different world, I’d use an expletive there, and I’d say it’s the oh my moment, right? But I think there’s something that compels them to say, “I am running now into situations which I am not able to handle anymore.” And I think that the world is getting kind of crazy out there, if you think about it. Lots of different dynamics are happening, and the cost and the complexity of what’s happening in a contact center is just going up. So anyway, I think that one is figure out really what you’re trying to solve for, and I think the biggest advice I have for people is not to stay on the sidelines. I think by and large, contact center operators are relatively risk-averse. I mean, it’s high-stakes stuff. You’re talking about your relationship with a customer and how you’re treating them. So they go in relatively slowly. Probably the best way to go about it, Greg, would be leading with data, right? I’m a very analytical CMO. I would go get an understanding of all the different things that are coming into the contact center, figure out what all the patterns are of what customers are asking for, pick a use case that you can start with that is very well understood, and then expand from there. There’s very real, tangible things that will work great now, and I would start with that. Unless there’s some kind of mandate that says you have to fix something or [laughs] some disaster happened and you have to figure out how to accommodate that the next time.
[00:12:08] Greg Kihlström: And speaking of measurement just in general, certainly there’s metrics like average handle time and things which reward, well, let’s face it, it rewards getting people off the phone or chat or whatever the case may be. But as service becomes more of a growth function, should we be thinking about this differently? I mean, sometimes the customer just wants to get in, get out, and be on with their day, but getting them off the phone, you may be missing an opportunity to either build a relationship or upsell, cross-sell, all those kinds of things. So how do you think about those numbers that have been kinda long held and used within the org?
[00:12:58] Jay Lee: I’ll tell you, this is a funny thing, right? As a marketing person, when you take a look at a company, marketing spends a huge amount of money to try and engage with prospects and turn them into customers, right? And the minute they become a customer, then you spend all this money to try and not talk to customers, you know what I mean? And try and minimize the expense. And I think that’s because the contact center is viewed as an expense. But if you think about it, and I believe that customer interactions are going to explode. They could be, and we should talk about this, they could be agent-to-agent interactions. But regardless, I think that the more that you’re interfacing with a customer and doing the right thing by that customer, the better off you are in the long run, okay? So when you think about all the metrics, you’re right, all the traditional metrics, they’ve always been about less. [laughs] A shorter handle time, higher containment, which would be fewer calls to the humans and so forth, and I think that that’s slowly going to start to shift. Here’s what I think will happen. If you can really harness the interaction with a customer, and we start to understand them better, because you have things like customer memory, you really understand what the relationship has been, a new metric I think we’re all looking for in this industry is probably something that I’ll just call a verified resolution rate or something like that, where did you actually solve what the customer wanted? And then the dimension that is gonna get added in, versus just how fast did you get them off the phone, is going to be, how long did they stay with you after that call? If you have a customer that comes in, you got them done, and they didn’t like the outcome, they’re not gonna complain, and they’re just gone in six weeks, they churn out. It’s over, right? That’s not great. Maybe that looks good from a handle time, but that doesn’t look good from verified resolution and customer value. So there’s some metric that’s going to start to come in where you’re looking at how you treated a customer and what happened with that customer over time. There’s this longitudinal analysis that’s gonna come in, and if you as a contact center operator can prove that you’re growing that relationship and that companies can start to really think about the contact center, all these interactions being turned into value over time, that’s gonna be super powerful. We’re not quite there yet, but I think with AI and all the data that’s here, I think that day is coming. That day is coming.
[00:15:24] Greg Kihlström: Well, and that also requires tying some of these teams together that may wave to each other from across the building or whatever, but they don’t work together. I think it goes back to the convergence conversation of it’s not that everything gets rolled into one department or team, but that teams start working together more closely. And I wonder if you have an example or could maybe walk through an example of how this could work in real life as far as a brand learning something in a service interaction, tracing that back to a marketing decision, kinda connecting the dots here, and what needs to get fixed to even make that happen internally?
[00:16:14] Jay Lee: I think that marketing is always trying to understand what’s happening with the customer to find out what they like and don’t like so that we can change campaigns and messaging. Probably the most important thing, though, really is feedback that comes back through the customer interactions and then feeds into product and engineering, right? That’s always, “Hey, this doesn’t work really well.” And I would bet you 80% of the time, product and engineering already know that there’s stuff that they need to fix, and it’s already in their backlog. But I think having that constant feedback and understanding that comes through is probably what’s most important. I think that the other thing that you sort of find out, too, is that with the capabilities that you have today, expanding service, even when you don’t have people, is probably another way to really find out what’s happening out there. So if you have specific contact center service hours, and they’re the hours that people aren’t able to actually access, expanding that through AI and other channels to get that feedback I think is also gonna be really insightful. So I guess it’s all just to say there’s no amount of information that’s bad. I think it’s just a question of getting it and then trying to get it to the right stakeholders so that they can do something with it.
[00:17:34] Greg Kihlström: Well, I also wanna talk a little bit about… Certainly, we talk a lot on the show about how the impact of AI and automation is changing roles. It’s usually not even a replacement of roles. It’s more an evolution of where we put human focus and things like that. And so I wonder here, as automation is taking over some of those tasks, those cases, in this case, of things that are easily resolvable, for a human to be part of it, I think in some cases customers just wanna get on with their lives. They don’t necessarily wanna talk with a person to do an easily solvable case. We need the humans, and the customers need the humans to solve the more complex stuff, but that’s also more ambiguous and sometimes more emotionally loaded, which is harder and not easier. So I think one reframing of this is, instead of those paths being an exception to the rule, it’s more like a premium experience. So you get the automated chatbot thing where you can just get on with your day for simple things. But how do you kinda change it from it’s a problem to it’s now we get the opportunity to have this premium experience for our customers? What are your thoughts there?
[00:19:09] Jay Lee: Well, I’ll say I think there’s a bunch of different dynamics in that, Greg. I think the first is you’re right. A lot of the easy routine stuff is already being handled by AI, right? So everything that’s left over requires judgment, empathy, and so forth. It’s all the more complex stuff, and that is ending up with all of the humans. And I’d say one of the byproducts of that that we have to all watch out for is that that just means that their day is getting harder and harder and harder. They don’t get to do simple things that give them a mind break, right? And I think where you’re not really understanding that dynamic and holding your contact center agents to the same metrics as before when they got to filter in a bunch of easy password resets or something, that’s not gonna be so great. But I think, to your point, you’re absolutely right in that you can turn the whole dynamic here, right? If you front your contact center with a voice AI agent as an example that semantically can listen and understand what a customer is asking for, instead of, say, when they had to go back and press 1 for this and 2 for that, there’s only so much information that you can get from that. But when they literally just tell you what the problem is and the AI understands what it is, they know, oh, send it down the easy path, and they’re gonna be happy that they don’t need to talk to anybody, just get it done as quickly as possible. Where it’s something more complex, you immediately know send it to the agent that knows how to handle this situation the best, right? Send it to the expert. And I think that that in and of itself is a premium experience versus fighting with AI that doesn’t know what you’re after, and then when you get fully frustrated after, say, 60 seconds [laughs] you get to go to a human who you have to say it all over again. This whole concept of this premium experience actually is what we call Humantic. I don’t know if you’ve heard us talk about that in the market, but Humantic is human and agentic working together to create a better experience overall, right? And so the idea is if you front the contact center with our voice AI agents, they understand what’s happening, they get you to where you need to go the fastest, understanding the value of that interaction. And then I think what’s really interesting about it too is, and you sort of need the right platform to do this, where the context of what the AI heard in that initial conversation, it needs to get transferred right to the human so that you don’t have to repeat yourself. You know what I mean? So there’s a whole experience in here that is actually quite premium if you do it correctly, and some of the ways that the industry’s evolving prohibit that just because of the way the technology works. But I think that that is the big idea for us here at Five9. We want that premium experience. We want that Humantic experience where AI and humans are working together rather seamlessly, so that you don’t have this disjointed customer experience.
[00:21:53] Greg Kihlström: Well, and to that point, it does feel premium when you’re understood, right? Whether you’re understood by AI or a human, it’s like I know when I walk into a hotel that I’ve been at five times prior and they recognize that I’ve been there before somehow, it doesn’t matter if that human was there or not, if there’s some record, that’s a premium experience, so totally agree.
[00:22:21] Jay Lee: I agree with you. Being recognized, being known I think is the greatest feeling in the world, right? That means that somebody cares, somebody knows who I am and they understand my preferences. I agree with you completely.
[00:22:32] Greg Kihlström: Love it. Well, Jay, thanks so much for joining today. Got two last questions for you as we wrap up here. First one, if we were having this interview one year from today, what is one thing that we would definitely be talking about?
[00:22:45] Jay Lee: I think that we are going to be talking about personal agents. I mean, if you looked at what happened over the last three weeks with Facebook’s Muse, it’s exploded, and I hear story after story of what it is that people are doing with an agent, a personal agent, right? And allowing everyone out there to have easy access to this where an agent is gonna start to do all of this stuff on your behalf, contacting all the brands that you’re a customer of, I think that’s gonna be huge. And I actually even think that it’s not just obviously Muse, it’s a great example, but every other major hyperscaler, [laughs] they’re gonna come out with their own version of that, right? And I think people are just waiting for the one that they trust the most before they really engage with it. So that is going to create a whole new type of dynamic that a contact center’s gonna have to deal with, and I think we will all be talking about that, not even a year, probably in just three to six months.
[00:23:43] Greg Kihlström: Probably a couple months. [laughs] Absolutely. Love it. And last question for you, what do you do to stay agile in your role, and how do you find a way to do it consistently?
[00:23:54] Jay Lee: I will say the only way you can stay agile with everything that’s happening with AI is you gotta be in there all the time. My AI of choice is Claude. My wife complains that I spend too much time with it versus her, so I think that’s what you need to be doing, and the more that you play with it, the more that you get in there to try and use it to solve your problems, the better you’re gonna stay on top of it, for sure.
[00:24:17] Greg Kihlström: Love it. Well, again, I’d like to thank Jay Lee, Chief Marketing and Growth Officer at Five9, for joining the show. You can learn more about Jay and Five9 by following the links in the show notes.
