[00:00:00] Jamie Davidson: To me, customer success managers are like these now, like, they're the Sherpas when you're trying to, like, climb Mount Everest. I'm a customer. I need to get to the mountain top. The mountain top is, like, value for me to see the product. I wanna get there as safely, efficiently, and effectively as possible, and it's the CSM's job to make sure that, like, the customer gets there.

[00:00:28] Andrew Michael: This is Churn.fm, the podcast for subscription economy pros. Each week, we hear how the world's fastest growing companies are tackling churn from using retention to fuel their growth.

[00:00:41] VO: How do you build a habit forming product? We crossed over that magic threshold to negative churn. You need to invest in customer success. It always comes down to retention and engagement. Completely bootstrap, profitable and growing.

[00:00:54] Andrew Michael: Strategies, tactics, and ideas brought together to help your business thrive in the subscription economy. I'm your host, Andrew Michael, and here's today's episode.

[00:01:05] Andrew Michael: Hey, Kelley. Hey, Jamie. Welcome to the show.

[00:01:08] Kelley Turner: Thank you. Appreciate being here.

[00:01:10] Andrew Michael: It's great to have you. For the listeners, Jamie is the CEO and co-founder, and Kelley is the SVP of Global Customer Success of Vitally. Vitally is a platform purpose built for scaling customer success and trusted by over 600 companies, including Intercom, Zapier, and Mixpanel. I'm also proud to have Vitally as a sponsor of the show and looking forward to our discussion today around the future of customer success in the age of AI.

[00:01:33] Andrew Michael: So my first question for you today is what does the future of customer success look like? I think this is probably one of the biggest topics now at the moment in terms of not just customer success, but pretty much everything and anything. Like, what does the future look like? So you see the insides of operations of CS teams across hundreds of customers. What is changing?

[00:01:53] Kelley Turner: Absolutely. I'm happy to start with that. So when I look out and I talk to peers, colleagues, and just truly a lot of friends across CS as well, what I'm hearing from them is the balance of truly being able to continually scale and be able to have businesses that perhaps before were 500 folks for X amount in revenue, but that number is not true anymore. So how do we effectively scale? How do we effectively think about the right ways to engage? But also to make every one of those engagements feel personalized and relevant.

[00:02:25] Kelley Turner: I think we're looking a lot also at things like marketing to say how have we done that well there? Because every touch point matters with a customer, whether it is a support touch point, whether it is a CSM, whether it is when it comes to renewal or having a contractual conversation, it can't feel scaled.

[00:02:44] Kelley Turner: And so when we think about AI, when we think about tools and systems, and when we think about structuring teams, that balance of how do we do this really well operationally, efficiently in a way that reflects I'd say the new state of SaaS, but makes our customers feel like they are getting someone who knows them, knows their business, and knows what they're looking to accomplish is the critical balance.

[00:03:06] Andrew Michael: Yeah. I think there's definitely, like, an interesting thing going on at the moment around personalization, specifically with AI, where it's like a few years ago, it wasn't great. So there was a lot of, like, misses on the end when you had landed in your inbox. You could definitely tell that it was done. Now, like, I think yesterday, when I saw, like, an amazing example of, like, really, really personalized inbound message, like, from the marketing front.

[00:03:29] Andrew Michael: My concern with all of this is sort of, like, at some point, though, if everybody's doing it and everybody knows it's AI, does it still feel personal? And like, I think definitely in the customer success space is like you wanna create these personal relationships. So I'm keen to think like how are you two thinking about this, and like how do we still keep things personal while keeping them at scale and making the person at the end reading it still feel like they're connected to the company and to the person that they know there?

[00:03:56] Jamie Davidson: I think it's important to not try to, like, dive head first into, like, you know, the world of AI yet. It's like, you know, just use AI for everything. Right? Because, yeah, I mean, the challenge of how to keep it personalized is - I mean, it's been a challenge historically in customer success. AI is a tool to help us do it faster, better, and scale it across more, but it's not a replacement to any of it. And so AI is a tool that we can use to get to that personalization level faster for more customers.

[00:04:27] Jamie Davidson: I like to use the analogy to go back to your first question, like, what is customer success? Like, what's the future look like? Like, to me, customer success managers are like these now, like, they're the Sherpas when you're trying to, like, climb Mount Everest. Like, I'm a customer. I need to get to the mountain top. The mountain top is, like, value for me to see the product. I wanna get there as safely, efficiently, and effectively as possible. And it's the CSM's job to make sure that, like, the customer gets there.

[00:04:40] Jamie Davidson: Now you could, today, replace, you know, maybe, you know, a guide up to Mount Everest with AI. But if you're a first time user, a first time climber, or, you know, first time user of a product, the last thing you probably want is AI to be the only thing to guide you to the top. Like, when things go wrong, when market conditions get bad, whenever you just kinda lose your way a little bit, you want somebody who's been there, done that, seen it all, and can guide you through the storm, essentially.

[00:05:21] Jamie Davidson: And I think that's customer success's job. And, oh, again, their job is to do it as efficiently, effectively as possible so that they can move on to, you know, essentially the next batch of climbers that they wanna get to the mountaintop. And AI is just a tool to help them kinda keep that moving along and keep things moving safely, efficiently, and and effectively.

[00:05:38] Andrew Michael: Yeah. I like that analogy. It's the first time sort of somebody's used an analogy and to describe CS in a way, and it definitely makes, like, a lot of sense. I think it's like there's this big goal that you wanna achieve. Like, there's typically, like, a mountain, a hurdle you need to get there. And ideally, like, we solve a lot of this friction with the product, but it's not always possible. And there are things a lot of times aren't actually product related where you need to get help your customers get to that successful state. Seeing those Shipas.

[00:06:05] Andrew Michael: So from that perspective then, obviously, like you said, you're looking as well into marketing and seeing what's being done there and seeing now in the customer success space. Like, what are some of the things that you're seeing companies do really well now and with their use of AI? And then maybe as well a follow-up to that would be like, how are you thinking about incorporating some of these into products and into how your customers will actually start to utilize AI?

[00:06:29] Kelley Turner: I'm glad to share what I'm sort of hearing out in the market, and then I'll tee it over to Jamie in terms of what we're building. There are two themes I would say in terms of how I'm seeing peers use AI really well. The first one is what I'd call sort of enriching their team. So you think about the work that we've done with tools to enrich data. How do we add more teeth to it, more information such that any data point truly is a rich piece of helpful, useful intel versus just another data point?

[00:06:59] Kelley Turner: And I think AI is also helping with that with our CSMs. So whether it is bringing a wide group of data to them easily and quickly so that they can process it and not spend that time going through each individual element that's saying, okay, these are the themes. This is what I need to know to really support my customer. So enriching both the resources, I would say, and the outreach and the conversations with just a nut with a depth of I think information that just isn't as possible when you have to read through extensive items. So I think that's the first piece is how do we enrich those experiences? How do we enrich our communication to make it real, make it relevant, and make it timely in a way that feels once again personal?

[00:07:42] Kelley Turner: The other piece I would say is efficiency of spend. So when we think about where folks are spending and investing and growing their teams, I see a lot of people asking first, okay, where can we do this with AI? Is this something that is an operational task, an administrative task, a tech task? And started to think first about their systems and tools to say, is this something that if we actually invested to make this work well systematically, we could think differently about our resources. And then once they've identified this is truly a resource we need, whether it's CSM, whether it's community, whether it's renewals, how do we help that person really ramp more quickly, be effective, and then be able to grow both their book of business as well as their ability to impact the outcomes of their customers by taking some of the more administrative items away?

[00:08:35] Kelley Turner: So I'm seeing both that efficiency of spend and really enriching the experience to be able to continue to scale, but focusing resources and teams on the spaces where customers are unique, whether it's, you know, the unique profile of that particular hiker or climber. And so that they can give that special help, but be able to more systematically assist on the things that maybe don't need that one to one personalization.

[00:09:01] Andrew Michael: Yeah. Before we jump then into like, Vitally and how you're doing that, I think just another follow-up as well because one of the patterns I've noticed on the enrichment side. I think it's amazing now what we can do with AI and how we can utilize sort of different tools. I think the first sort of wave really allowed you to do, like, simple search and pull back different things. So actually, the first wave was probably like enrichment services, let's say, which utilized a domain name or an email address and collected certain publicly available data points about a customer. And you felt like you had a little bit more context about these people.

[00:09:32] Andrew Michael: Then we sort of like tools like Clay and others, like, started popping up, which allowed you to do, like, take those details and then go and search their site and pull out information and stuff. And then now things like with deep research where you can just literally, like, plug in a customer and, like, it's full on reports back. And I think it's amazing.

[00:09:50] Andrew Michael: But I think at the same time, it can also be overwhelming to a certain degree of, like, understanding, like, what details you really should be focusing on. How do you get to the results where, like, these reports are really helpful and useful for the CSMs and things like that. So I'm keen to hear, like, what are some of the best ways, like, you've seen customers enriching data, and what are some of, like, the best practices you're seeing when it comes to, like, their utilization? So, like, I'm a CSM. I have a customer meeting coming up. Like, what's an ideal scenario for me look like through the use of, like, really good enrichment?

[00:10:22] Kelley Turner: Mhmm. And I also wanna tee it to Jamie because I think one of the unique things that he seems he's thinking about is how you go from information to action. And I think the unique place that Vitally can operate in terms of not just sort of flat information someone engages with but how do we turn that into something that is, what do I do with it? So I wanna give that to him.

[00:10:43] Kelley Turner: I think that is actually where I'm seeing the most excitement is how do we move from a place where it is flat information, where it is a prompt and an ask, a prompt and an ask. But to truly say, if you have a strong AI engine, it becomes embedded such that there isn't that stopping point to say, do I have the information? Then what do I do with it? Then can I turn it into something? Then what happens after that action? But really making it part of the continuum to kind of ignite action versus just information point to advise what's next.

[00:11:20] Andrew Michael: Mhmm. Very nice.

[00:11:22] Jamie Davidson: Yeah. And I can talk a little bit about - I mean, Kelley said it perfectly. It was literally the direction I was going to go with, like, how we kind of see the world of AI play out in our platform. Yeah. It's certainly twofold. It is helping you understand your customers better at scale and then helping you indeed take that next action. Like, we're a unique platform and that - like, most platforms are, like, either here's their data and analytics. Right? Here's, you know, the data. We're gonna educate you as best as possible, but it's up to you then to figure out what to do with it. And then there's just pure workflow tools like, you know, your project management kinda like platforms that are just pure, like, get your work done.

[00:11:58] Jamie Davidson: We're the intersection. We do both, which means we can leverage AI to help you do both, help you understand your customer's scale, and then help you get that next task done. On the first side on helping you understand your customers, customer success is in a really unique position, I think, to leverage AI here because we deal with a massive amount of unstructured, like, conversational data. Emails, conversations going back and forth, meeting transcripts.

[00:12:22] Jamie Davidson: You know, if I'm in a call with a customer and a customer says, I might churn unless, like, you know, integration X gets built and feature Y gets to market or I'm looking at competitor Z. All of that is great structured data, but it's not structured when it's just coming from, you know, the voice of a customer and it's living in, like, maybe a meeting transcript. And so what we can do is apply AI to help get that out so it can be, you know, stored in your structured way so it can be actually - and then, yeah. Once we can kinda do that and leverage Gen AI to help you understand your communications better at scale, we can then help you get the next step done.

[00:12:55] Jamie Davidson: We can help you log the data in the CRM. We can help you send the right email follow-up. We can help you, you know, engage the customers in the right ways. And so I really get it as kind of two-folds. I don't think we can do either one or the other because we don't really complete the narrative. If we just do the first bit, then, you know, getting the work done is a little less efficient. But if we just do the second bit, no customer success team should be, you know, going heads first down trying to get work done without, like, the best understanding of the customer possible. And so we have this unique job where we have to do both pretty well.

[00:13:24] Andrew Michael: Yeah. It's super interesting. I think just hearing you talk as well and listening to sort of the - I like the point of, like, tying in not just having a report, but then having actions on the other end of it. And then I think that's where, like, actually having the customer data plugged into your system, you can utilize that as well along with the context that you start building to start delivering action plans for them and really, like, taking advantage of this next wave now.

[00:13:47] Andrew Michael: Because I think also the other side of it is, like, customer success, as you say, you're living on this, like, trove of conversation data between customers. Like, they're understanding the problems, they're understanding the pain points. And I think more often than not as well, there's this always, like, this little bit of tension as well sometimes between, like, product and customer success and, like, how do we get things prioritized? And I'm hearing these things, and this is not happening. And I think the biggest problem I often see, though, is, like, typically what will happen is, like, a CS rep will come and say, this is a problem. I hear it all the time. Like, we need to fix this.

[00:14:16] Andrew Michael: But then they'll never really be able to quantify that. And it's just, okay, well, give me examples. Tell me, like, and you never get to the bottom of it. And it's, okay, well, if we really need to prioritize these things, we need to be able to actually say, okay, this is the number of people impacted. This is the potential revenue impact. This is why we need to do this now. And I think, like, something like you're sitting on the state essentially. Like, you're having these conversations within the platform. I'm considering, is this something that, like, you're thinking about at least for yourself internally or, like, how you see things evolving?

[00:14:43] Jamie Davidson: I mean, definitely. [inaudible] and actually having that exact battle, you know, all the time here between, you know, customer success on the front lines. They're hearing the same thing over and over again. And, you know, it goes into backlog and, you know, maybe it doesn't get acted on and there's questions of why. Like, I'm hearing it over and over again. Why don't we do it? As a product CEO, what I'll have to say is, well, it's not that easy. Right? Like, you know, some things -

[00:15:02] Andrew Michael: The biggest good question to ask with both of you on the line. Yeah?

[00:15:06] Jamie Davidson: Yeah. It's like, something might be a common paper cut, but, you know, there may be a workaround to it. And the solution might be so complicated that, you know, it's worth having the paper cut, for example. Or maybe it doesn't align with, like, the product vision, the product strategy. Because, you know, it's a balance. Right? Product management is a balance of strategy and vision and also satisfying customers. And if you just do one or the other, either you end up with a Frankenstein product that has no vision or you end up building something that maybe customers don't want. So it is a balance.

[00:15:54] Jamie Davidson: I think we're well positioned to do it because, yeah, we have the most robust understanding of the customer possible. We know, you know, when a customer churns and why. We know who was talking to them before that happened, how they were using the product, what features they were using, what features they weren't using. And that can you know, that is a massive amount of data that once we are able to digest it, analyze it, and show it to the right team in the right context, can educate that next step for anybody and can kinda help people get aligned.

[00:16:21] Jamie Davidson: And so if we can correlate, like, you know, common feedback across these customer records and say that, like, you know, this actually led directly to, you know, hundreds of thousands of dollars in churn in the last year alone, that helps make the decision for the team and justify why something is being done or why something isn't being done. And so, yes, we absolutely can and are thinking about ways to solve this problem.

[00:16:43] Andrew Michael: Nice. Yeah. Because I think, like, a lot of times we - this is a very, like, common pattern I noticed when it comes to churn and retention is that as soon as somebody has a problem with it, they say, okay, like, we need to figure out why. They churn - like, they turn to a churn exit survey. They think, okay, like, the reasons for churn, this is why we need to fix these. But more often than not, those aren't really the problems. And most of the time, those aren't really, like, the good fit customers that were meant to be with you anyway.

[00:17:08] Andrew Michael: The problem's very early on. It's on activation. People haven't really got to establish the value, and you need to understand, like, what are successful people doing to help more people get to that success state. And I think with, like, the data that you do have is you have those early conversations. You have those things that are the precursors to churn and to understand like what's leading up to it versus like that end result. And more often there's not, it's not just like this, oh, I wake up today, I wanna churn from Vitally and leave the product. It's like, this is a series of decisions and discussions and pain points and things that have happened along that journey. And you're definitely sitting on, like, a lot of that data. I think that can help the end user get to where they need to be.

[00:17:44] Andrew Michael: What's one way that you're utilizing then, let's say, like, some of these new tech in Vitally with your customers?

[00:17:50] Kelley Turner: So I will say when I think about what we're doing with Vitally. First of all, I've been doing a lot with my team in terms of just learning more about AI. I think there has been a natural - there's a natural learning curve for folks in terms of getting more comfortable understanding what is a GPT, how can I use this and making it feel very, very natural. So I've been doing a lot of that just to say, hey, anytime you start coming to a conclusion whether it's prepping for a conversation, whether it is sending a note, whether it is thinking about a QBR, take the time, run the numbers, start looking at how we can do themes and and conclusions more accurately out of AI.

[00:18:30] Kelley Turner: I think it reinforces the efficiency play, but more often it reinforces thinking differently about the problem, which I think is gonna be sort of the real growth engine as we use AI is it will fork a force us to think differently about the question we would ask, how we would go about it, and what really good preparation looks like, I think, in terms of our customer engagements. And I mean that both macro and micro.

[00:18:56] Kelley Turner: Whether it's preparation for a call or preparation for how do we come up with our product roadmap, how do we think about what we should monetize in terms of different services, how we think about what the right segments are. That's also, I would say, as a CS leader, where I'm looking to use AI a lot more is not starting with a question to find a specific number, but what is it that I haven't thought of that's in the data that could really change my point of view on what the outcome should be?

[00:19:25] Andrew Michael: Mhmm. That's interesting. I think because a lot of times, like, we just don't have the bandwidth to process all this information and AI does. And you were gonna say something as well, a follow-up to that, Jamie?

[00:19:34] Jamie Davidson: Yeah. I was gonna say that, you know, our team, of course, is leveraging AI to better educate themselves about, you know, how to prepare for a conversation, customer meeting, a QBR, and everything like that. I think the important thing, though, and especially for customer success that I encourage is to going to do the job for you, not yet and certainly not by a long shot. And it's also not, again, I think what customers really want right now, they want that personalized experience. Like, they want to to feel like a human and, you know, relationship exists, and the human has tried and, you know, learn as much as they can about them.

[00:20:05] Jamie Davidson: And it's similarly in the history of customer success. We've had, I think, a similar problem with health scores. We've all, like, you know, last fifteen years, have all wanted one score, like, one metric to tell us if a customer is gonna churn or not or is, like, healthy or not. And we want that thing to be the indication of, like, what we should do next. And I don't think we've gotten there in, like, fifteen to twenty years of customer success because it's impossible to have, I think, one score that tells a completely, like, comprehensive story of the customer.

[00:20:33] Jamie Davidson: And so, like, we had a - even a few years ago, we had, like, a little bit of a campaign calling, like, trust no health score. Like, you have to treat it with skepticism. Like, it's a suggestion of, like, maybe what's going well or what's not, but you still have to know the customer. You have to dig in. The same thing is true of AI. If it's, you know, telling you these are the things that are the next steps, if it's trying to give you a summary about where a customer is at and whatnot, like, that's great. It's a starting point, but it isn't the directive. It isn't exactly what you should do. It is still your job to understand if what it's recommending is helping you uncover maybe some blind spots. It's telling you things you already know, or it's perhaps missing something and it's time for you to still dig into the data.

[00:21:08] Andrew Michael: So, yeah. Yeah. I think that's a very good point. And I think it's definitely, like, one of - I've noticed in my habits while using different AI tools is that I never normally, like, rely on the first answer anymore. And I think it typically tends as well to bias to the answer that you would be expecting as opposed to the answer that's like, it should be, is the best answer. And I would also typically like say like, do not take any bias for my question. Answer me as like the best answer possible that you believe for the specific topic. And like, I think that's sort of like tensing.

[00:21:37] Andrew Michael: And I also like, do you really think this is the best? I'm questioning and probing as well because I know often than not, it's not like that first answer that you're getting back, but there's layers deeper that uncover insights, to your point, Kelley, as well that things you weren't even considering and just realizing now.

[00:21:53] Kelley Turner: I would agree. To me, AI is a lot like - so I agree in the trust no health score because as soon as you start averaging any sort of data, you lose the peaks and valleys of what actually was true. To me, it's much like a signal. It's a signal of, is there additional information you need to gather? Is there something that might have been a blind spot? But treating it, I think, as a signal adds a element both of richness, but of a need for further action.

[00:22:23] Kelley Turner: And I think in a world where folks have large books, have a lot of priorities, being able to quickly get to where are my signals and what are the signals that matter, that I think is is helpful for CSMs and CS teams to then say, now I invest that personal time. Now I really dig in because it is not just a customer, it's a human. But I'm well equipped with some information to make that conversation meaty.

[00:22:49] Andrew Michael: Yeah, very nice. And you also mentioned as well that, like - I think this is a very, like, common habit, pattern happening across most, like, tech companies outside, at least to start, is that everybody's questioning and saying, okay, how can we get the most out of this? There's been a lot of like memos that have gone around and said, okay, we're not gonna be hiring anymore unless you can justify why AI can't do it. So I think Toby from Shopify, I've seen another three or four others like post these memos.

[00:23:16] Andrew Michael: And this is definitely a common pattern. And I think, like, the more we advance and the better AI it gets, more people are gonna be looking for these efficiencies. So I think in this world then, like, things change quite a bit, I think, from a customer success base, from a pricing and packaging perspective as well. I think for the most parts, like, CS tools and most SaaS software revolved around, like, pricing per seat. And so I'm keen to see, like, how you're seeing this evolve in the landscape. How are you seeing your customers adapting to this? Like, is it still too early and we haven't seen this big wave shift yet? And, like, is it something we anticipate? Or, like, you think it's just going to be, like, this makes us more efficient and we're still gonna continue to grow large organizations and there's still gonna be a space for both?

[00:24:01] Jamie Davidson: I think it's more the latter. I think we're still too early. I mean, I think the challenge - yeah. Like, actually, Duolingo just did the same thing the other day. I saw it because they made a bit of a - yeah. A wave of - yeah. They created a hundred AI courses in, like, a year when it took, like, fifty years to do their first 150 or something like that. But, I mean, I think the challenge of, you know, pushing, you know, people to think of automation or AI as a solution to a problem is something that's certainly fine and was something that could have been done even before the world of AI. Like, there are plenty of inefficiencies in businesses that could have been automated away that, you know, us humans were doing and challenging everyone to kind of try to find those and, you know, create more efficiencies there is fine.

[00:24:43] Jamie Davidson: I think the tone of some of these messages could probably be worked a little bit from the CEOs and the executives that are delivering it. But I think, naturally, the danger of again, the tone gears towards, like, everything can be AI automated away is like, well, yeah, I definitely don't think we're there yet. I think it's probably at least another decade off before we're really going to be seeing, like, such a dramatic shift across the board in companies and enterprises, like, just truly having, like, AI only employees and things like that, like, if we ever even get there.

[00:25:13] Jamie Davidson: And so I think the danger is in the short term leading to a more mediocre, like, less differentiated kind of company or offering. Because, yes, AI could probably do a lot of the things that your employees are doing in a somewhat competent way, but that's not the bar, at least not in a very competitive market. And so you saw it with the backlash on the Duolingo of people talking about how the AI courses are lower quality and, like, there was, like, people asking for boycotts. Like, I think you need to dip your toes in the water a little bit and and try to, like, bake it in a little more organically. Don't lose the differentiation, which the humans can give you today, especially again in a competitive market.

[00:25:55] Jamie Davidson: And, yeah, you know, things will evolve over time and, yeah, in 2030, and beyond, I think, yeah, companies will certainly look a little bit different, but I don't think this is gonna be something that just dramatically changes the landscape of a company in the next, like, year or two. It'll take a little bit of time.

[00:26:12] Andrew Michael: I think I saw an interesting, like, I think it was a post on LinkedIn by Jason Lemkin where he sort of highlighted and he said, okay, like, AI is going to make your, like, top performers most highly paid look cheap because it's gonna, like, 10X their performance. And it's going to make, like, your mediocre team members look expensive because they could be replaced with AI. And I thought that was, like, an interesting take as well. Like you said, it's a super competitive landscape. Like, if you're not adapting these tools, like, at the top level to take advantage of them, you're like falling behind competition.

[00:26:44] Andrew Michael: But there's also the other side where like there are probably people within the org that could be replaced with these tools and it's naturally going to happen. But, yeah, I found that it was an interesting, like, take on sort of things. But, yeah, I tend to agree. I think, like, things change extremely fast and extremely slow at the same time. And I think we'll notice these different changes in different ways.

[00:27:03] Jamie Davidson: I like to use the analogy. I got into a debate with, like, an old business partner of mine, like, ten years or fifteen years ago, back when like self-driving cars came about. And everybody was like, when that came out, we're like, okay, in the next, like, you know, five years, we're gonna have self-driving cars.

[00:27:17] Andrew Michael: I was one of those people.

[00:27:19] Jamie Davidson: But I was in the other side. I was like, no.

[00:27:21] Andrew Michael: I have this debate with a friend of mine as well. So yeah.

[00:27:23] Jamie Davidson: I was like, that's gonna be, like, thirty years. Like, I mean, first off, there's a whole bunch of regulations and governmental, like, you know, things that we gotta get through. But second off, like, there's, you know, 8,000,000,000 people in the world or whatever it is. Like, we don't just all change overnight. Like, it's sometimes a slower evolution. And so yeah. Like, these changes the Internet was maybe a little bit of a different thing where, of course, like, that was rapid evolution. Like, that sort of dramatically changed things. But, typically, things evolve a little bit slower than we kinda come to expect to begin with.

[00:27:54] Jamie Davidson: And I think AI is probably in the middle. I don't think it's the Internet, and I don't think it's the self-driving cars. I think it's probably somewhere in the middle. It's going to introduce rapid shifts as we've already seen, but it will also take time for consumers to adapt to it and everyone else. So, yeah.

[00:28:07] Andrew Michael: I think there's the levels of friction involved. Like, self-driving cars, there's a lot of friction. There's a lot of danger where AI as well, like, there is elements of danger still, and there's that hesitation. I think there's also people feel threatened by it, I think, at the thing. And the other side of thing, for me, I get a little bit surprised still where people like get defensive and threatened by it because I think this is like just missing the point. And in some ways, like, you're digging your own grave as well by not wanting to learn and understand and how these things work and take advantage of them because that's what they're there for, for us to improve and to be better.

[00:28:41] Andrew Michael: Nice. And so maybe with that in mind, I'm keen to understand, like, how are you thinking about the team and growing Vitally, and have you written a memo internally that you're sending out to everybody, get on board or jump off ship? That's been the general theme.

[00:28:55] Jamie Davidson: No. We haven't gone that far. I mean, it's been more of a balance, right? We certainly are encouraging and creating structures and programs to train and educate people to find ways to, you know, use it and improve their work. I look at it as I often do a lot of things of empowering managers and the middle managers and down to kinda, like, you know, find what's best for them. I'm not typically the type of the CEO that just has, like, one broad memo that says, Everybody do this or, like, you know, adapt or die? Like, it's not, like, typically my message to the team, at least not broadly.

[00:29:26] Jamie Davidson: And so, like, yeah, we're working on training, like, you know, engineers to code with AI. We're working with, of course, the CSMs to adapt AI into how they, you know, understand and prepare for their customers. And if, you know - we're certainly, you know, not as large as some of the companies that are sending these memos, which you kinda get because if you're, you know, if you're 30,000, 40,000 employees. It's kind of a broader message, gets the thing across a little bit more efficiently and effectively.

[00:29:54] Jamie Davidson: I still know every job role that, you know, goes up for us. And so I don't need that memo at least at our stage. And if we - yeah. Depending on what the role is and whatnot, if there's an opportunity for us to question whether that's the right role in this new age of AI or whether AI can be used there, and maybe that budget can be applied to something that's a little bit more challenging or differentiator or, like, a tougher kind of position to fill, then that would be a net win for the business overall.

[00:30:18] Jamie Davidson: But, yeah, we have the luxury of a little bit of a smaller scale than some these companies that, you know, where the CEO certainly doesn't know every job role that they've opened, and they need to kind of force that change of thinking a little bit, you know, across a larger population.

[00:30:31] Kelley Turner: I would say leading the CS team at Vitally, it's mostly inspiration with some expectation. So inspiration is, hey, have you thought about or did you just see what somebody tried and how well it worked or the win they just got by using XYZ. So it's a lot of sort of crowd sourced, hey, let's check this out. Oh, that's really neat. I'd love to learn that. So really encouraging people to try, to share, to feel safe, trying something and then really giving recognition, appreciation and then the ability to impact their peers really well by sharing their learnings or their wins.

[00:31:04] Kelley Turner: I would also say though that being a CS team at a CSP, we have a unique role in being both consultative at the product level but also being a sort of a correlator and a connector of information across CS. And so I very much encourage my team to be asking questions to their customers. How are you using it? And then pushing themselves with that information to say, how do we continue to be leaders in the space, be able to speak authentically, but also with sort of a personal experience to this is where I'm seeing value to then pass that inspiration on to other folks.

[00:31:39] Kelley Turner: Because we we have these conversations and I think it's an honor that we get to sit in this space and have conversations not just about sort of product and platform but where folks are going overall and being able to bring additional information from the wider crew to advise them and and perhaps inspire them to try something new as well.

[00:31:58] Andrew Michael: Yeah. That's very interesting. It's a very, like, advantageous place to be as well from your perspective, especially when you're looking for inspiration. Like, as you're talking, I was just thinking now, like, how are you sharing these stories more widely as well? Like, there's probably a lot of valuable content that you're sitting on from an internal perspective just hearing your customer success stories. And I think a lot of companies now are interested to, like, see interesting ways.

[00:32:21] Andrew Michael: And I think it's definitely happening in, like, coding, and it's happening in marketing now. And you're seeing those, like, being shared more widely in sales. Like, I haven't really seen many of these success stories, like, floating around from a CS perspective yet. And I wonder when those things are gonna start popping up on my LinkedIn feed and just taking over there.

[00:32:38] Kelley Turner: I'll say for the good of the folks who are already working with Vitally, we're really doing an investment in community coming up. And I think some of that opportunity to continue to connect people and learn not just tactic but strategy in this space will be really exciting. And once again, inspiration creates more inspiration.

[00:32:54] Andrew Michael: Yeah. Absolutely. I have a question then as well. And I was thinking how to phrase this because my first instinct was like, what does CS look like in five years? And I think, like, five years almost feels like impossible to predict at this stage. So my question is like, today is obviously like, what is the future of CS within AI? Let's like put a milestone out like two years from now, three years from now. Where is customer success? Like maybe in your mind, Jamie, like, what is the ideal scenario from a product perspective? And then from your side, Kelley, like, from an operational perspective and a CS, like, what does that future look like two, three, if you wanna go as far as five years and you can do you think you can see that far? But I'd warn you, my friend still reminds me like ten years later that I was wrong about self-driving cars, so.

[00:33:38] Jamie Davidson: You mean for a product - you said for a product perspective. You mean like in our product, what does it look like?

[00:33:41] Andrew Michael: Yeah, your product or in general, like what would be the ideal state? And then, can you like as an operator how you see things evolving?

[00:33:49] Jamie Davidson: It's a good question. I mean, I think in the ideal state is that everything that can be automated or repetitive or definitively decided is done for the CSM. I mean, historically, before, especially before the world of AI, CSMs spent a lot of their time doing, like, manual data entry. They would talk to a customer. They would take notes, and then they would figure out which fields to update, you know, what task to create and whatnot, which email to send and who to send it to, when to get logged in the CRM. And then maybe a fraction of their time went to figuring out, you know, strategy and, like, unique engagement kinda, like, you know, mechanisms and, like, hunting for that, you know, that one customer that isn't actually labeled as a churn risk, but is and something that they can, you know, actually, you know, go out, seek out, and and resolve.

[00:34:36] Jamie Davidson: And I think in, you know, ideally, in the next even couple of years, they are spending almost next to none of their time on that repetitive, tedious, you know, manual data entry note taking side of things. Like, I like to think of, like, customer success, it's like there's the note taker version of customer success, which is like the old version, and then there's, like, the next gen version. If all your CSMs are doing or talking to customers, taking notes, moving on to the next customer and taking notes, that can be done by AI. What you really want your CSMs to be are, to Kelley's point, like, the consultants, the experts on your product, on what you're doing, and then on each customer's business.

[00:35:11] Jamie Davidson: And, ideally, what the future holds is CSMs to have a whole bunch of time to achieve that goal and less time spending on the tedious and on the repetitive because AI is doing that for them. So they become these much more differentiated skill thought leaders on their customers and on the product and can actually spend time doing the unique strategies and the unique engagements with customers that have true measurable impact.

[00:35:36] Kelley Turner: I love that, for very obvious reasons. The other thing I would add on to it is that there's no such thing as stale customer data. One of, I think, the challenges that I've seen in so many organizations is the constant element of data cleanup. Our contacts from Salesforce are old. Our information on X, Y, Z is old. And so because of that, a lot of CS teams spend their time in spreadsheets. I will say, being a leader at Vitally, this is the first organization I've ever led where I was not constantly feeding spreadsheets to my team. It is dynamic. We have that ability today and it is incredible.

[00:36:13] Kelley Turner: What I think though that AI also has the ability to do then is to keep that kind of information true and clear and fresh. Not just specifically what's in the platform about how they're using it, but about the organization. I mean, so many folks have spent time, oh, well, this company is based in this organization, in this country, in this state, in this city. This is where the people are. With AI, we should be able to really quickly be able to see where are our customers located, where is those spaces, where should we be thinking differently about events and connection?

[00:36:45] Kelley Turner: And if we also are pulling in more information truly from wider available sources about customers, then we come in with truly confidence in knowing everything I'm looking at is correct, up to date, analyzed, thoughtful. And then that conversation is so rich. That conversation walks in within - one of the interesting things I think in like a vendor partner relationship is there's always an imbalance of information. So one side of the house is always trying to understand more about the other side and spending a lot of time on those questions.

[00:37:19] Kelley Turner: If most of it is actually understood already, then those conversations are not about, so what is your goals for this year based on what your board has told you? Because shoot, we already just pulled in your last financial reporting. It's, hey, we both know this is what you're trying to do. Let's talk really specifically about what's in your way for that goal. And it gets to that meatiness and that depth that usually you have to go through so many levels of data validation, question, and understanding to get to.

[00:37:50] Kelley Turner: And I think that then, that is the personalization. That's the connection. That's the human. How do I help you overcome the roadblock in front of you? Because I am here to be that person. I have the path. I've trod it many times before. How can we go arm and arm together there?

[00:38:07] Andrew Michael: Yeah. I think that's super interesting, like, as a use case as well for AI. Because these tools and systems are only really as good as the data that's fed into them, and data does go stale and becomes useless. But if you actually have AI, like, constantly trying to refresh this data and understanding and updating for your customers, and that way when you do start to seek for, like, answers and questions, like, you always know you're getting the information. And as you said, like, both of you, it's the end point where you don't have to do the mundane tasks of the data entry or the data cleansing or the sorting and filtering and stuff. And you're really just there.

[00:38:41] Andrew Michael: You have the answers basically, and you have the confidence then to like go into these discussions and spend time building relationships, I think, at the end of the day, which is like, it's doing the job of like a CRM, like customer relationship manager, which is always like a weird title for database. But, yeah, nice.

[00:38:58] Andrew Michael: So I see we're running up on time today as well. Before we wrap up, like, I wanna leave the floor open. Like, is there anything that you think is interesting or top of mind that the audience needs to hear about, like, now in this next wave? Any parting thoughts that you would leave us with today?

[00:39:27] Kelley Turner: I think there has been a lot of change in CS, I think, in the last couple years financially, et cetera. And it has brought in, I think, a new wave of data-focused, revenue-focused leaders who - we are thinking business, we are thinking relationship, we're thinking impact. And all of us, I think, are looking to be inspired by the potential and at the same time not losing that personal touch. And so that's what I think I'm really excited for is how we also evolve AI in CS.

[00:40:01] Kelley Turner: It's sort of, I think, just a, here is AI, how will CS use it? But much as every discipline over the years and decades has figured out the right tools and structures for them, I'm excited to see us evolve what AI could be for CS, not just here's what you have, but what do we also make it for ourselves.

[00:40:23] Jamie Davidson: And I got something. I wanna talk to the non-CS leaders that maybe maybe listen to this is that too many, I think, non-CS leaders, maybe even CEOs, but even financial folks look at - yeah. The customer success like, you ask them what the job of customer success person is. They'll say to talk to customers and hopefully help reduce churn. And that's a probably uncontroversial answer, but I think it's the wrong answer. The job of customer success is to, ideally, of course, reduce churn, but especially in tough market conditions, which we might be in, which we're certainly in a little bit right now, is job of customer success then to also influence the business that we can the business can better itself from what it is learning from customers. The job is to be the internal voice of the customer.

[00:41:04] Jamie Davidson: And so as it, you know, as customer success looks to learn what is causing churn, we'll learn how customers are using the product, the more that they can leverage that data to then, you know, collaborate with other teams to help marketing speak more directly to the customer, to help sales pitch real world solutions to the actual problems that the buyers are gonna face, to help product build a road map that's gonna have a greater impact on churn. Like, that is, I think, the future world of customer success. And I think CEOs, CFOs, CROs need to get out of the lens of the job of CS is to talk to customers and take notes, and it's to be that internal, like, strategic voice of the customer so the business can evolve backed by customers.

[00:41:44] Jamie Davidson: And not enough, I think, non-CS leaders look at CS in that way. AI can help us get there and how it can help CS teams go from maybe that note taker and that, like, very focused on the relationship side of customer success to that internal, like, voice of the customer. But it's - I think CS leaders need more empowerment from CEOs and CFOs to get there. And I don't see it enough, I think, today.

[00:42:08] Andrew Michael: Yeah. I think that's well said. And I think, though, this has changed a little bit, I think, since, like, COVID and then now all of this wave of AI. I think, like, there's definitely been, like, this switch. At least I've noticed in the interviews with CS leaders of, like, CS now having more of a focus around revenue and, like, generating impact in the business. And I think this as a result then is helping, like, have more of that impact within the wider organization because it's no longer just, like, updating CRMs and many relationships. Now it's actually this, like - and churn is a big part of it, but it's also become like a revenue driving role as well because you see the impact through these relationships that are built and through the engagements with customers.

[00:42:45] Andrew Michael: Very nice. Well, it's been an absolute pleasure chatting to you both today. Thank you so much for joining. It's been great talking about the future of AI. Hope to see, like, our predictions come true and that you're not being pinged, like, two, three years from now and saying, hey, Kelley, Jamie, you're completely wrong on that. But, yeah. Literally, it's like every year, I get a reminder from this friend. He's like, hey. No self-driving cars. Like, hey. What's going on?

[00:43:05] Jamie Davidson: I find it amusing that I use the analogy that really hits you there. I didn't know it would.

[00:43:11] Andrew Michael: But no. So, yeah, thanks so much. And for the listeners, we'll make sure to leave everything we discussed today in the show notes so you can pick those things up there. And thanks so much for joining both of you. Best of luck going forward.

[00:43:21] Jamie Davidson: Thank you for having me.

[00:43:21] Kelley Turner: Thank you so much.

[00:43:22] Jamie Davidson: Take care.

[00:43:23] Andrew Michael: Cheers.

[00:43:30] Andrew Michael: And that's a wrap for the show today with me, Andrew Michael. I really hope you enjoyed it and you're able to pull out something valuable for your business. To keep up to date with Churn.FM and be notified about new episodes, blog posts, and more, subscribe to our mailing list by visiting churn.fm.

[00:43:50] Andrew Michael: Also, don't forget to subscribe to our show on iTunes, Google Play, or wherever you listen to your podcasts. If you have any feedback, good or bad, I would love to hear from you, and you can provide your blunt direct feedback by sending it to andrew@churn.fm. Lastly, but most importantly, if you enjoyed this episode, please share it and leave a review as it really helps get the word out and grow the community. Thanks again for listening. See you again next week.

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