Season 3 Finale - Kate Neal on AI Proofing your CS Career

Hello, rebels, and welcome back to the studio. We are above elated to be welcoming Kate Neal with us today from Gainsight to share all the fantastic AI strategy with us and just to talk about kind of the significance of that and what that looks like operationalized at scale. Scale and the benefits of that can happen inside of the customer journey relevant to that. Welcome, Kate. We're so excited to have you here on the show. Thank you so much. I'm really excited to be here. Yeah. So tell us, it's important, I think, to talk about journey because we are people, right? And so would love to know more about how your journey ebbed and flowed to get to where you are today and all of this. Tell us all about you. Yeah. So I took, uh, bit of a roundabout journey to make my way into tech. Prior to tech, I had owned and operated a landscaping company for many years, probably more than I care to admit. And I had just gotten to the point where my body already felt 80 years old, even though I was, you know, in, in my thirties or late twenties. And so I was starting to explore, you know, what other options were out there. When I went to college, I majored in creative writing and theater arts. So it wasn't necessarily like a super serious career generator in many ways. And so I just, you know, started looking around at what was possible and what I could shift into that had transferable skills. And by chance, I ran into an acquaintance out one night, a dive bar in Portland, Oregon, and was catching up with her and letting her know like, hey, I'm, you know, just kind of on the hunt for a job. She was working for a tech startup and said, hey, we're looking for, they called it a managers at the time. It was, it was account management, but also CSN kind of wrapped into one role before they, they were calling it customer success or CSMs. And she said, you should apply. I think that, you know, you can make it work. And I said, I have no experience in that area, but sure, I'll apply. And sure enough, I got the job and stayed at that company for about 7 years, really building the customer success function from the ground floor. So implementing and administering Salesforce, Zendesk, creating process. We were handling a very small team of us, the end-to-end process. So we would take inbound sales, convert them to customers, and then continue with retention and expansion beyond that. So that's what started me in tech and then grew from there. What sort of my career has been defined by is love to go into early to mid-stage startups and build that customer success team and function from the ground floor. So I've done that at 4 different startups. Most recently, back at the end of 2021, I joined a very early seed stage startup called Staircase AI. We built the industry's first customer intelligence AI native platform. And last year, year before, it was acquired by Gainsight. So that's how I made my way to Gainsight, by way of that acquisition. Yeah, there's something about the excitement that exists in those early stage startups. It's hard once you've been there and you've been part of that and you see how that can really just impact the whole client journey. And it sounds like, you know, in your situation, quite frankly, you were building across the whole funnel, which is really cool. So Staircase, actually, the first time that I saw that, the first time I saw that demoed, I just thought, I said, wow, this is cool. And I'm curious, you know, it's very apropos, and obviously because you, you are with that with that entity. I mean, where do people start? What does it really solve for, right? I mean, in my opinion, it really gives us the ability as leaders to get an idea of visibility. It's like, hey, you can't fix what you don't know. It sounds very antiquated, right, which is not obviously aligned with Rebels of SaaS in any way, haha. But We can't fix what we can't see, right? 100%. 100%. Yeah. I mean that, you know, Staircase was born out of a need that the CEO and co-founder Ori Entis, who I, I worked side by side with to build that company and product, had previously been a customer success leader for a global team at a, at a very large company and had, you know, all the tech stack at his fingertips, had the, the CSNs, the teammates. And kept finding himself unable to answer really two fundamental questions as a CS leader, which is what's going on with my team and what's going on with my customers. And sort of pre this AI transition in customer success, the way that we would determine, you know, what we call customer health is we would look at a subjective measure from the CSM and we would look at product telemetry. And there's a, a missing piece of that, right? Which is the human-to-human exchanges that you're having with your customer across various communication channels, and those are all happening in silos. We're not aware of what's going on. And to your point, you know, knowledge is power. Like, that is a, a saying for a reason, because you can't improve things that you aren't aware of, right? And so that's what Staircase does is, you know, if we think of a product usage tool, we call that product analytics. What Staircase does is it connects to all of those channels of various customer communication across chats and emails and meeting transcripts and support tickets, pulls all of that in, and it analyzes it so that you understand what's going on with that human-to-human connection. What are signals of risk or opportunity that are coming up? And it can look at a lot of things that the naked human eye can't see, right? You know, as CSNs, they tend to get into their inbox and into their calendar. And if you aren't a part of that, right, those customers in their book are typically falling, you know, through the cracks and to no fault of their own. It's just difficult to keep up with everything. And so it's able to kind of aggregate all of that data and then provide insights at both an account and of course across account level. Wow. Yeah. And see, to me, I just, I remember the first, again, when I was checking it out the first time and hearing some of the idea and the visibility, my thought was, hey, this is remarkable. Because like you said, going through and building a traditional, I'm just gonna, I mean it comedic, but it's true, 17-step manual entry score that's missing everything. We don't know what the sales, what's the sales guy talked to the client about this week through the communication channel. What's the Slack? You know, what's going on on Slack, what's hit, what's coming on on Zendesk, what's the, you know, not just, oh, they had 5 support tickets. Like we would say, hey, that could be potentially a health indicator or not. 10 years ago, we would. We obviously, that's not true now, right? And we all know the reasons why, because there's context behind all of those engagements. So all of that being said, how difficult, I'm curious, is this from a perspective of, you know, the uplift, so to speak. I mean, is, are you looking at something as, I know you said you implemented Salesforce. For those of us that have, we know the, I'll just say nuances of doing that. Like, is this a, is this as difficult from a tech stack perspective as like standing up that functionally? Or is this something that's a pretty plug and play? Like, talk to me a little bit about that. Talk to us about that. What's that look like? Yeah, that was really one of the fundamental pillars as we were building this product is we didn't want to create a product that required that sort of manual overhead, both to implement and also to administer over time, right, to upkeep. And a lot of these products that we're using in our tech stack to get value, they require the humans to go and input data, input context. And that takes time and people are inconsistent and humans are inherently biased. So one person's interpretation or judgment is completely different than another's, right? That's not good or bad, it just is. And so Staircase is a tool. I, I remember, and this is early on, I, I think, you know, now that we're part of the Gainsight team, there's rotations and onboarding, especially for customers that share the integrated platform of, of CS and Staircase. Together, but a Staircase standalone implementation, I think my record when I was still running those pre-acquisition was we did an entire onboarding session. We got the entire tool set up in 45 minutes. And luckily it was just one person that was the admin of all of the integrations that we needed to do, who also happened to know the CS strategy so that we could input and kind of tune a couple of the settings. But really once you connect the systems and you fine-tune a little bit of the settings, the system, one, gives you value immediately, right? Within 24 to 48 hours from loading the accounts and the data, you have insights at your fingertips, both again— Oh, stop it. You're literally reading my mind. That's the next thing we were going to— oh yes, that's cool. Yeah. So it's very easy. And, and then the other thing that was important to us is that we really didn't want to build a system that required any manual input. So beyond the initial onboarding and setup, and then of course, like ongoing, if you change your segmentation or if you change your engagement model, you may have to make a couple, you know, go back in and make some tweaks to the settings. But on a day-to-day basis, the team isn't inputting anything into Staircase. It's collecting all the data that it needs automatically and giving them insights, right? So that they can just go parse through the signals and take appropriate action. That's awesome. What, from a metrics standpoint, right? I mean, I know obviously going back to that whole visibility piece of this, when you look at the journey, I loved what you did in Boston, by the way, and when you were speaking and sharing at CS Collective out there. And so, you know, you chatted a lot about and shared about in the community around journey and aligning that to kind of that long age-old Pareto's tail, that long tail of customers, like you said, that smaller segment of customers that we all have that really can add up to be monumental for our business. And I mean, so I'm curious what, when you are looking, building, like thinking through this from an orchestration standpoint, what metrics are the most important from your perspective? Around customer journey and how AI interacts specifically? I mean, I think the first thing is super foundational, which is having a clear customer journey with defined milestones and outcomes, right? And ideally that doesn't necessarily differ too much between the segments that you have. There may be some nuances here or there, but that's, that's just a very fundamental kind of foundational place that a lot of companies are still not quite figuring out, right? Maybe they have a ton of different segments or across different business units or across different products. And I think the way that you can set yourself up for success for the best in terms of the future of AI is to get the foundation and the fundamentals right. So that's your data layer, that's all of your processes, because that's what AI needs, right? AI isn't exactly intelligent in the way that people think it is, at least not yet. It's really working on like tokens and pattern recognition and able to follow really clear directives and instructions and decision trees. And so if you're really looking to start to kind of weave AI into everything that you're doing, I would start with defining, you know, what are the key data points that you need to fuel AI to get to your desired outcomes? And what are the processes that we need to train, right? Agents or AI, I think of them as digital teammates. And so just like a human teammate, they need onboarding, they need training, they need processes and instruction, they need coaching. You, you know, you, you monitor with accountability and provide input so that they can improve over time. AI is the exact same way, right? And so when you're looking at that, you wanna start with, you know, I think the foundational metrics may change per customer, but they're generally gonna be like, We know who the customer is, when their renewal date is, what the ARR is, right? And that gets us to the right context. AI having the right data and context to give us those, the right outputs. Once we have the outputs, then the metrics that we're trying to impact, there's the lagging, of course, right? GRR, NRR, it's kind of things. But we also want to make sure that we're identifying signals of risk. Many days sooner than a renewal, right? We want to track right now before we start is what is the time between first signal and renewal today? What's the average time? And once we implement AI or a system, for example, like Staircase, that's giving you much more proactive early signals, are we able to increase the average number of days between first signal and renewal? AKA, are we able to build in programmatically an increase in our runway time to tackle an issue before we come up against the pressure of a renewal. Because that's where the customer starts to get leverage. Yes. Yes. And how many times, I'm sure so many have probably that are listening to this right now, have went to forecast a renewal and you miss one because we went off of gut feeling, you know, instead of, and yeah. And I liked what you said about the inherent sort of, you know, human in general. I mean, obviously that's the wonderful, in my opinion, one of the wonderful opportunities that does live inside of AI in general and innovation inside of it. It's like, hey, there's all these really great ways for us to be able to partner together and just make life better for our customers. And that's pretty, that's a pretty cool opportunity. I mean, if you had to look 3 years in the future, like what do you think lies ahead for AI and customer success. And you mentioned that, you know, now, you know, AI is very focused to your point on patterns and that type of more rudimentary types of tokens. But what does that look like as we continue to move forward on the evolution of this path? I think it's, I think it's gonna dramatically change what teams look like, not just CS teams, but teams overall. I think it's gonna change how we execute the jobs that we do. I think it's gonna change the skillset required to be someone in tech, especially in like B2B SaaS customer success, right? We've sort of primarily been focused on this idea of relationship builder and someone who can make the customers happy, and CS has shied away from things like revenue responsibility or targets or being too technical in the product. I think that the CSM of the future is going to need to be AI literate, and they're gonna have to have business and commercial acumen. They're gonna have to have, you know, technical expertise on the product because those are the things that we need to like inject that human differentiator at the right time, right? Anything that's repeatable or mundane, um, or admin overhead, AI is gonna take all of that. And so what excites me the most about where we're going is You know, I've been saying forever as a CS practitioner and leader, it's not about our product, right? The product is the vehicle that a customer uses to get to a destination, and that's their out— their desired outcome or the result. And that's why, you know, in Boston, I talked about this shift at Gainsight from SaaS, software as a service, to RaaS, retention as a service, because the move really needs to be We're driving an outcome and not, we're driving adoption of our product, right? Or we're driving you being happy because we chit-chat and we're nice at the top of a, a regular check-in call. They need to be able to be strategic and leverage AI and systems to really ensure that they're moving that customer toward that desired outcome and tying those results back to business metrics internally, right? ROI for the customer, bottom line impact for our, for our business. That, yes, absolutely. I would agree with, I would agree with all of that. You know, the thing that I really, I think maybe it's just me, but I think that's something, you know, a lot of people who have been in customer success that are learn, wanting to learn more in tools, processes, AI, you know, in general. You know, where in your opinion are places to go look for continuous knowledge repositories, you know, ways to become and to grow our teams. Because from my perspective as a CS exec, like, when I bring someone in, when I hire someone onto a team, I want to say, hey, I want to set you up for success, right? And taking into account everything that you just said from a visionary standpoint, and I agree completely based on the research that I've done and what I've, you know, just honestly sort of hypothesized based upon that. It's like, how do we set people up to be successful and to thrive in this remarkable new world that, you know, AI's kind of setting us up to adapt and flex to? Yeah. I mean, I think, you know, where I would focus my energy are experts in the AI space and experts in the CS space with the caveat experts in the CS space who are embracing and being visionary about AI and how it will change. I think if you see someone parroting sort of traditional, uh, customer success, you know, metrics or playbooks or strategies today, probably not the right person. But aside from that, I think the beauty of where we are in today's world is however you as a human prefer to learn or consume data, there's an avenue for you to get up to speed on what's happening with AI and how it's changing CS. If you are a social media person on your phone, there's influencers talking about both of those things that you can follow. If you like newsletters or Substacks, there's people you can follow. If you like podcasters and podcasts, there's things like this and other podcasts that are like AI specific. You read news articles. Heck, you can even create a chat in a GPT or a Claude or something like that to give you sort of like what's the latest in AI news and technology advancements and, you know, what's the latest and what people are saying, how this stuff is gonna be used or how it will change AI. So I think the best thing to set yourself up for success is to get curious about these things. Seek out the, the people and the ways that you wanna learn. And just start consuming that and then asking yourself as you're going through your jobs to be done every day, you know, what of this that I'm doing do I have to do? Like, that makes me as the human the different. And what are things that I could automate that I find myself doing repetitively? Or what are things that I could maybe build an agent to do so that I can focus elsewhere, right? Like, basically start building into what you do as an employee, you know, where can I free up my time to be focusing more in front of customers on strategy and driving value and outcomes? I have to know, are you, do you like, are you a Google, do you like Google or are you more of a Claude or are you more of a ChatGPT? What's your, what's your LLM of choice or do you just use everything, whatever you feel like you need to architect or run through? I know it's completely different than when you're building an actual tool. But like just everyday person using and asking these types of questions, like which, what's your preference or do you not have one? That's completely okay too. I think, you know, to your point, it depends on what I'm doing. If I'm doing, um, you know, vibe coding or building, uh, or a bunch of deep data analysis, I like to use Claude. If I'm just doing some general, you know, uh, kind of thinking partnership with, with a GPT, I'll use ChatGPT. Yeah, I, I think at this point they're all getting better and better, so you can't go wrong. And it just depends on personal preference and, you know, got it. Yeah, I, I think you can't go wrong. So tell us, we talk a lot about positive rebellion on the show, right? Obviously. I mean, everything that you're saying and sharing is honestly innovation that's moving us all forward in terms of how we think about what we do. And the thing that I love about your journey, honestly, Kate, is that you've seen an evolution of that journey. You've seen what that journey, what the more stale metrics, I'll put it that way, looks like. It's, you know, you've seen this thing play out. And so, I mean, when you think about rebel, a rebel of SaaS, like what does that mean to you? I think, you know, my first response is just being a woman in STEM. I think is a positive rebellion. You know, I've been in tech since 2011, and I can probably count on one hand the number of times in a meeting that I wasn't the only woman. And so I think there is an aspect of, you know, having a seat at the table, being a voice in the community for change that is positive. So that's kind of my first answer. And then I think around AI specifically is, you know, just being a voice in the industry that demystifies AI. There's so many buzzwords. There's just so much going on right now about AI and people freaking out and fearmongering. Absolutely. Yeah. Yes. All the time. Yeah. And also rightfully so, you know, some of the other impacts of AI, like environmental and like, you know, ethical and, and just being someone who's using AI responsibly and changing the way that we do business for the better. Because AI, like any other tool, can be used for really good or really bad, and it's up to us, the people using it, to, you know, decide which direction we're going. And so I think just being a, a positive, you know, voice in that it's, it's here whether we want it to be or not. And so it's up to us to shape what that looks Absolutely. Absolutely. Well, I don't know how, cuz we've been having so much fun, but alas, our time has come to a close. So at least for this episode. So how do we find more about you, Kate? You can find me on LinkedIn, Kate Neal. I'm out there. Kate Neal. She's, we're, you know, she's out there. So it's a good thing I'm okay at customer success. The jokes get not better at all, Kate. Rebels, thank you so much. And Kate, thank you so much. For coming on and sharing. I mean, from a perspective, it's invaluable to learn from someone who's been living AI strategy in real time, in real life, shaping what that looks like globally. So can't say enough. Thanks for hopping on with us to chat. Of course. Thank you so much for having me. All right, Rebels, until next time, do it big and do it different and go play with some AI. All right. Bye for now.