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Hello everyone. I'm your host, Anika Zuber, and welcome back to the next episode 

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of the Customer Success Pro Podcast. I am a customer success executive leader award-winning, 

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CS strategists, CS coach, and customer success fanatic. I help CS leaders and CSMs 

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build and scale world-class CS processes and teams. I'm a strong believer that customer 

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success is not a destination, but a journey, and it can be a tough journey. But don't 

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worry, I'm here to help. This podcast was created to help make your CS journey a 

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little bit easier to navigate. Join me every month on this podcast where we will 

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dive into the hottest topics in cs, the newest strategies and the best practices 

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in customer success, so you can make your CS journey a little bit easier. Make sure 

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you subscribe on Apple Podcast, Spotify, or wherever you listen to your podcast so 

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that you can continue to learn on this CS journey that we are on together. 

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Welcome back. Today I'm speaking with Alex Turkovich, who is the global Director 

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of Digital Customer Success at Flexera. Alex has over 15 years of experience in customer 

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success, leadership, professional services, and customer education. As the global 

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Director of Digital customer success at flexera, he combines digital and scaled customer 

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success strategies with customer education programs to ensure optimal customer lifecycle 

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engagement and outcomes. Alex is also the host of the CX podcast where he explores 

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different aspects of building and maintaining world-class digital customer success 

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experiences with guests who are infield and executing. Today, we will be chatting 

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with Alex all about AI in customer success. There has been plenty of chat about the 

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importance of how you should be using AI and SaaS and customer success, but how are 

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you doing this? How can you use AI every single day as a CSM to help you save time 

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and provide more value to your customers? That's exactly what we are gonna talk about 

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to Alex today, so stay tuned to get tips, tricks, and best practices on how you can 

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use AI daily in customer success. 

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Thank you for listening to this month's episode of the Customer Success Pro podcast. 

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This is just a quick reminder that this episode is brought to you by our sponsor, 

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CSM RevUp Academy is my complete step-by-step coaching program that helps you elevate 

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customer success pro.com/csm, rev Upen Academy, or by checking out the link in the 

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show notes of this podcast. 

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So, welcome, Alex to the podcast. I am so very excited to have you on this podcast 

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with us today. But before we get into probably the hottest topic in customer success 

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of the year, can you please tell my listeners a little bit more about yourself, how 

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you started at Flexera, what your role is, what are you up to? Give us your elevator 

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pitch. Sure. 

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well, first off, thank you for having me. It's awesome to be here. 

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I've been a fan of yours for a while and the show, and it's, it was awesome having 

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you on my show, and now I get to be on your show, so that's super cool. 

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yeah, Alex Turkovich, I've been in post-sale kind of education and post-sale for, 

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gosh, almost two decades. It's kind of scary to think about, but, 

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last few years really, I've been just laser focused on digital customer success specifically. 

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So that's been my role at Snow Software now of flexera because there was an acquisition 

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that happened there, 

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super fun. And, 

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yeah, I've been host of the Digital CX podcast for, gosh, year and a half, year and 

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year and change. And, 

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that's been just super fun talking to, you know, in various industry leaders such 

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as yourself about 

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digital and, 

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you know, by extension that has naturally, 

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kind of extended into topic of today, which is really around artificial intelligence. 

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But, uh, yeah, 

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that's been my focus for the last four or five years is just digital, digital, digital. 

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Yeah. Love it. I love it. You push out so much digital, digital CX content. I think 

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it's critical in the world that we find ourselves in where everyone's just trying 

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to do more scale, more do everything and anything in customer success when we can't 

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always do it all. So yeah, I do love all the content, especially the podcast that 

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you're putting out there, and it was an honor to be on your show. So it's, it's really 

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exciting to have you on this one now, for sure. 

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but give us a bigger picture understanding about your team, about flexera. What are 

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the types of customers you're dealing with? What are, 

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is it all digital customer success that you're looking after? Give us a little bit 

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of a bigger picture here. 

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Yeah. 

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so we, 

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you know, we look after, 

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you know, okay, I hate to say that digital is a segment because the, we, we all collectively 

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have been saying, oh, digital is not a segment, but it's a strategy. 

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but at the end of the day, there is still a segmentation element of digital, right? 

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So I do look over global digital cs, which means inherently we do provide some CS 

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coverage for the six, 7,000 accounts roughly that don't have an assigned CSM. So 

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it's a, it's a pretty big account set. 

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and so we do that by way of a team that we've kind of split into two. We have a, 

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you know, a customer facing team. A lot of people would call it a scaled team or 

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a digital CS team or whatever. 

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but I have a, a team of, of folks that are customer facing that 

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reach out to customers at certain times, whether it be time-based or whether it be 

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off of certain triggers or events that happen along the customer's journey. 

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they also do things like, 

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respond to survey, uh, responses, 

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because I'm a massive advocate for if you 

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put out a survey, you better respond when somebody, uh, submits a survey response. 

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definitely. So that's kind of like the customer facing side. And then I've got the 

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programs team, which are focused literally on 

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building and testing and iterating upon all of the digital motions that we have in 

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place or want to put in place. 

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so that's, you know, program manager and a, an analyst to look after data hygiene 

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and all those kinds of, 

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fun little logistics things you could say. Really. I mean, we don't have a CS ops 

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function, so we've been kind of taking on 

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a, a, a big portion of what you might call CS ops as part of that function as well. 

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Awesome. And I think that you just mentioned you really started to focus on digital 

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customer success in your personal brand and everything you put out on LinkedIn and 

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the podcast about a year and a half ago. Is that when you also made the shift professionally 

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or what really inspired you to start in digital customer success or really shift 

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kind of your focus? Was it the role, the company? Where did this all come from? Yeah, 

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a great question. So I spent a lot of time in, 

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professional services and onboarding and just kind of post-sale leadership. And in 

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all of those roles, I always looked for ways, uh, that we could automate to make 

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things easier. 

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my goal has kind of always been to make sure that the, the, the people we have on 

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the team are operating efficiently and that they're engaging in 

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conversations where it really drives value versus just like doing the mundane things 

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over and over again, right? So I've always kind of had this eye towards automation. 

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and about three and a half, four years ago, 

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this opportunity came up at Snow Software to build their digital CS function. 

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And so, uh, I jumped at the chance, had jumped in with both feet and started building 

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a team and started building processes and really injecting, uh, digital CS into a, 

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you know, roughly 20-year-old company already that has been, had been very used to, 

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you know, operating, 

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uh, enterprise level one-on-one kind of, uh, high touch relationships. And so interjecting 

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some of that stuff into, into the business was a really, you know, valuable experience 

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and, and really made me, 

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uh, realize that, hey, this is an intersection of all the things that you love 

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about, 

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know, post-sale. Uh, 'cause I'm massively into enablement and, and education and 

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those kinds of things. I love 

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figuring out how systems work together and data and all that kind of stuff. So it 

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was just a perfect marriage and, and, 

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at some point, 

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you know, I was having these conversations with other leaders around the, uh, 

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around the CS industry about this stuff because, you know, for self-education purposes. 

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And I was like, why? How, why am I not recording this stuff and why am I not putting 

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it out there? And so that just kind of organically led to, hey, let's start a podcast. 

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And, 

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you know, one thing led to another, and here I am year and a half later and 65 episodes 

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later. So 

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I love it. I love it. I still remember when you put out your first episode, I was 

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like, that's cool. Way to niche down in customer success. There's tons of podcasts 

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and content out there, so I absolutely love that you went in that direction. But 

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from the outside, it looks like you've definitely nailed a niche. And when it comes 

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to digital cx, you've ended up in a career trajectory where you're director of customer 

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success. I really love asking all my guests like, 

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what is next in your CS career? What's your next dream or ambition, or the next step 

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really for you now that you've kind of nailed this part of customer success, both 

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personally, but also professionally? That's a great question, and I wish I knew the 

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answer. 

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no, 

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seriously though. Like, 

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I, I've, I've really gotten a lot of value 

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and grati, you know, grad 

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gratification, satisfaction, satisfaction. I was trying to, satisfaction, that's 

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a new word. We're gonna coin that. Yeah, 

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we're gonna, we're gonna go with that. 

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just helping 

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other leaders and organizations, 

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increase their digital maturity, right? I've, I've, that's been a, over the last 

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year and a half that has probably been like the number one source of, 

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personal fulfillment is just helping other organizations do that. And so, 

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I think what the future looks like for me over the next few years is, is, is, 

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continuing to help organizations evolve that way. 

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probably through some, some consultancy, but also, 

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whether it be, 

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W2 or independently as a consultant, 

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that's, that's gonna be the focus. 

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I have con, 

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consciously as part of my career, 

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I wanted to collect experiences. I didn't wanna just niche down into one area, which 

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is why I spent a lot of time in professional services and onboarding and education 

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and those kinds of things. 

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and so, you know, for me ultimately, you know, whether that turns into, you know, 

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c-level, chief operating officer, chief customer officer kind of stuff, or, 

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know, whether I continue down that path of, of building the independent, uh, that 

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that independent consultancy remains to be seen. But that's, that's kind of the crossroad, 

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but that's exciting though. I feel like that's a lot of options and I love seeing 

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a lot of CS leaders kind of branch out. You just mentioned like chief operating officer 

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as well as Chief Customer Officer. I think that there's a lot of directions, customer 

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success is going into the future, and it's really cool to see a lot of what I call 

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my LinkedIn friends, explore different avenues, but also take on different Sure. 

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I guess, challenges and, and really start to sharpen their skillset when it comes 

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to new things within customer success, whether it's consultancy, whether it's niching 

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down into a specific vertical, which I like yourself, have done a million different 

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things, right? 

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I feel like I definitely have it niche, but it's, it's really cool to see customer 

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success evolve over the, the last few years and also looking into the future, like 

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what it can mean for a CS professional as next steps. So thanks for sharing that, 

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even though it's not exact direction, I really love hearing everyone's personal next 

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steps. Yeah, I started car myself a professional mutt, but that didn't really go 

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over too well, so I'm just going with, you know, whatever. 

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No, but I mean a mu I think that's a dog who does build dogs. I'm like, you could 

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spin it in a positive way. I, I could. Yeah. 

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Awesome. Well, listen, I love the backstory, but I think we should get into our topic, 

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which I know you've discussed on your podcast where you've talked about like an AI 

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primer episode. And for anyone listening, I'll make sure to link it down in the show 

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notes if you are interested in, in listening to that 

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episode, Alex has done, but AI has become the hottest, most talked about topic in 

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SaaS in general in the world, and now specifically in customer success. I think a 

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lot of CS professionals know they should be using AI in their CS strategy, but it's 

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still at a loss of how we should do it. How does it play a role in your day-to-Day? 

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And that is exactly what I wanna talk to you about today, Alex, is really 

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the practical use of AI every single day in customer success. So just to warmer selves 

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up into this topic, can you just give us an overview of like how you used AI in customer 

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success, or how you started to use it in your day to day, either in operations and 

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customer success? Just give us a, a starting point here of what you're using AI for. 

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Yeah, it was brute force. I mean, look, I'm in my mid forties, 

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and, uh, you know, the, if when you're in your forties or fifties, it doesn't, 

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you know, adopting new technology, I don't think is, is is 

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the first go-to, like, you just kind of wanna operate as you have been operating. 

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And so for me, it was really like a matter of, you know, ripping the bandaid off 

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and just trying to pull 

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AI into my everyday work stream, no matter kind of what I was doing. And I can give 

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you some, some tactical examples. First off, 

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I've just been trying to educate myself as much as possible on what is currently 

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happening in, in artificial intelligence, just because I feel like, 

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you know, with, with the podcast, I have a little bit of a, 

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uh, you know, it's part of the job description to stay on top of this stuff, right? 

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And to, to share some of those findings and whatnot. 

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but my first stop was just, you know, chat, JPT, uh, you know, uh, the, the, the 

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pro license. 

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and, and using it in a, on a almost daily basis. In fact, 

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I, I've started recently using it on my walks, 

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which, ooh, doesn't, doesn't sound, how are you using it while walking, right? So, 

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this all came about very recently because, you know, these things iterate so quickly. 

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So, so Che or Open AI came out with, 

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GPT-4 oh, which 

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on the mobile app allows you to literally have a conversation with it. 

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and so I'll go on walks and if I'm stuck on something either professionally or in 

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my business or whatever it may be, 

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I'll have a conversation with it about, you know, what it is I'm stuck on, 

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and describe, you know, all the different details of why I am stuck on it, and it'll 

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help me unstick things. So a lot of times, you know, a 10, 15 minute conversation 

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with chat GPT will kind of open up my eyes to some things that I may have been, you 

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know, missing or thinking about in the wrong way. And then when I get back, you know, 

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to come to, to my computer or whatever, I'll have the entire transcript of that whole 

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chat and I'll just, you know, plug and play. I've used it to, 

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you know, create some outlines of shows and, and, you know, uh, kind of draft articles 

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and like, uh, really isn't as, as a personal assistant almost like, and it, it does 

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make me look a little bit crazy. I, I guess not, I mean, it just looks like I'm on 

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the phone. No, everyone's talking to their phones these days. That doesn't sound 

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crazy at all. I guess not. But, 

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I literally use it that way on a daily basis as kind of my personal companion to 

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just help me think through things and get things done. And so personally that has, 

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that has really helped me. 

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I would say the other thing I would add is from more of a CS speci, uh, specific, 

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angle, I use perplexity a ton, 

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for research, 

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because it's essentially, 

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I've almost replaced Google, quite frankly, with perplexity in terms of my searching, 

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because it is, it is, they call themselves a research engine, which is true. I mean, 

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it, it does, you know, give you a nice summary of whatever it is you're searching 

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for, but then also gives you the sources. 

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and I know Google's trying to do that a little bit, but it's still, I mean, Google 

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searches feel cumbersome in comparison. So, 

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I'll do account research for instance. 

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a lot of times when I'm, you know, engaging with an account I didn't know before 

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or don't have any context about, I'll do a, a quick perplexity search 

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on that company and try to get, you know, the, the history and the financials and 

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who the board is and who the investors are and all that kind of stuff. And a lot 

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of times I find myself 

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almost being more educated about that particular company than my points of contact 

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there are. So I use it for interesting things like that just to, just, again, that, 

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that kind of copilot mentality is what I take. 

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I love that there's so much in there that I can just ask so many follow up questions 

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to, but I have never thought about talking to chat bt while walking. I definitely 

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have like a list of prompts and a list of like copy paste of things that I always 

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ask AI to do for me or help me out, like you said, researching customer things or 

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I really have replaced Google with chat GBT, and I thought I was cool with the short, 

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but, uh, like a short key on my, on my keyboard to talk to chat GBT, because now 

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there's like a Mac, 

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app as well for it. So I use that. But you've just given me a great new idea about 

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talking to my phone and basically having a conversation with ai, which is, could 

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be quite scary for a lot of people who are intimidated by it. 

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Yeah, I mean, you know, the, the comparisons when that, when that first released, 

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everybody was making the comparison to her the movie and for right, for the right 

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reason, because it is basically that experience. It's, it's really bizarre. And the, 

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the, what they've done in terms of, 

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the nuances of tone of voice and things like that, it gets sarcasm, for instance, 

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which just blows my mind. And so, 

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you know, it, it is, it is very bizarre. It's a bizarre experience and, and it takes 

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some getting used to, but once you're in there, it's, it's pretty neat. 

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Definitely. I think it's really cool that we're living out all of these future movies 

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that we watch. Like if you think like Her or iBot or like any of these things that 

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I think are like these dystopian future about 

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like robots running our world, I sometimes think like it's kind of cool that we get 

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to actually try this. I know it's intimidating and scary, but hopefully everyone 

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listening to this podcast, you get some ideas to actually take back and apply and 

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realize that these are things that people are using every single day in customer 

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success. And it's really helping us open even more avenues of success for our companies 

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and our customers and for ourselves personally, which I think is the big part of 

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AI that's probably not being unlocked by most people. And you mentioned that you 

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had to go through kind of brute force, Alex, to basically start using it, which is 

295
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something you did personally, but what about with your team? I know you're using 

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it within, 

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Flexera. I know you guys are starting to use it a lot more in your digital strategies 

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as well. How did you motivate CSMs or your team to actually start integrating AI 

299
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into their day-to-Day? Yeah, I mean, part of it is, it's, it's an ongoing thing, 

300
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right? Some are using it actively, some aren't at all. 

301
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and that's kind of personal preference, obviously. I just, I just share my experiences 

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and my own personal experiences and, and the benefits that I get from it, uh, with 

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the team on a regular basis, which just naturally kind of builds curiosity and whatnot. 

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you know, the, I mean the, the other element of AI is that all these, 

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all these companies, uh, these vendors that we're using are starting to integrate 

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AI in 

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some more, some less meaningful ways into various platforms. And so 

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I think, I think, um, you know, part of using it is also just taking advantage of 

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all the, the platforms that are building AI copilot and tools in, you know, into 

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the, uh, into their ecosystems as well. 

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I think it's, for me, what it comes down to is just 

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just being comfortable with the technology. 

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I, I listen to Gary, Gary Vaynerchuk quite a bit, and one of the things that he has 

314
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always kind of harped on 

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specific to social media, but it applies to ai, is that, you know, you may not be 

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interested in using TikTok. You may not be interested in whatever, but if you learn 

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how to use it and if you learn how to post on it, guess what? The next platform that's 

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coming is gonna be based on best practices that TikTok was using, right? And I think 

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the same thing applies for artificial intelligence, right? Right now is the time 

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to start playing and to explore and to, to, to break stuff using AI and to just really 

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get comfortable with 

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prompting and, and prompting with high quality and, and really flexing those muscles 

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because all of, you know, this is the way it's going, whether we like it or not. 

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And so by ha, by building that kind of, that knowledge base and that that muscle 

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now will just help you going, going forward. And, and this, I think this is especially 

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true for job seekers at the moment. 

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Like if, you know, there's, there's a lot of folks on the market, and if, if you 

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can spend some time now to really get deep into artificial intelligence, guarantee 

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you that's gonna look great on your resume. 

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So, but also it's gonna be such an edge for you as a CSM or a CS Pro or leader coming 

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in, having AI like skillset or just any sort of ideas on how to implement AI into 

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a customer success strategy or an overall business growth strategy is just, that 

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is a skill that I think any employer would wanna see and hear about. So I think it's 

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just, it's beneficial, like you said, to get on the education bus or whatever you 

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wanna call it, and really understand how AI can help you every single day and not 

336
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shy away from it. And I think most CS professionals aren't shying away from tools 

337
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or technology. We all live in technology where we literally work as professionals 

338
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trying to enable our customers to use our product. So why shouldn't we start using 

339
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a product or tool that's mostly free? That's the great part. Yeah. Right. Yes, we 

340
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can pay for chat GBT, but most of the AI tools out there are, are free. And like, 

341
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I just wanted to dive in a little bit deeper into, you mentioned a few tools already 

342
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that you're using. 

343
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Are there any other common AI tools or, or apps or platforms that you're using daily, 

344
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and how are you using them to help yourself as a busy CS pro? Yeah, 

345
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I mentioned perplexity that I'm just using the snot out of perplexity. I think it, 

346
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it does a really good job of just summarizing the internet, 

347
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you know, whereas chat, DPT kind of doesn't do, you know, it's, it's, it's still 

348
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a little bit limited 

349
00:24:03,140 --> 00:24:04,150
in terms of that. 

350
00:24:04,170 --> 00:24:08,540
another tool that I have grown really fond of is, 

351
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is, well, two of them actually, you know, 

352
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gamma is, is quite known for building PowerPoint decks. Yeah. 

353
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so that's been really cool because if, if I want to go build a, 

354
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I don't know, a, a, a business case or a proposal or a pitch deck or something like 

355
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that, like whatever it is I want to build, 

356
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I'll, I'll spend, you know, two or three minutes coming up with a really solid prompt 

357
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and it'll spit out a 20 page deck that gives me probably 80% of what I need in terms 

358
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of structure and verbiage and basics, you know, to to, to really build out a, a solid 

359
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PowerPoint deck that probably would've taken me days otherwise, you know? 

360
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So, 

361
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that's one. There's others that are, I'm drawing a blank on, but I'm sure I'll get 

362
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to them at some at some point during this conversation. That's all good. All good. 

363
00:25:01,960 --> 00:25:05,340
I think that some of the tools you're just mentioning, like, first of all, if you're 

364
00:25:05,340 --> 00:25:11,580
a CS Pro, that's like struggling time-wise. I know I use AI all the time from like 

365
00:25:11,580 --> 00:25:15,810
a, let me speed up a process, whether it's research about a customer, but you just 

366
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mentioned Gamma, which is a great tool to help you speed up making slides. I know 

367
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we make so many slides in customer success, why not have some other tool make it 

368
00:25:25,920 --> 00:25:30,960
visually more appealing Yeah. Or attractive to you because listen, I'm not a designer 

369
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here. I don't know how to design slides. I can put something together, but I'm sure 

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AI can do it better for me. Yeah. And the same thing goes for managing my calendar 

371
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or again, responding back to customers or creating, creating 

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with how I engage with customers. All of these are just, again, prompts, things you 

373
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start using every single day as a CS professional. And I think you mentioned it earlier, 

374
00:25:53,770 --> 00:25:59,590
it's really how you prompt these tools and how you're able to engage with ai. Yeah. 

375
00:25:59,620 --> 00:26:02,470
That really makes a difference day to day. And 

376
00:26:03,610 --> 00:26:07,270
like, I just wanted to ask, are there any prompts that you're using? How do you save 

377
00:26:07,270 --> 00:26:11,440
your prompts? How do you even go about creating a prompt, maybe explaining that might 

378
00:26:11,440 --> 00:26:13,330
be a good place to start? Sure. 

379
00:26:13,930 --> 00:26:19,750
there is, you know, I used to keep a kind of a doc of saved prompts, you know, 

380
00:26:19,770 --> 00:26:24,480
and sometimes I still reference it, but now with chat GPT, you can actually save, 

381
00:26:25,830 --> 00:26:25,800
um, 

382
00:26:26,450 --> 00:26:31,320
save some critical information into chat GPT itself so that it can reference it in 

383
00:26:31,320 --> 00:26:31,860
the future. 

384
00:26:32,550 --> 00:26:34,410
so I'll give you a practical example. 

385
00:26:35,600 --> 00:26:35,450
uh, 

386
00:26:36,270 --> 00:26:37,580
I used to ask, 

387
00:26:37,590 --> 00:26:43,850
chat GPT all the time to use a specific tone of voice that kind of, uh, you know, 

388
00:26:43,850 --> 00:26:49,400
I, I, I basically figured out what my tone of voice was in relation to TA chat GPT 

389
00:26:49,400 --> 00:26:50,120
by feeding it 

390
00:26:50,640 --> 00:26:54,380
like a bunch of my articles and, and transcripts and things like that. And it gave 

391
00:26:54,380 --> 00:26:58,790
me back like, these, these are the parameters you would use to prompt me, you know, 

392
00:26:58,790 --> 00:27:00,260
to sound like you. 

393
00:27:00,950 --> 00:27:04,360
and, and so I used to copy and paste that into like every single one of my prompts, 

394
00:27:04,360 --> 00:27:04,990
but now 

395
00:27:05,720 --> 00:27:10,450
I've just asked Che GPT to save that out for me, right? So you have this, you can 

396
00:27:10,450 --> 00:27:14,200
literally tell it, commit this to memory, and it'll commit these kinds of things 

397
00:27:14,200 --> 00:27:14,620
like, 

398
00:27:15,260 --> 00:27:19,780
you know, tone of voice or, you know, other bits of context about who you are, who 

399
00:27:19,780 --> 00:27:24,190
you work for, what you do, what your field of expertise is, and those kinds of things 

400
00:27:24,220 --> 00:27:28,960
into it so that you don't have to include that in your prompting every time. So that's, 

401
00:27:29,440 --> 00:27:32,770
that's kind of hot tip number one. The other thing that I use all the time, 

402
00:27:33,650 --> 00:27:37,630
and I've talked about a lot, is called the Risen framework, R-I-S-E-N. 

403
00:27:37,650 --> 00:27:43,810
which is a great framework for producing really detailed prompts. And, and that's 

404
00:27:43,810 --> 00:27:44,590
the key, right? 

405
00:27:45,190 --> 00:27:48,820
asking chat JPT to create an article about, 

406
00:27:49,800 --> 00:27:51,850
I don't know, electric guitars 

407
00:27:52,970 --> 00:27:57,820
that's going to get you one result, right? But, and a very basic result of that and 

408
00:27:57,820 --> 00:28:01,180
very basic result. It's not gonna be anything that you want. And a lot of people 

409
00:28:01,180 --> 00:28:04,690
stop there. They're like, okay, the novelty of it, right? Right. I mean, an article 

410
00:28:04,690 --> 00:28:08,200
about electric guitars, okay, that's great. It's not really, you know, relevant or 

411
00:28:08,290 --> 00:28:11,110
great or whatever, so I'm just gonna go and write it myself. 

412
00:28:11,620 --> 00:28:12,600
but, 

413
00:28:12,900 --> 00:28:17,420
with frameworks like Risen, there's a few others out there as well. You can give, 

414
00:28:17,420 --> 00:28:21,500
GPT the, the details that it needs to create a high quality, 

415
00:28:22,670 --> 00:28:26,580
you know, bit of work that is aligned with what your expectations are, right? 

416
00:28:27,430 --> 00:28:34,980
So risen, of course, it's an acronym, uh, r stands for role. So you're going to literally 

417
00:28:34,980 --> 00:28:40,990
tell it like what is the role you want it to assume. So, uh, in the case of the electric 

418
00:28:40,990 --> 00:28:41,590
guitar, 

419
00:28:41,610 --> 00:28:42,620
you might say, 

420
00:28:42,620 --> 00:28:49,310
your role is to play the an an, an expert in musical instruments. You've, you've 

421
00:28:49,460 --> 00:28:53,660
been in music for, you know, a couple of decades and you know everything about, you 

422
00:28:53,660 --> 00:28:58,460
know, specifically, you know, instruments used in whatever you like. You can get 

423
00:28:58,460 --> 00:29:02,300
really like nitty gritty with what the role it is you want it to play. But that's 

424
00:29:02,300 --> 00:29:03,740
key because 

425
00:29:04,470 --> 00:29:09,200
it may sound kind of weird to do that, but what it does is it tells, it tells the 

426
00:29:09,200 --> 00:29:13,280
model to ignore everything else that it knows. Yeah. Which is huge 

427
00:29:13,850 --> 00:29:17,900
by ignoring all this other stuff you can get it to focus in and not, not feed you 

428
00:29:17,900 --> 00:29:19,160
with a bunch of weird stuff. 

429
00:29:20,910 --> 00:29:23,510
The next, so r and then instruction is, I, 

430
00:29:24,280 --> 00:29:27,650
that's where you're telling it, okay, I want you to write me this article, or, and, 

431
00:29:27,790 --> 00:29:32,330
and, and those kinds of things. Then you tell, it steps you, you literally, so that's 

432
00:29:32,330 --> 00:29:36,800
s you, you literally tell it, okay, I want an intro paragraph, I want three bullet 

433
00:29:36,800 --> 00:29:38,540
points and sub subheadings, 

434
00:29:39,110 --> 00:29:44,390
you know, A, a closing whatever, and a and a call to action. Like you can tell it 

435
00:29:44,390 --> 00:29:50,930
exactly how you want it structured you. Then the E is the end goal. So you, whether 

436
00:29:50,930 --> 00:29:54,140
you want to drive like website conversions, whether you just want to be informative, 

437
00:29:54,140 --> 00:29:58,730
whether you want to, you know, lead to something else, you can literally tell it 

438
00:29:58,730 --> 00:30:02,810
what is the goal of this, this piece. And then lastly, N is narrowing, 

439
00:30:03,860 --> 00:30:06,920
which I think is also critical. You want to tell it not to like 

440
00:30:07,830 --> 00:30:10,790
do certain things like make stuff up, for instance. 'cause 

441
00:30:11,570 --> 00:30:16,430
uh, chat GPT is great at making stuff up if it doesn't know, right? And so 

442
00:30:16,880 --> 00:30:17,310
you wanna narrow 

443
00:30:17,340 --> 00:30:17,790
the answer, 

444
00:30:18,680 --> 00:30:22,810
it isn't always the answer. So you want to narrow it down to like, you know, being 

445
00:30:22,810 --> 00:30:28,360
very, very specific and only give you factual information that's related to this 

446
00:30:28,360 --> 00:30:33,130
one thing. So that's a quick, quick and dirty run through of Risen, which I use all 

447
00:30:33,130 --> 00:30:33,640
the time 

448
00:30:34,130 --> 00:30:35,650
and it's, yeah, served me quite well. 

449
00:30:36,260 --> 00:30:41,770
Definitely. I think that having a prompt like that and then making sure you are telling 

450
00:30:42,300 --> 00:30:47,110
the tool what to do, because I think a lot of people, like you said, use the novelty 

451
00:30:47,110 --> 00:30:51,670
of chat GBT, like write me a poem about electric guitars or something like, you know, 

452
00:30:51,670 --> 00:30:55,600
the fun stuff that we originally did when we started using AI a little over a year 

453
00:30:55,600 --> 00:31:01,210
ago now. But like you said, it is super powerful when you tell it to do something 

454
00:31:01,210 --> 00:31:06,250
very specific. If you think about even any sort of assistant, we kept referring back 

455
00:31:06,250 --> 00:31:10,390
to it being your, your assistant. Really your assistant's only gonna know what to 

456
00:31:10,390 --> 00:31:16,990
do when you tell your assistant exactly what to do. And again, AI is only as smart 

457
00:31:17,820 --> 00:31:21,850
as the prompts you put into it. And I think that that is just, there's one thing 

458
00:31:22,20 --> 00:31:27,490
anyone takes away from this episode. I would say just get really, really comfortable 

459
00:31:27,490 --> 00:31:32,150
with what you're prompting it to do. Like really work out much like Alex already 

460
00:31:32,150 --> 00:31:37,790
said, tone of voice. What is it you wanted to do? Is it an article, is it short form, 

461
00:31:37,790 --> 00:31:43,700
is it an email? Also formality also even like for example, I use British English 

462
00:31:43,700 --> 00:31:48,950
versus American English for example. Like get as specific as you can with your prompts 

463
00:31:48,950 --> 00:31:53,360
because I think those little bits are what makes AI 

464
00:31:53,970 --> 00:31:58,730
easier for you down the line. If you're just using very generic prompts, you're gonna 

465
00:31:58,730 --> 00:32:02,420
get very generic answers and then you're gonna give up on using it. 'cause it just 

466
00:32:02,420 --> 00:32:06,920
sounds like a robot because you're not telling it to assume a role like you said, 

467
00:32:06,920 --> 00:32:10,720
or not lie or whatever else you need to prompt it to do 

468
00:32:10,720 --> 00:32:11,450
so. Yeah, yeah. Exactly. 

469
00:32:12,160 --> 00:32:15,680
Definitely. So we talked a little bit about prompts. We talked a little bit how you 

470
00:32:15,680 --> 00:32:21,920
personally use ai. Let's talk about how AI can be used to streamline customer success 

471
00:32:21,920 --> 00:32:26,300
processes, specifically digital. 'cause I think a lot of people listening, a lot 

472
00:32:26,300 --> 00:32:30,410
of people that know, you know, that you work in scaled or digital customer success. 

473
00:32:30,440 --> 00:32:37,100
And I can see how the, not the easiest, but probably the, the first place AI comes 

474
00:32:37,100 --> 00:32:42,440
into customer success isn't digital or scaled models. 'cause it's like, cool, it's 

475
00:32:42,440 --> 00:32:47,300
a tech touch model, let's get something tech to help us out. Has there been some 

476
00:32:47,300 --> 00:32:52,790
sort of strategy or process you've put in place that, that you used AI to do so? 

477
00:32:53,430 --> 00:32:53,990
Yeah, 

478
00:32:54,50 --> 00:32:56,700
it's a, it's, it's a tough one for us because, 

479
00:32:56,700 --> 00:33:01,300
you know, the one thing that we have been struggling with that I've probably learned 

480
00:33:01,300 --> 00:33:02,110
the most, 

481
00:33:02,810 --> 00:33:06,520
uh, from over the last year or so is just 

482
00:33:07,850 --> 00:33:09,160
data quality. You know, 

483
00:33:10,350 --> 00:33:14,900
there, there's a lot of promise in artificial intelligence and using it and there's, 

484
00:33:14,960 --> 00:33:19,760
you know, uh, any number of chatbots out there and there's cool tools that'll analyze 

485
00:33:19,760 --> 00:33:23,210
your support ticket volume and suggest knowledge base articles and like all that 

486
00:33:23,210 --> 00:33:24,560
kind of stuff, right? 

487
00:33:25,100 --> 00:33:26,330
The problem with it is, is like 

488
00:33:26,830 --> 00:33:29,660
if you plop it on top of some, 

489
00:33:30,300 --> 00:33:35,690
some kind of ratty or poor poor data, you're going to get poor results out of it, 

490
00:33:35,900 --> 00:33:37,280
just like anything, right? 

491
00:33:37,810 --> 00:33:38,780
And so, 

492
00:33:39,650 --> 00:33:43,540
you know, the, the, the probably the thing, the number one thing that I've learned, 

493
00:33:44,250 --> 00:33:45,70
is that, 

494
00:33:45,890 --> 00:33:51,560
you know, you really want to make sure that whatever you're, whatever you're training 

495
00:33:51,920 --> 00:33:55,430
your GPT or your, you know, your AI tooling on, 

496
00:33:55,430 --> 00:34:00,740
is is built on a solid foundation of solid data that you know is right 

497
00:34:01,230 --> 00:34:06,290
and you know, is accurate. And, and it doesn't have to be like, you know, squeaky, 

498
00:34:06,290 --> 00:34:09,800
squeaky clean. 'cause I've never seen a squeaky clean customer data. Yeah. I about 

499
00:34:09,800 --> 00:34:13,850
to say, when is that ever the case? That doesn't exist, right? That doesn't exist. 

500
00:34:13,850 --> 00:34:17,180
But there, there needs to be kind of a baseline level there. 

501
00:34:17,180 --> 00:34:18,200
one example, 

502
00:34:18,220 --> 00:34:23,720
of, you know, in fact how, how it didn't quite work well, well, for us is, 

503
00:34:23,560 --> 00:34:25,920
know, the, the CSP we're using has, 

504
00:34:25,930 --> 00:34:32,370
a a, a lovely account summary feature that, you know, that that gives us like a nice 

505
00:34:32,300 --> 00:34:37,700
run through of not only you know, who the company is and what they're all about and 

506
00:34:37,700 --> 00:34:41,370
what their industry is and those kinds of things. But then it also runs through, 

507
00:34:41,370 --> 00:34:45,230
okay, this is, this is their history with you as an account. These are the recent, 

508
00:34:45,780 --> 00:34:46,250
you know, 

509
00:34:46,280 --> 00:34:51,570
updates and tickets and sentiments and those kinds of things. And, and it's, it, 

510
00:34:51,470 --> 00:34:53,420
it's a lovely, lovely thing, 

511
00:34:54,550 --> 00:35:00,740
but invariably if it's, if it's fed off of data that doesn't quite hit it on the 

512
00:35:00,740 --> 00:35:05,360
accuracy perspective, then it becomes just another thing that people start questioning, 

513
00:35:06,370 --> 00:35:06,890
you know? 

514
00:35:06,900 --> 00:35:11,610
and so that's the big thing to kind of avoid when you're looking at AI tooling, 

515
00:35:12,140 --> 00:35:17,220
and, and, and wanting to implement these things is like, are you ready for that? 

516
00:35:17,250 --> 00:35:21,720
And are you ready to put in the effort that it takes to get it up and running? 

517
00:35:22,450 --> 00:35:24,220
one brilliant thing that, 

518
00:35:24,250 --> 00:35:30,220
I think everyone who's exploring a customer facing chat bot should do is to test 

519
00:35:30,220 --> 00:35:31,420
it internally first. 

520
00:35:31,420 --> 00:35:36,840
and, and I've I've heard of, you know, I've heard several CS professionals that either 

521
00:35:36,840 --> 00:35:40,230
I've had on the show or had conversations with and, and something that we're, we're 

522
00:35:40,230 --> 00:35:41,370
doing right now is like, 

523
00:35:41,400 --> 00:35:45,930
you know, if if you're, if you're implementing this chat bot based on your, you know, 

524
00:35:45,930 --> 00:35:50,790
and, and it's, it's trained on your knowledge base and your community and your support 

525
00:35:50,790 --> 00:35:50,970
and all that kind of stuff, 

526
00:35:51,510 --> 00:35:56,340
test it internally with your internal team members and use it as a, as an internal 

527
00:35:56,340 --> 00:36:01,140
tool for people to get answers quickly to customer questions and those kinds of things. 

528
00:36:01,590 --> 00:36:06,350
Because that's, that's how you're gonna find out if it's, you know, directionally 

529
00:36:06,350 --> 00:36:10,320
correct or if it's not, and what you need to do to fix it before you open it up to 

530
00:36:10,320 --> 00:36:12,240
the masses and, and have it be a, 

531
00:36:12,900 --> 00:36:16,680
uh, kind of a publicly available thing. So I don't know if that answered your question, 

532
00:36:16,680 --> 00:36:20,450
but that's where my mind went, Isaac, like I feel like you gave some advice in there, 

533
00:36:20,450 --> 00:36:24,510
which is actually my next question, but you definitely summarized about clean data 

534
00:36:24,690 --> 00:36:29,370
or as clean as you can get it, because that's always tricky in any, any organization. 

535
00:36:29,370 --> 00:36:33,720
And then you also said like, testing internally before you go externally, is there 

536
00:36:33,720 --> 00:36:37,200
any other advice you would give? I think a lot of people listening to this episode 

537
00:36:37,200 --> 00:36:42,930
are probably thinking, how can we start implementing AI into our daily workflows? 

538
00:36:42,930 --> 00:36:48,240
Anything else, any pieces of advice or maybe any no GOs with AI in, in their workflow? 

539
00:36:48,540 --> 00:36:54,750
Well, the big no-go, the big no-go is if you are, if you are working with, 

540
00:36:54,770 --> 00:37:00,630
kind of publicly surfaced models, do not feed at any kind of proprietary information. 

541
00:37:00,630 --> 00:37:05,250
Please, please, please, please, please. 'cause basically you are giving your competitors 

542
00:37:05,250 --> 00:37:07,220
a bunch of proprietary information. 

543
00:37:07,550 --> 00:37:08,730
that said, 

544
00:37:08,750 --> 00:37:12,310
you, we made a very conscious effort early on, 

545
00:37:12,330 --> 00:37:14,940
you know, like a year or so ago to actually, 

546
00:37:14,960 --> 00:37:21,490
you know, feed chat, GBT, uh, a bunch of our kind of openly available information 

547
00:37:21,520 --> 00:37:25,660
and, and train it on who we were as a company and those kinds of things, which is 

548
00:37:25,660 --> 00:37:29,470
a very interesting thing to think about because anything you put in there, if it's 

549
00:37:29,470 --> 00:37:33,970
not within your kind of company environment, it's gonna go, you know, it, the model's 

550
00:37:33,970 --> 00:37:35,880
gonna be trained on it, right? Yep, 

551
00:37:36,480 --> 00:37:36,610
yep. 

552
00:37:36,620 --> 00:37:41,950
so I, I think that's probably the big no go and, and or at least, you know, go verify 

553
00:37:41,950 --> 00:37:46,40
that, um, whatever it is you're interacting with. I mean, if you're using like, 

554
00:37:46,890 --> 00:37:50,830
you know, Microsoft copilot or whatever it is, like you're, you're probably okay 

555
00:37:50,830 --> 00:37:56,260
because it's in your instance of Microsoft 365 or whatever. So just, you know, touch 

556
00:37:56,260 --> 00:37:59,620
base with your IT department and make sure that, you know, whatever it is you're 

557
00:37:59,620 --> 00:38:06,880
doing, um, is either secure or not secure and see what their AI policies are. Um, 

558
00:38:07,210 --> 00:38:12,550
aside from that though, um, my, I mean, look, my biggest advice, and it goes back 

559
00:38:12,550 --> 00:38:17,890
to what I was talking about earlier, is just start using it, you know, integrate 

560
00:38:17,890 --> 00:38:19,930
it into everything you're doing. 

561
00:38:19,960 --> 00:38:26,490
whether it's, you know, drafting newsletters, drafting articles, drafting emails 

562
00:38:26,490 --> 00:38:29,460
and those kinds of things. I'm not telling you to like 

563
00:38:29,570 --> 00:38:34,560
draft it, copy and paste it and stick it in your inbox or stick it in your, you know, 

564
00:38:34,590 --> 00:38:36,360
into your outlook to send to your customers. 

565
00:38:37,140 --> 00:38:38,560
but start using it, 

566
00:38:38,820 --> 00:38:43,690
in, in a way that you can, you know, have it basically consolidate a bunch of information 

567
00:38:43,690 --> 00:38:48,700
for you that you would've otherwise gone and, and had to search for in various different 

568
00:38:48,700 --> 00:38:49,330
places. 

569
00:38:49,920 --> 00:38:54,640
Pulling that into one place and then creating these kind of, whether it be email 

570
00:38:54,640 --> 00:39:00,340
templates or whatever out, uh, the therefore you, so that then you can spend, instead 

571
00:39:00,340 --> 00:39:03,790
of spending half an hour creating an email, you can spend five minutes polishing 

572
00:39:03,790 --> 00:39:05,170
what, what the output was. 

573
00:39:05,910 --> 00:39:10,990
Yeah. I also think that if I look back at it, I would love to have started to use 

574
00:39:10,990 --> 00:39:17,920
it earlier to validate or to even like, give me a cleanup of my ideas, I should say. 

575
00:39:17,920 --> 00:39:21,340
Yeah, totally. Like if you're building a customer health score or you're thinking 

576
00:39:21,340 --> 00:39:27,400
of building a digital success program or something that you're working on, just ask 

577
00:39:27,400 --> 00:39:33,850
it to give you ideas or ask it to refine your idea. I think I wish I started doing 

578
00:39:33,850 --> 00:39:37,690
that sooner because I, I used it for all the fun things, like we talked about earlier. 

579
00:39:37,690 --> 00:39:41,950
I was like, let's make a funny joke about customer success with AI or something. 

580
00:39:41,950 --> 00:39:47,410
And I'm just, I, I really wish I started to use it sooner in more practical settings. 

581
00:39:47,410 --> 00:39:52,420
I wish I started to use prompts more or at least get better at prompting it as well. 

582
00:39:52,420 --> 00:39:56,530
I mean, much like everything else in life, I wish I started so many things sooner 

583
00:39:56,530 --> 00:40:00,430
as well. And like, if there's one thing I would say is like, you should really be 

584
00:40:00,430 --> 00:40:05,810
using it every day. And I hope this episode gives you everyone that wake up call 

585
00:40:05,810 --> 00:40:10,790
of like, you should be using AI somewhat in some way every day. And, 

586
00:40:10,800 --> 00:40:11,630
I guess that kind of 

587
00:40:12,200 --> 00:40:16,610
just leads me on to the final piece that I wanted to ask you, which is like, is there, 

588
00:40:17,50 --> 00:40:21,140
if you look back at your AI journey and how you've been using it, is there anything 

589
00:40:21,140 --> 00:40:26,240
you wish you did sooner or is there anything that you wish, you know, you would've 

590
00:40:26,240 --> 00:40:30,550
learned faster? What, what is it that you'd love to leave our listeners with? 

591
00:40:31,390 --> 00:40:36,410
I mean, I wish that I had, uh, you know, in retrospect I wish I had gotten in, just 

592
00:40:36,410 --> 00:40:40,340
like you said, gotten in there sooner and, and started using it sooner because I 

593
00:40:40,340 --> 00:40:44,240
was a little bit behind the eight ball and then, and then it was a bit bunch of cramming 

594
00:40:44,240 --> 00:40:44,930
to get me there. 

595
00:40:44,940 --> 00:40:48,360
one thing that has helped me tremendously is, 

596
00:40:48,370 --> 00:40:49,230
there's one, 

597
00:40:49,940 --> 00:40:55,800
uh, daily, uh, live stream slash podcast that I actually listen to quite frequently. 

598
00:40:55,800 --> 00:40:57,990
I don't listen to it daily, who has time for that? But, 

599
00:40:58,220 --> 00:41:01,930
I was about to say, whoa, what a commitment. Yeah, but it's called Everyday ai. 

600
00:41:01,950 --> 00:41:06,320
and so I'll listen to it quite frequently, probably two, three times a week. And, 

601
00:41:06,330 --> 00:41:08,900
the ho the host Jordan and his team 

602
00:41:09,460 --> 00:41:13,550
basically summarize what's happening with AI on a regular basis. They have a lot 

603
00:41:13,550 --> 00:41:19,130
of amazing guests from like Microsoft and Google and, and Amazon on regularly to 

604
00:41:19,130 --> 00:41:23,120
talk about what, you know, what it is they're doing as well. But then they'll, they'll 

605
00:41:23,120 --> 00:41:27,170
give you practical advice. So if you look back at their show, 

606
00:41:27,180 --> 00:41:31,910
you know, there, there are episodes that are related to like practical advice on 

607
00:41:31,420 --> 00:41:37,300
how you can just go use it for X, Y, Z or incorporate it into your daily, you know, 

608
00:41:37,330 --> 00:41:38,170
work life. 

609
00:41:38,190 --> 00:41:41,670
that's, that's been instrumental. And, and there's, you know, there's a couple other 

610
00:41:41,670 --> 00:41:45,340
creators out there, but that one in particular is one I pay attention to a lot to 

611
00:41:45,340 --> 00:41:45,630
keep me current. 

612
00:41:46,500 --> 00:41:52,540
Thank you for that suggestion. I also have another suggestion as well, which is Superhuman. 

613
00:41:52,540 --> 00:41:57,840
A, it's a, it's an AI newsletter that comes out every few days and he just has the 

614
00:41:57,840 --> 00:42:03,960
top notch, what's happening in ai, what is next in AI prompts that you can copy, 

615
00:42:04,140 --> 00:42:09,420
just like ins and outs of AI in general. And I, I can't say I read every newsletter 

616
00:42:09,450 --> 00:42:12,750
'cause it's every other day I think he sends something out, which is quite a lot. 

617
00:42:12,750 --> 00:42:13,240
But 

618
00:42:13,590 --> 00:42:18,40
I, I have things saved. I try to get through it all. But yeah, there's just tons 

619
00:42:18,40 --> 00:42:21,720
out there and, and thanks so much for your suggestions, Alex. I know that everyone 

620
00:42:21,720 --> 00:42:25,710
listening probably has been taken tons of notes and we could probably keep talking 

621
00:42:25,710 --> 00:42:27,840
for ages, but dude, we can keep going. I wrap up 

622
00:42:29,710 --> 00:42:34,410
and I loved having you on the show, but are you ready for the quick fire questions 

623
00:42:34,410 --> 00:42:40,200
where I challenge my listeners and my, my guest to try to answer this next few questions 

624
00:42:40,200 --> 00:42:41,760
in a sentence or less? Are you ready? 

625
00:42:42,520 --> 00:42:44,980
Yes, I think, okay. I don't know. Okay. 

626
00:42:46,630 --> 00:42:51,640
Everyone struggles if that helps you out. But my first question for you is, if you 

627
00:42:51,640 --> 00:42:55,930
could predict the future, what do you think CS will focus on next year, 

628
00:42:56,840 --> 00:42:58,420
2025 

629
00:42:59,710 --> 00:43:02,860
next year? Gosh, is it, okay, that's 2025. 

630
00:43:03,340 --> 00:43:07,780
look, I mean, I don't want to sound like a broken record, but it's this stuff, like, 

631
00:43:08,110 --> 00:43:15,160
it's, it's incorporating these technologies into like our everyday activities because 

632
00:43:15,160 --> 00:43:19,360
this stuff is developing rapidly. The cost of development is plummeting and so we're 

633
00:43:19,360 --> 00:43:23,290
gonna see more and more and more apps filling more and more and more use cases. And 

634
00:43:23,290 --> 00:43:29,140
so it's gonna be, I think 20, 25 is the year to like wrangle it all into, okay, this 

635
00:43:29,140 --> 00:43:31,60
is what we need to go do with this stuff. 

636
00:43:32,340 --> 00:43:36,250
Awesome. I think that was more than a sentence, but let's see if we can get better 

637
00:43:36,280 --> 00:43:37,150
at the next few ones. 

638
00:43:39,160 --> 00:43:44,350
So my next question is, which app or software do you use every day, every week on 

639
00:43:44,350 --> 00:43:48,760
your phone or laptop and why? I think I know the answer, but let's see what it is. 

640
00:43:48,760 --> 00:43:48,130
I 

641
00:43:48,820 --> 00:43:54,220
mean, perplexity, that's it. You know, it's like, it's become my Google and, uh, 

642
00:43:54,490 --> 00:43:55,360
enough said, 

643
00:43:57,430 --> 00:44:00,610
not sponsored by Perplexity, but I feel like not it's sponsored at all. I should 

644
00:44:00,610 --> 00:44:04,320
actually talk to them about podcast sponsorships should because about them lot you 

645
00:44:04,320 --> 00:44:08,560
should, you should, you love them. Cool. Next question is, if you could change one 

646
00:44:08,560 --> 00:44:10,840
thing about customer success, what would it be? 

647
00:44:11,380 --> 00:44:12,460
The QBR. 

648
00:44:13,180 --> 00:44:15,400
Ooh, that's controversial. 

649
00:44:16,350 --> 00:44:19,930
Is it though? I mean, everybody talks about how much they hate it and like, 

650
00:44:19,950 --> 00:44:23,980
I don't know. An executive who really wants to sit on the QBR, especially if you're 

651
00:44:23,980 --> 00:44:28,540
low value vendor. Like if you're not part of the huge part of the budget, then 

652
00:44:29,330 --> 00:44:35,110
don't bother. Like, engage in meaningful ways with, with like meaningful information 

653
00:44:35,110 --> 00:44:38,200
instead of just like an hour meeting that has no value. 

654
00:44:38,870 --> 00:44:42,850
Yeah, I can see where you're coming from on that. I have the opposite opinion 'cause 

655
00:44:42,850 --> 00:44:46,910
I have like super high touch enterprise customers, so very different. Sure. 

656
00:44:47,750 --> 00:44:52,360
You know, that's fine. But yeah, I, I can see where it's, it's definitely outdated 

657
00:44:52,360 --> 00:44:57,490
and not a good use of time. So great answer. And my last question that I love asking 

658
00:44:57,490 --> 00:45:00,160
everyone is who should be my next podcast guest? 

659
00:45:00,790 --> 00:45:02,620
I should have prepped for this, but I didn't. 

660
00:45:03,160 --> 00:45:03,730
Um, 

661
00:45:05,700 --> 00:45:06,550
Sarah Roberts. 

662
00:45:07,500 --> 00:45:11,410
Ooh, Sarah's great. Sarah's awesome. I love Sarah. Sarah's awesome. Yeah. Yeah, I 

663
00:45:11,410 --> 00:45:15,190
love her too. Well, Sarah, if you're listening, I'd love to chat to you, but thank 

664
00:45:15,190 --> 00:45:20,200
you so much, Alex, for your time and your expertise and all your insights. 

665
00:45:20,710 --> 00:45:25,930
I absolutely loved having this conversation with you. If our listeners want to learn 

666
00:45:25,930 --> 00:45:30,130
more or chat to you further or maybe have a follow up question, what is the best 

667
00:45:30,130 --> 00:45:34,120
way to get ahold of you? Uh, LinkedIn is great, but you can also go to digital customer 

668
00:45:34,120 --> 00:45:35,590
success.com. 

669
00:45:36,160 --> 00:45:39,970
Awesome. I'll make sure to link everything down in the show notes. Thanks again, 

670
00:45:39,970 --> 00:45:42,330
Alex. Thank you. That's awesome. Appreciate it. 

671
00:45:43,870 --> 00:45:48,160
Thank you for listening to the Customer Success Pro podcast today. I hope you learned 

672
00:45:48,160 --> 00:45:51,940
something new to take back to your team and your company. If you enjoyed today's 

673
00:45:51,950 --> 00:45:56,180
episode, please can you take one minute to give me a positive review on Apple Podcast. 

674
00:45:56,450 --> 00:46:00,140
It takes a lot of time and energy to create an episode, and I wanna continue to create 

675
00:46:00,140 --> 00:46:04,790
more for you, but it would be great to know that you are enjoying these episodes. 

676
00:46:04,430 --> 00:46:10,130
Also, do not forget to subscribe to this podcast on Apple Podcast or Spotify or wherever 

677
00:46:10,190 --> 00:46:14,930
you listen to your podcast as I release a new podcast every month. And if you have 

678
00:46:14,930 --> 00:46:18,890
any topics that you would like me to discuss in the future or you would like to be 

679
00:46:18,890 --> 00:46:23,90
a guest on this podcast, please feel free to reach out. All my contact details are 

680
00:46:23,90 --> 00:46:27,230
in this show notes. And if you enjoyed this episode, don't forget to share it because 

681
00:46:27,230 --> 00:46:31,700
sharing is caring. Thanks again for listening. And tune in next time for more on 

682
00:46:31,700 --> 00:46:35,600
customer success. Cheers to your CS journey and catch you next time.