Another Cloud Podcast
A podcast designed to bring you stories from the smartest minds in IT, operations and business, and learn how they're using Cloud Technology to improve business and the customer experience.
Using AI to Make Great CSMs
with Alex McBratney and Aarde Cosseboom
Don't have time to listen? Read the full transcription.
Aarde, Bryan, Alex
Hello, and welcome to another cloud podcast designed to bring you stories from the smartest minds in it, operations, and business and learn how they're using cloud technology to improve business and customer experience.
Thanks, everyone for joining another cloud podcast. Today we have Alex. My co-host, Alex, welcome to the show.
Always a pleasure Aarde great to be here
Of course, and we've got our guest speaker today. Bryan Plaster. Bryan, you're from complete CSM. So tell us a little bit about who you are what you do, and a little bit about complete CSM.
Awesome, I'd love to do it. So welcome, everybody. Thanks for inviting me to your podcast. I'm really excited to be here. And what we do is an AI extensions to customer success. Like if you've ever looked at customer success, intelligent, it's like okay, well how do you make each CSM better give them digital superpowers? How do you enhance and augment their power across every stage of the customer journey?
And when you when you talk about AI, I know that's like a touchy subject for our audience. Are we talking about automation? Are we talking about something that's listening to everything that's happening and giving them subject some suggestions? Tell us a little bit about you know, artificial intelligence or what you guys do in the background that can make a CSM? You know, a super CSM?
Haha, that's a super loaded question. I love it. So let me give you a little more background. So I've been in customer success, I guess 13 years, I was one of the first people that use gainsight. I was customer number five. And I found these guys because I want to answer that ultimate question, NPS surveys, net promoter score. And I want to answer that ultimate question and get people to get our customers to give us feedback on us. Right, so I started down that path, I found games I they were called Jabara, there was this little company like 10 people about the size of us, and they had a solution that I needed, right. And so I got involved in customer success and games, I've since grown to be a billion dollar unicorn, which is great. And they've, they've brought me along the way, like I've spoken every pulse conference, and every customer conference, they had just a great partner. So we started off with, okay, customer success, needs technology, right? Because you're trying to make sure that customers are successful, trying to make sure they adopt. But you know, unfortunately, people don't give you the CFOs don't ever give you the right ratios. Right. Okay, you got 100 accounts, it's your responsibility to make them all successful. Okay, how do I do that? I'm gonna call everybody, right. And so I've always looked at technology to help like the net promoter score in different pieces. And so we look at gainsight as a platform, there's a lot of other really strong platforms out there to this mark, carrot and turn zero, you know, customer success has really evolved, right, it's the next level. And so what we set out to do is not rebuild a platform. We set out to be extensions to the existing platform by adding AI. So So your question is, you know, we thought a lot about that, do we just rebuild, you know, a customer's test platform, where do we go and we do something really cool with it right, and really give the CSM digital superpowers. So that's what we set out to do. It's every single interaction that you know, the CSM, the support whoever, you know, on your team, every interaction that they have with the customer, that should lead to the next best action. Right? And it's hard to do, because you have this full team of people, right? You have to go ask them, Hey, how did the meeting go? What was going on? You know, tell him Tell me more. And then let's make a game plan. Let's have another meeting ourselves to make a game plan with the customer, like every interaction to lead to the next best action. And you should use things like leading indicators, the same sentiment to really drive the next conversation. Right? So once you have an action plan with the customer, now you got to start looking at Okay, well, how did they feel about us the last 10 times that we talked to him? Are they getting more and more excited to work with this? Or do they like not like us? Right? It's kind of this way that you can you can subjectively talk about that stuff? But what if you could use the data from all the interactions and conversations to tell you more about that customer? Right. And then I guess the third piece you're asking about AI? That was I was my long winded answer to your question. So the third piece of AI is, you know, we have this digital EQ coach, so it's all about emotional intelligence. So you use what happened in the interaction, then conversation with your customer from the people, and you, you go back and you can coach them and train them, Hey, don't do this or do this more or have more enthusiasm or, you know, be a little bit more confident. Or you know, try to be careful. Don't tell so many stories because it just made the customer upset like they shut down. Um, so that digitally code EQ coach kind of brings it back to Okay, well, how do we make take the team from really good to great now using technology? And that's what we do.
I really love that. And, you know, we just posted a podcast yesterday about AI and just how, how good it is, but also how bad it can be at the same time because there's this balance between Well, how much data can we capture from consumers or from, you know, from other businesses? And what's, what's fair, what's, what's ethical and things like that. But what I'd like to ask is, you know, what was the process of, you know, when you guys started to developing that platform? And what what did that look like for you guys to get it to a point where like, Okay, this is, this is valuable, this is gonna be great for our clients to add this as a widget to their CSM.
That's, that's a great question, Alex. So I love to tell the beginning, right? Like my, my co founder, and I said, hey, let's do customer success in AI. And our question was, what does that mean? Right? What would actually help people? Right? And so myself being a practitioner for the last 15 years, I thought, well, you know, there's a lot of little plugins and little things that I could do right, now, I can add a little plugin to his him conversation, right. And I can add a little inbox piece to Gmail to give me some insights on my emails, as long as little things that that you can do are starting to pop up in AI. But really, none of them are are looking at the longtail the history with each client. Right? They're not looking at the history of, Hey, I have these 10 interactions with this person. Are they slowly getting more loyal? Or is it like a boxer nicer to them, and they're getting more and more upset? Right? So these, these tools that just plug into one conversation are are nice, and they're exciting, but they don't really give you what you need to be able to work with those people. Right? And so we look at our API s we're not trying to be big brother. We're we're trying to look at, Okay, tell me more about this person, how to follow up with this person, how to work with them. What are some observations about, you know, how we can work together better? together better? Right? We actually have something called a SWOT analysis. It's a strengths, weaknesses, opportunities and threats, which is, hey, what what are some opportunities to work with this person? And so then you can work on Okay, well, how can we partner Better Together based on their personality profile? And you know, the really the thing that really kind of set this in motion was a pandemic, honestly, when the epidemic hit, everybody was grounded. No one was traveling anymore, including me, right? I'm like a Marriott lifetime platinum. Titanium elite, actually, like I'm like, the full marathon person cuz I traveled so much. But when the pandemic hit, no one traveled. So how do you continue to compete, like communicate with your customers? Well, everyone's doing zoom. Yeah, right. So Gong Gong IO took off, you saw that? So I was looking at Gong, I'm, like, pretty simple, what they do they record conversation, they let people add notes to them, and they play them back. Right. And so people were doing six conversations a day. And then they were rewatching them for the next two hours. Right, trying to trying to, you know, take notes and figure out what happened. And so, you know, part of our kind of plan was, okay, this is this is something that is taken off, everyone is doing things a different way. I'm gong is more for sales teams, it's a little bit, it's a little bit different thing that people are thinking about, they're accepting it. So let's, let's look into zoom, right? There's 300 million users on zoom, you just like you did, you can record the zoom conversation. It asked people if they want to be recorded or not, you know, people have been for years being recorded. Right? They're like, Hey, you know, we're gonna record this for training purposes, or whatever. So people are getting super used to that. And so people are open to recording, we actually produce, we also have an AI insights on the meeting. So we have an email that we can send out to people, hey, you know, if you recorded I'll give you insights on on some how we could work together. Right? So we're trying to make it easier for people to work together can demystify the AI piece and just make it so it's not big brother, it's, hey, we can all get along better. Right? You know, if there's some kind of a, you know, a tense situation, this person responds well to laughter. Whereas this other person responds well to resolving that issue right then. Right. So when you look at AI, you're right, there's a, you know, over the last couple of years, there's been a kind of mystique about it. That's why these all these different applications have these very kind of targeted, okay, well, you know, we're going to look through an email and give you one insight or we're going to write this for you. Right, and so it's slowly getting accepted in the pandemic, just put it into high gear. They're like, Hey, we're being recorded. Anyway. Let's use it to work together, together better. Right. That's what we're trying to do.
Yeah, like, I like how you guys are focused on more than just one conversation or one data set or data point, you guys are focused on the overall interaction between the customer and the company. And using those as indicators, not only the actual interaction itself, so the conversation, information or detail, but also the subjective and objective data around it. Like what was actually said, what was the sentiment to drive potential outcomes, like, this person, potentially might be good for upsell or cross sell or, you know, maybe they're, they're, they're trending towards wanting to cancel or leave. So do kind of a touch, touch base with them to make sure that they're still happy. So how, tell us a little bit about how you guys orchestrate you know, the next step or because every organization, every CSM team is slightly different? Is it something where there's like some, some knobs and, you know, checkboxes for them to configure to make it easy or do you guys, do you guys help with that? How does that work orchestrating the next step?
Well, some of that's our secret sauce. But I think the foundational piece of it is, you need to grab every digital interaction like, like every zoom recording every email, every support ticket, every web chat, every survey, right, the more information, the better for sophisticated AI to really work, you need a lot of information to be able to work through it. And so how do we roll that up? Well, you we break it into we have an AI pipeline that looks at sentiment, right? Regular sentiment, but it looks at emotion. Right? It starts looking at objective behavior, like, you know, how long did you wait, before you answer that question? How many questions are asked versus statements? How many action items from the call? You know, what did you smile? Its facial expressions? Were you were you delivering enthusiastic peace, but frowning? I don't know, let's, let's take a look. What was your commitment to the conversation? But how motivated were you to continue on with that conversation or continue to do the things that we talked about? Right. So you there's a lot I mean, there's a lot of information in conversation. And truth be told, we use a lot of AI pieces we use Amazon comprehend, right? We use Google AI, when we use theory, no AI, right? I mean, we use a lot of really powerful AI technologies. And a lot of our value prop is we consolidate them together by person. And then we can follow those by count, right? We're in customer success. And then the prediction piece is okay, well, how do you get there, you now you have loyalty of the account, we projected out into the future. So we use something called a polynomial regression to project out the line in the next 18 weeks. And then it gets into line math, we look at the slope of the line. I'm probably telling you too much already. But you know that like, How much is it trending up or down? You have the flag. And then you compare that to other metrics like like adoption, right? So if you have, for example, the loyalty of a customer is going going down. But adoption is going up, you know that that tells you something that tells you that, hey, they don't like us as much overall, but they're using our product more, they're probably migrating off our platform. Right? And that's the things that I've seen from my experience and customer success. And so we have 10 different scenarios. We're like, how far out do the lines cross? What are the thresholds of hitting top or bottom? And so the the predictions aren't just rules led one of the things that I like Gong, I like what they stand for, were the gong for customer success, right? We build AI models that then learn from from the different meanings, right, and then the trend predictions are from those models in those meetings. Right. So it's a pretty simple setup, you build a model of the process, though, the cut the way the customer journey is supposed to go. That's the way you build the models. Yeah, right. Like everyone has the ideal land adopt, expand, renew, everyone has the ideal state. And so we capture that what's supposed to happen. And then we actually apply it to the real conversations and interactions and see how you did. Right. How are we doing? And then what's our feedback loop to teach the CSM? Okay, well, how do you do better? You know, don't say these words, or say these words or don't tell, don't tell a lot of stories in escalation, and keep your enthusiasm down because the customer super upset, right? These are all the human first factors that we trigger in these models. So it's just basically what you were doing anyway, using EQ and AI and then applying that to How did it go. Right, and then you start getting more and more insights on your CSM, and also your customers.
So what was interesting because it sounds like there's a lot of pre work that POS that needs to probably be done by the client to make sure they have a framework around what CSM is to them, or what Customer Success is, before they could just jump in and throw AI at it, right? It's only as good. It's only as good as what you build it to be. I think one of our guests even before said, you have to treat AI like an employee. Right? If you don't nurture it, and grow it and teach it, it's gonna go off on some different direction. And you have to look at it like an extra employee, how did how did you get a client started from zero? So they, hey, I love this idea. Sounds great. You know, what does that look like?
That's a great observation. And you have to train AI, right? In all the movies. It's like, they I started the kid, and then slowly grows and learns, right? And so I started messing with AI, I used the Microsoft AI. I guess it's been about six years ago, building chat bots, like how do you listen to what people are typing in, interpret, interpret it right, and then give back a regular response. And the training took a long time, right. And the way that you trained it was you would have to train it by what, what do you think should happen, and then you train it by what actually happens with the customer, right, and then it slowly gets better. But the problem is, if you're just starting out, it's not very good up front. Right. So that's what a lot of people say so. So what we've done at complete CSM to try to mitigate that was we have these digital twin their digital archetypes of each type of meeting and every stage of the customer journey. So So we've gotten with our advisors, and I've gotten with quite a few Customer Success leaders, like I'm pretty connected in the space, I know a lot of people we get we get together and everyone's gives their input on what a model should look like. So an onboarding model, right is be super enthusiastic, right? You know, you want to have this logical structure of what you're trying to accomplish, because like an onboarding, you're trying to set go live date, right, you're trying to make sure they have their team trained, make sure they're enlisted with support. Like if you think about onboarding, it's pretty standard, what people do. So we provide this existing built in model, they start you off on the right path. And then what I've seen our customers do is they say, Okay, well, let me pull up my playbook for onboarding. Let me tweak this change that let me add a few more moments or words. And then you have a starting point, that's actually pretty accurate. It's what you're already doing. So it's not building it from scratch, is the using best practice models that you're probably already using, and then tweaking for your organization? So we have that, you know, executive Business Review, what happens there tell a lot of stories about how successful you are. Right? I mean, there's all these different pieces that you put into the model. So we have those built in. And so the the retraining aspect isn't really huge. But we on the models, let's say we actually focus that effort on, okay, we have the model. Now we've talked to customers, and we've, we've processed those interactions, and we've gotten insights from it. Now let's go back in let's coach the CSM, right, that way, the CSM can change their behavior, like you know, that's the simplest form, it's like don't use these words, because these are allergic reactions. And at the more complex, it's like, if you're in an escalation, don't be super, don't be so enthusiastic, because your enthusiasm was through the roof and the customer knew, right and their commitment level started to drop. So that that becomes this coaching back to the CSM, who can then practice the session against the model and tweak it if they need to, or the manager can tweak it, and then go through the whole cycle again. Right now you're getting more insights on customers, you're learning as a CSM team. They're growing, it's just taking, taking everything from good to great is the way I like to look at it. It's like, you know, how do you how do you go from a really good team to a great team. But it's a very specific process to get there. It's not just going to happen because everyone likes each other. It's going to be like, Okay, well, what do we need to do to get from good to great, and that's what completely offended does, you know, down to the EQ of delivery and delivery of meanings.
Yeah, I used to run a team and we were in there were some collections but also some cancellation and save attempts. And oftentimes, I would overhear just because this is before there was AI and the ability to really record in a meaningful way. I would just walk the floor and I would hear the words sorry, a lot and, you know, sorry, is good to a good term to use emotional intelligence wise, it's good to be empathetic and apologize for things. But it's not good to be using that you know, every 10 minutes or every, you know, when the third call because I At that point in time, it just turns into, you're just apologizing for letting the customer down, instead of actually just understanding what their needs are, and then fixing their needs, because the person who's calling in may not really want to hear sorry, that you let me down, they know that you've heard the feedback, they just want some sort of solution answer. But shifting gears a little bit, I had a question around. And you said something earlier, that was really, really insightful. Because we had a couple other people on the podcast earlier, who oversee these Customer Success teams, customer experience teams, and for software as a service tools. And they like to use their internal metrics of adoption or usage as a positive. But you said something was very interesting that I don't think a lot of people and this might be insightful for the people listening to this podcast, sometimes usage could actually be an indicator of potential churn or someone who, you know, has bought another product. So they're getting all their information out of your product by going in and manually taking things out, or maybe your products too hard to use. So it's 10 clicks instead of two. So they're in there, and they're using it to its fullest. Because they have to, and they're, they're handcuffed by, you know, having to go through all those steps. So, tell us a little bit about how you adopt and change to all of your different customers. Because some customers, you know, adoption can be an indicator of upsell. And some can be an indicator of churn some because it's more of like a neutral metric that doesn't really play into anything's out. How do you work with them? How do you how do you adjust that secret sauce? So that's catered to the individual person?
I love the question. I mean, that's something that everyone thinks is completely custom, right? Like, I have to make my own turn model because no one else is the same. Right? And I've always said the same thing. I've run Customer Success teams at Talon that Informatica BDC. You know, I go in thinking, Okay, whatever you're doing is wrong. We're gonna go fix it all. Which is, of course, a very poor change management attitude. I've learned from those. But you're looking at Okay, well, how did how does the business work? You know, if the adoption is going down? And but they seem like they're loyal, and they're going to renew? What exactly does that mean? Like in that case? A lot of times, it means well, you know, the champions loyalty, but the users are not, right. So every one of these scenarios, they're common, but they're also different. So what we do a complete CSM is we let we let our customers configure the thresholds. Like it's like, Okay, well, this scenario only works if the renewals six months or more out, right? It's like, How fast is adoption going now? Is it a slope of negative one, two or three? Right? So you have all these metrics, and in a lot of people, they don't care about that. They're like, hey, just give me the base model and go for it. But, you know, we have those indicators that you can change and tweak, to really let you understand your business and the way it came up with it. Like we're working with talent right now, on a turn modeling. And so I sat down with VP of customer success, and I gave him these 10 scenarios. Alright, so based on the conversation of loyalty, and based on adoption, and based on the csml, if it looks like this, what does that mean to you? Right? And you have a lot of me confirmed, I think a couple of them, he kind of changed his mind a little bit. He's like, Well, actually, only if it's like, way out in the future. Like, yeah, that's the discussions that you have. And so that's, that's the configurable thing. In complete CSM, it says pretty new technology. But I mean, I've made so many German modelers write in spreadsheets. And I've come up with so many if then else statements, and so many, okay, well, if this, you know, the turn is lack of adoption, but it's because, you know, maybe the they didn't like, their account manager, or maybe it's because they didn't get the feature that they asked for. Right. And so you get these super deep levels of rules and triggers, right. And those, you know, after a year, you can imagine, you know, always reinventing in those things get huge, and then all sudden, you have to start over. So that's why we really bold it up in the line math. Because if you can project the way things are going it's like a trending fatigue, they are more trending, they're more excited. You look at this time series data. If you're getting super excited, aren't you right? Like, you know, I went to a couple of conferences last year with snowflake and data robot, you guys heard of those guys? before and and data robot is one of the first kind of mass market AI systems you know, there's there's some really neat technologies out there and platforms like databricks is another one right? And and so when you look at these platforms they live they let you go hire a data scientist. And do all this yourself, right. And of course, it's, you know, to your projects, and if 20 people, but I saw a few things that I thought were really powerful. And we use some of those same techniques, like when you look at a time series, it's not two years worth of data, it's the window. So let me define, you know, the time series is moving window of data. Let me let me look at how things interact in that window, to then look at how the next window is probably going to be. And so we started looking at all these different pieces, and you really dive into, you know, the AI is this big umbrella. But then you get into NLP to understanding and kind of pulling the data out, structuring it, you get into NLU about suggestions, then you get into predictions. But it's hard to predict things based on rules, you want to predict things based on trends. And so that's what we did is we took it to, really, we feel like a whole nother level, if you just have a bunch of rule system, there's gonna be some problems, you'll never get it right, you'll always be tweaking it. But if you look at things or the way things are trending over time, then you can really put together you know, how, what's the slope of the trend? How far till the lions interact? Right? When do they hit upper and lower limits, right, you start looking at these things, you get deep insights. And when you pair that with, you know, myself, my team and all the bps and of customer success that I've talked to, right, you start to get consistency, that, hey, this always looks like this, right? And so it's not so much a prediction that you don't trust, it's like, this happened 10 Other times, it's gonna happen again, right? And so you start getting confidence in what you had. And then you can tweak it if you want. But I mean, you know, when you when you try to simplify things to make them more powerful that that really works in my book.
I totally agree with that. That's, that's a huge area where we're moving in the future. Go ahead, Alex.
No, I was just gonna say like, you know, you talk about data scientists, and you talk about, you know, your VP of customer success that you talk to, and it just, it seems like it was as much AI as you add, you still need people with experience, you need people that know what they're doing. And that have been around data that have been around the industry, to be able to interpret the trends and interpret and know what questions to ask. Because a lot of people just think AI as you plop it in it goes, but that really takes a smart mind to get the most out of it
Is SME, so you're totally spot on Alex, like I spent a lot of my days and, you know, running development teams and product companies. And the, the more removed you are from the SME, the harder it is and the longer it takes to build things that like you have to make requirements. And then you have to go through the product person that doesn't know the business, but then or the product, then you have to give it to developers, right? So if you think about all these different levels of being removed, it's, it makes things super complicated, right, you got to iterate over and over and over again. And that's when you train AI, that's, you know, if there's a long tail, you have to sit there and you have to keep, you have to train it by adding a bunch of rules and keep getting feedback. It's this big loop. But you have to do but how do you accelerate that loop? Right? Where you get experts to sit there and say, Okay, well, this is my scenario. And then they apply the limits into their scenario, and then they validate if it's right or not, and then you can learn from that. So I, I'm with you, it's, it's kind of fun, neat stuff. And I have so many war stories about these things that happened. Just like, you know, why would a green customer turn? Well, because they were migrating off our product. And we were like, so excited that the adoption was there. And they're, they're almost at full capacity, we're gonna go sell them double, because we're so excited. But in actuality, you know, they're, they're actually moving off your product. They're not talking to you. Right. And so if you just look at one metric, like already was saying, Man, these guys do is through the roof, but we can't get ahold of them, Well, probably means something. Right? That means that they're probably, you know, migrating off your platform or getting as much out of it. As I was thought I was talking to actually a VP of customer success, like right before, we talked today. And he was saying they have this this data science project around kind of clinical trials and medical research, real interesting company, and we're, we're talking about partnering together. But one of the things he told me is, yeah, sometimes our usage goes through the roof, because they're getting all the research they can, you know, before they move off our platform, and so now now they look for that. But you know, you need the I look for that for Yeah, based on my model. So that's, those are things that everyone sees, but I think you want to be able to quantify those. And that's what we really help with
You've talked a lot about predicting the next step or having the AI put everything in a data lake like a snowflake, and then have the AI actually tell you what the next step should be, it's going to be the customers gonna call them three days or renew or, you know, they're gonna go dark on you, you should probably reach out. But you know that that next thing? And I think you mentioned it earlier, but there, there's a big difference between having a very flat decision tree, you know, yes, no, yes, no, yes, no, across just a couple of dimensions, maybe 234, or five, verses actually feeding all of it into one and then having a having it generates some sort of outcome or next step, as opposed to going down a predetermined path. For a call flow. I know in the conversational AI space, things like chatbots, and voice bots, we're growing out of this NLP, which is natural language processing, which is understanding what the person is asking and then pushing them down a decision tree, you know, go left, or go right into this nlg, which is natural language generation, which is understanding what they say and then curating a response from verbs and nouns and sentence structure. So there is no path. It's an unknown, like, literally, they're going down this, but the time is being written in real time as, as you're interacting with it. So what is your guyses strategy with your AI? Is it a little bit of both? Is it you know, more of a decision tree? Where you have a company organized, you know, this? Is the life cycle on these, this is where things should flow over time? Or are you trying to take that information processing and then return some sort of generation of the next step in in the relationship between them and their customer?
That's, a very good question. Because a lot of people are using AI term. And everyone's trying to figure out what it means. Right? I totally get that. And so you, you hit on the key points, you have natural language processing NLP, which is how do you take all that data and structure it somehow? Then you get into natural language understanding? Would you Okay, well, now let's start looking at the timing. The word choices, how subjective is the word, right? What's the polarity? Right? How long did they wait before they answered a question? Or what was the pace of the piece? Right? So you get into this understanding to really understand not only what they said, but what they meant. Right? And that's how you start getting to the next level, then you get into natural language generation, which is I mean, we do that pretty straightforward in our application. It's, you know, how do you follow up with this person? Right, it's how do you work with them? Right? And it's insights, that SWOT analysis about them, like, Hey, here's some strengths about the person. Right? And it's not telling you exactly what to do. But it's telling you how to work with that person there. We call them leading indicators of sentiment, like, how do you work with these people? That to really make make it a better experience for everybody? Right, and then you tie all that up? Right? That's, that's where we got into the term turn Mahler, then you tie up each person's insights into the company insights, and then you can start looking at Okay, well, for all the people in the company, you know, here, here's the way things are trending up or down. Yeah. So it's really it builds on itself. You know, like we talked about at the beginning of the podcast, there's a lot of really cool AI technology. And we're using the best one we could find, right and even build some of our own. But the key is, they're all independent individual. So when you start rolling them up, and you start getting insights on people from the multiple interactions that everyone in your companies have with them, and the people at that company, that's where you get, I mean, just a magnitude, exponential insights and help. So now you can actually start trusting the system to Okay, well, what's the next step? We should take? Right? It was just okay. Well, they call it next best action, because I've heard you probably heard that term before. That's a pretty neat, neat, neat thing that telecom kind of pushed. There's like, Hey, you know, what's the next thing we do? Because in customer success, you have this journey that you're taking, you know, you have journey stages, you're taking customers down this path of, Hey, this is the best outcome for customers. Like a lot of times it's adopt and then expand and then there's, you know, renew and you do different things in them. So as a as a CSM or theism team, and I've fallen into this trap before, it's really easy to just follow that path, no matter what's going on. All right, hey, we got to get you to the next phase, right? We got to get this we got to get we got to have an EPR this date, right? We got to do this thing. And so when you look at not only the path that you want to be on, but you know what are Some of the variants that the customers are taking on that path, that's where you really get some insights. And you're going to adjust may maybe the path that you define two years ago isn't the same, right, and you need to tweak that you need to get that feedback loop to, let's update our journey for the customer. And let's also teach the team to trigger these right, kind of EQ moments, right to say, the right words that people respond to, and then instruct right to do the right thing for the nation, meaning like to have the high level of confidence and one type of meaning, and maybe keep your enthusiasm kind of at bay in a different kind of meeting. Right? So when you when you look at all these different factors, it's not only the insight you generate, but it's a feedback loop back to the team to go from good to great. Is this what I've always strived for as a running Customer Success teams, is we always got great people. We're always working together, we're always teaming to figure it out. Like I've never been on a bad team. But how do you get them to great, right, that's some kind of big pivotal thing that really requires a lot of data. Right? You're gonna have great CSM, but for the whole team to bring up that that's something that you can really only do with technology. And by leveraging all the data, all the conversations that are being had by everybody.
Yeah, and that's really the goal, right? Because you can use it to scale, right? You always have your unicorn salesperson, a unicorn CSM, that's just naturally great at something or they just have that knack. But it's like, how do you take that and learn from it for everybody else? And that's where the AI is doing that for them. Right? And you can see the where it's all going.
Yeah, I used to call it a baby, I still do the digital imprint, you take the digital imprint of your best DSM in each area, and then you replicate it to the team. Right? So it's not quite that simple. I mean, that would be cool. But do you understand, okay, how do they deliver this meaning? And then you bring it back to the team? Okay, well, you should do these same things, too. And it is not just what you say. But it's down to that EQ, those are the ways that you act the way that you deliver. And that's so I think, the brilliance that gone and done in the sales side that you mentioned earlier. You know, that was their whole goal is like, how can you take, you know, take those learning moments, teaching moments for the good people, or what they're doing well, or what they're not doing well, and, and making it teachable? Yeah. And they've done that real well. And I, I love Gong, I like to say where the gong for customer success. Yeah. Because with the sales situation, you know, you have the bdrs. And you have the pre sales pieces, you're trying to prepare and say the right things and meeting when you get the sale will fail sales teams move on? Right? with customers, you have, you know, 369 a year two years of data, right? If you captured it all to really understand what's going on with the customer? And how can you make it a better experience for them? where everyone can win win win scenarios.
So I suppose if someone doesn't have a solution, like yours now or they're not quite ready for it, what would the suggestion be? My thought is we'll start recording everything start getting like a database of data so that when you are ready, you can have a year's worth or call two years worth that you can dump into the machine to get a head start. Is that something that you recommend? Or how do you see that for companies that are wanted but aren't quite ready for it?
I would absolutely recommend exactly that is you know, you probably you've already been recording your meetings, and then pandemic, and then you got to take time to go rewatch them to figure out things. Yeah, but keep doing that and start recording pieces, we actually have on the zoom marketplace, you can find complete CSM, where you can just go install the zoom plugin. It's called zoom for complete season. And then every one of your meetings that you record, we get insights that you can access on that meeting. We also have, we had the same challenge with another customer. They're like, okay, I've been recording all year, but I'm starting now. So another thing we did was we have an import process where we can import if they're still at zoom, we can import all your meetings from from as long and back as you have them. That's we've done that for a few customers that's worked out well. And then all of a sudden, you have six months to date. And you're like, Alright, let's go make this thing happen. And so that's pretty exciting, as well. But yeah, that's, yeah, we're still a new company. We're a startup, but we're just going by the things that just just like you you have experienced in these areas, your customers ask for stuff. We're like, Alright, that makes sense. Let's knock that out. Let's do that. So um, I would say that's the way to get started. Start recording everything. Then you can add on support tickets. You already have web chats. I mean, it's amazing. You're probably already sending surveys out, right like NPS surveys. I mean, it's amazing what you're already doing. It's all siloed and it's probably just being ignored. So keep doing what you're doing. Do more of it. And then yeah, Complete CSM brings it all together. And that's just the way we look look at it. But I wanted to take a second to look at this beer collection on the top. We talked about that though. I love this. Like, how many do you have beer from? Because this is
Different taps. Yeah, so, um, you know, during the pandemic, everyone went to work from home. And, you know, we all realize that our zoom backgrounds, the physical ones, not the virtual ones. They're, they're a representation of like, who we are and what we do, what we what we, our hobbies are. So I decided to put a stormtrooper in business attire because I'm a little bit nerdy, but a little bit business. And then my passion of beer. And, you know, back in the college days, it would be a bottle of beer that you put up on a shelf, but those get all moldy and gross. And you know, those were like $1 each. So I decided to step up my game and do beer taps, I would say there's about 40 of them up there. There's another shoe box of about 10 more that I have hidden away. And there's some ones that were gifted to me, there's some that I bought, there's some that were, you know, accidentally taken from some bars or borrowed from some bars at some point. But each one has its own story. And I tried to get one that's but what about you guys? I know Bryan, we were running up on time. But tell us a little bit about your background. I know we had a pre call and he talked about woodworking. So tell us a little bit about your wall back there.
Yeah. So you know, your your background is a little bit of you after this pandemic. And I think we're not out of the woods yet. But we're almost there. So yeah, I love the woodwork. So I built the wall here and stained, it's all red oak stuff, the fancy mahogany, but it's all red oak stuff. I could get it Lowe's and Home Depot and but the trim, I have a record player from record, I have a tube amp over here. So I'm rolling the music and vintage equipment. I have sleeping my little town so I'm out of Texas, there's a lot of little Texas towns that have like the post office, the you know, the the courthouse, all those little pieces. So my dad,
that's the way a lot of cities Look, they're all a town square.
So I like to keep my roots. But yeah, it's all kind of a weird color. So that's, that's where I'm at. I mean, on the other side of my office, I have a port wine collection. So
I went to Porto in Portugal. And I heard the whole story that's probably a whole nother podcast, I heard the whole story about port wine about how it's great to only grow in Porto. They, they burn up in the summer, and then they freeze in the winter. But that makes important. And then they put them in barrels, ship them down the river, and they pull them out and they add a bunch of brandy and stuff to it right and they distill it. But when you're in Porto, you're looking across the river, because one side restaurants, the other sides, all these wineries basically. And you see sandemans and you see like the you see all these different kind of Port companies over there. And I just kind of fell in love with the concept. So whenever I see a bottle, pour, wanna buy often, I have a kind of a stack like you do is great. I love it. Now.
My background is pretty boring right now it used to be more exciting with the mountain backdrop and a surfboard. But I still got to get it still moving into the new place. So we'll have to step up my game here for the next call.
We got Alex? Yeah, you're gonna have to play us some music?
No way. Yeah. I've been I haven't played for like a month. But I should, we'll just do I'll serenade you guys. But Bryan, it's been it's been a pleasure having you on and I just love the AI conversation. And I love just where it's going and what good companies are doing with it, and how it's helping businesses and consumers. And ultimately, you know, I think it's a you guys are on a great path. And I appreciate you coming on to the onto the podcast. Absolutely. I appreciate it. And I appreciate your love and interest. That's why I wanted to do this with you guys. Because it's all about new technology and how we leverage it for like real business outcomes. And we're super excited about just being part of bleeding edge and getting our story.
Good. We're a new company, but we're having lots of fun. So keep that going.
Awesome. Great. Thanks, everyone for the podcast. Join us next time on the next episode. Thanks, Alex for hosting. I'll talk to you soon.
Well, that wraps up our show for today. Thanks for joining. And don't forget to join us next week as we bring another guest in to talk about the trends around cloud contact center and customer experience. Also, you can find us at Adler, advisors.com, LinkedIn, for your favorite podcast platform. We'll see you next week on another cloud podcast.