How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy)

Matt Britton is the founder and CEO of Suzy, a consumer insights platform that has raised over $100 million in venture capital and works with top brands like Coca-Cola, Google, Procter & Gamble, and Nike. Matt is also the bestselling author of YouthNation, a blueprint for understanding the seismic shifts shaping our future economy, and Generation AI, which explores how Gen Alpha and artificial intelligence will transform business, culture, and society. In this episode, Matt demonstrates how he built a comprehensive AI workflow using Zapier that transforms customer call transcripts into a wealth of actionable intelligence. Despite not being a coder, Matt created a system that automatically generates call summaries, sentiment analysis, coaching feedback, follow-up emails, SEO-optimized blog posts, and more—all from a single customer conversation. What you’ll learn: How to build a trigger-based workflow that automatically scrapes and processes customer call transcripts from platforms like GongA systematic approach to quantifying customer sentiment on a 1-10 scale that has proven highly predictive of churn and upsell opportunitiesHow to create an automated coaching system that provides personalized feedback to sales reps after every customer interactionA workflow for extracting keywords from customer conversations to inform Google ad campaigns without manual interventionTechniques for automatically generating privacy-compliant blog content from customer calls that drives organic traffic and paid search performanceWhy CEOs and executives need to build AI skills firsthand rather than delegating implementation to engineering teamsHow to use Google Sheets as structured databases for AI lookups and enrichment within automated workflows— Brought to you by: Brex https://brex.com/howiai—The intelligent finance platform built for founders Zapier https://try.zapier.com/howiai—The most connected AI orchestration platform — Where to find Matt Britton: LinkedIn: linkedin.com/in/mattbbritton http://linkedin.com/in/mattbbritton Instagram: https://www.instagram.com/mattbrittonnyc/ https://www.instagram.com/mattbrittonnyc/?hl=en Company: https://www.suzy.com/ — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — In this episode, we cover: (00:00) Introduction to Matt Britton (02:36) Why Zapier became the backbone of Matt’s AI automations (04:17) Identifying your core business problem (09:02) How Matt built the initial trigger automation with Browse AI (13:42) The value of CEOs getting hands-on with building (14:00) Scraping and processing call transcripts (20:14) Using LLMs to generate call summaries and sentiment scores (23:25) Creating a Slack channel for real-time call insights (26:17) Extracting keywords for Google Ads campaigns (28:35) Building an AI coach for sales and customer success teams (29:48) Creating a follow-up email writer for post-call communication (35:25) Generating redacted blog content from customer conversations (37:51) How this approach changes team building and hiring priorities (40:19) Matt’s prompting techniques and final thoughts — Tools referenced: • Zapier: https://zapier.com/ • Gong: https://www.gong.io/ • Browse AI: https://www.browse.ai/ • ChatGPT: https://chat.openai.com/ — Other references: • Qualtrics: https://www.qualtrics.com/ • SurveyMonkey: https://www.surveymonkey.com/ • Slack: https://slack.com/ • Google Sheets: https://www.google.com/sheets/about/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  With my company, my sales team was consistently telling me that they just didn't know how to find anything. They didn't know how to find what customers were interested in.

• 0:08Claire Vo?

  You had a bunch of salespeople. They said, I need more information to serve our customers better. You realize you had 25,000 hours or something of recorded customer calls, which are the perfect source of truth for how customers want to be interacted with. You're going to show us a Zap now that takes a single recording and does a bunch of stuff.

• 0:26Speaker 00

  So basically, I needed to figure out, well, can I create a feed for Zapier? So new, the call ID of each new call as it occurred. So the first step is essentially a trigger where a new call comes in. It'll basically scrape the information from Gong. And one of the things Gong will give you is that call ID. So that appended to the URL essentially is all I needed to give browse, to be able to go to that URL and say, but essentially scrape the entire transcript. It wasn't connected. I had to kind of hack it together.

• 1:01Claire Vo?

  I love a CEO that knows how to build it. I love a CEO who knows that no problem is not solvable.

• 1:10Claire Vo?

  Welcome back to How I AI. I'm Clara Vow, product leader and AI obsessive here on a mission to help you build better with these new tools. Today, we have Matt Britton, CEO of Suzy. Now, normally we show two or three workflows, but today Matt's going to show off the one mega workflow that rules it all at his company. He's going to show you how to take a single asset and turn it into tons of go-to-market goodness, from emails to customers, enrich data sources, and even marketing assets that can be used to source more prospects that are going to be successful with your product. Let's get to it. This episode is brought to you by Brex. If you're listening to this show, you already know AI is changing how we work in real, practical ways.

• 1:55Claire Vo?

  Brex is bringing that same power to finance. Brex is the intelligent finance platform built for founders. With autonomous agents running in the background, your finance stack basically runs itself. Cards are issued, expenses are filed, and fraud is stopped in real time without you having to think about it. Add Brex's banking solution with a high-yield treasury account and you've got a system that helps you spend smarter, move faster and scale with confidence. One in three startups in the US already runs on Brex. You can too at brex.com/ how I AI. Matt, thanks for coming on How I AI.

• 2:39Claire Vo?

  I'm excited because as I was saying before we started the show, we get vibe coders left and right. And I know we're going to talk about some custom software that you built, but we just do not get enough on the go-to-market and marketing side of AI automation. So I'm really excited to show what you have to share. So really appreciate you joining today.

• 2:59Speaker 00

  I signed to be here.

• 3:01Claire Vo?

  So you and I both really love Zapier. And I have to ask, even before the age of AI, was this a tool that you relied on? Why has that this specific software become kind of the backbone of so many of your AI-based automations?

• 3:17Speaker 00

  So I've always been fairly technical, but I've never been a coder. I sold the first ads ever on Facebook directly to Mark Zuckerberg and Warner SEVERIN in 2005. I bought some of the first Google keywords ever to exist right when I started my business in 2002, my first ad agency. So I've always loved sort of ad tech and getting like 100% of these tools work. But at the same time, I've never been an engineer. And as I wanted to get more sophisticated in the in the tools and solutions I've built for various companies that I've run, I've needed to not just use one tool like AdWords, but multiple tools to stitch things together to be more efficient. And I was turned on to Zapier, like most other people just do a Google search.

• 3:59Speaker 00

  And I think I wanted to connect, you know, leads that were coming in through my website to some type of flow where it automatically emailed the person who signed up. And then I just kind of started to dive into it. But to your point, Claire, it wasn't until Zapier integrated AI when kind of my mind just became blown in terms of what's possible.

• 4:17Claire Vo?

  Okay, so you're going to show us how you take a single asset, and I won't spoil what it is, and turn it into basically a full suite of activities across your marketing, sales, internal company work. So why don't you pull that up and show us what you built?

• 4:31Speaker 00

  Before I pull it up, I guess as you'd say that I think it's all about figuring out what problem that you want to solve. And I think with AI in general, people get so overwhelmed with just the amount of tools and the rate of change that they just find themselves kind of playing around with all these tools, trying to get to the point where they feel like they're comfortable in understanding them, but at the same time, they're not really moving their business forward. And I think the reason that's the case is people don't ever take a step back and think like, what is the core problem that I need to solve in my business? Like, what's holding me back from growing faster than I want to? What's getting in my way? Or what's an opportunity I know is there, but you know, I'm not, I'm not able to take advantage of it.

• 5:13Speaker 00

  And with my company, what I was hearing over and over again was my sales team was consistently telling me that they just didn't know how to find anything. They didn't know how to find what customers were interested in. They didn't know how to find, how to speak to people of a certain industry or a certain title in terms of identifying use cases. So just so many unknowns. And so, once I understood or put my finger on that, on that problem, I just became very sort of tunnel visioned. And I was determined to figure out how I can build solutions that can aid my sales and customer success team to beat more than no. So, once you've actually identified the problem, the next step is figuring out what data can

• 5:56Speaker 00

  help you seize that opportunity. And in the case of, you know, understanding our customers and getting that information, it just so happens that since the pandemic, when our company went remote, we've been using this tool called Gong that's essentially attached to Zoom calls that records every single call that we have. So it says this call is being recorded for quality assurance purposes. And I always knew we had, obviously Zoom, and I knew that we had Gong, but what I didn't know is that Their transcripts were amazing and that we actually had 25,000 hours of call transcripts that had been amassed over the last five years. And if you think about understanding information about your customers and your business, there's no better source of truth.

• 6:38Speaker 00

  So we have since built an entirely operating system around this information, not just the historical information, but a variety of different workflows that happen with each new call that occurs, because it's not just about understanding what's happened in the past, but it's also being able to be highly responsive to what's going on in the present. So the first thing I'm going to show today is an automation that we have created based upon calls our, our teams have, either our sales team or our customer success team. So, and essentially what happened is as soon as that call is over, a series of events happen with that individual transcript. We also do things sort of at large with the aggregate transcripts, if that makes sense. But right now I'm going to show you what

• 7:22Speaker 00

  happens kind of like real time once a call is completed.

• 7:26Claire Vo?

  Great. So while you pulled it up, just to recap for our listeners, you had a bunch of salespeople. They said, I don't know how to find the information that I need. I don't know how to generate the information I need. I need more information to serve our customers better. You realize you had, and I'm correcting me if I'm wrong, 25,000 hours or something

• 7:43Speaker 00

  That's correct.

• 7:44Claire Vo?

  of recorded customer calls, which are the perfect source of truth for

• 7:48Speaker 00

  Yep.

• 7:49Claire Vo?

  how customers want to be interacted with. And you decided that was going to be the core context for a lot of these actions inside your company. And then you're going to show us a zap now that takes a single recording and does a bunch of stuff. I got a preview of this and it does a lot of things.

• 8:06Speaker 00

  I've, I tried to give AI to my engineering team to figure stuff like this out and it just became overwhelming to them, even integrating the product. And what's been helpful for me was first, Building things on my own and I'm not technical enough to be able to build on top of our software product. So the tools like the one I'm going to show you today was a great way for me to be able to dive into the power of AI. Because it was on the edges of the, it wasn't the product, it was sort of on the edges of how we operated. And through that though, I became far more adept at AI. And now I'm very much involved in our product itself. So often people struggle to find a way in and there's lots of different ways.

• 8:48Speaker 00

  And one way is actually building something for yourself personally, or building something for the marketing organization or somewhere else. And then through that process, you really start to get it and then you can start to be more proficient in AI and much more substantive ways within the business.

• 9:02Claire Vo?

  Yeah, and I want all the other CEOs and executives watching this podcast to listen to exactly what you've said. Because it is not sufficient to instruct your engineers to build AI.

• 9:12Speaker 00

  You'll go nowhere. Yeah.

• 9:13Claire Vo?

  No, you'll go nowhere. And I've said this a lot, this is a moment for actual hard skill building in leaders, which is you actually have accessible skills to build in. Using AI, building AI tools, using these sort of like no code versions of tools to really upskill yourself on the capability. And that's going to make you a much more relevant leader, much more.

• 9:35Speaker 00

  Yeah, you're opening up the hood. It's like you think about if you bring your car in and you don't know anything about fixing a car and they tell you $4,000 to fix the transmission. You're going to say, okay, because you need your transmission fixed, right? But if you actually just open up the hood and you understand how transmission works, even if you're not a mechanic. Maybe you can say, well, it really shouldn't cost $4,000 to fix this. It costs closer to $2,000. And I think that's sort of the same analogy when it comes to AI. So, so the first step is building what I call a trigger automation. And this trigger automation essentially comes from a tool that we've created called, that we use called Browse AI.

• 10:14Speaker 00

  So this is Browse AI and essentially what Browse AI does is it runs like a script where essentially scrapes information from GAN calls. So what you see here is a URL string. You need a URL string in order to identify a call transcript as it comes in. And GAN didn't have an easy way to do this. So I basically started to bring up a bunch of call transcripts. And what I started to see is they all kind of start the same way and they just end it with this call ID. So the only thing different from call to call was this call ID. So basically, I need to figure out, well, can I create a feed for Zapier?

• 10:55Speaker 00

  So new, the call ID of each new call as, as it kind of occurred. So the first step is essentially a trigger where a new call comes in, right? And then what happens is when the new call comes in, what it will do is it'll basically scrape the information from Gong. And one of the things Gong will give you is that call ID. So I'm able to actually see the call ID. So if I click here and I scroll over, you'll actually see that there's a call ID that I can identify here, which is right here.

• 11:31Speaker 00

  And so that appended to the URL essentially is all I needed to give Browse to be able to go to that URL and essentially scrape the call transcript. So it wasn't connected. I had to kind of hack it together. So if you'll see here, it basically knows what to run just based upon what's brought in. And then it will go to this page, which, which I will show you here, which actually is where the transcript is. And it's able to essentially scrape the entire transcript. So this is the raw transcript that's coming from the Gong calls by Browse AI going to that Gong page and just getting this information. But I had that initiated. So that first step essentially initiates the scrape.

• 12:15Speaker 00

  And then when the scrape is completed, it starts my next automation.

• 12:19Claire Vo?

  Yeah. And so just to call this out for folks that are trying to build their own thing, it's okay if your tool itself does not expose the data you want. In this age now, you can usually use another tool or an alternative.

• 12:32Speaker 00

  There's always a way.

• 12:33Claire Vo?

  Or an LLM to really pull the data you need out of, out of any system.

• 12:37Speaker 00

  Yeah. And I could have given up, Claire, like at that point, that probably one step took me the longest. And if I never would have gotten past that step. And I think a lot of people would probably have given up at that step. But after I got over that hurdle, then everything else became so much easier. And it's really like an analogy for life, like building something like this. And there are other stumbles I've had along the way in building things. But you just have to know that there's a way and using. And because just because a tool doesn't do it, doesn't mean it can't be done. And this like in the river mirror seems obvious. And now, if I had a similar challenge, I'd be able to do it right away. Because what will happen is every time you solve a problem such as that, the next time you need to build something, you'll have all these sort of ideas and like hacks

• 13:21Speaker 00

  in your tool chest, so to speak. And then now I'm at a point where it's like nothing you can tell me to build that I wouldn't know how to build because I just know how all these little things can be solved for. And you learn coding along the way, like along the way. You learn what JSON means and all these things by having your hands on it and creating the automations.

• 13:41Claire Vo?

  A hundred percent. What I was going to say is this is a CEO that I love. I love a CEO that knows how to build it. I love a CEO who knows that like no problem is not solvable. And I think just even getting hands on with some of these no code tools and these AI tools just gives you a little bit more context to be bolder about what you build. Okay, so you have.

• 14:01Speaker 00

  That's right. So the task is done, right? The call is done. And so this next trigger is to when browse AI successfully scrapes a call transcript. And the first thing it'll do is obviously it'll trigger it. And you'll see here, it'll give me the entire transcript of the call. And that's basically now it's like, okay, now I'm in business, right? Now I have everything I need. And there's a bunch of other stuff in that gun call transcript that I use to do database lookups throughout that we'll kind of get into. I'll have a delay of about two minutes before pulling in data like this, just because I want to make sure that all the data is brought in, the scrape is done. And I'm just, you're prone to errors, especially if you're running a lot of tasks quickly.

• 14:44Speaker 00

  If you don't put in a delay, so always like a one or two minute delay is a buffer just to let the system catch up so it doesn't break. So that's kind of self explanatory. The next thing I do is I run a format or I'm basically removing all the HTML from the transcript. So when you scrape, sometimes it'll pull in the HTML and I don't want that. I just actually want the actual text. So I run a formator step where I'm removing all that. I'm pulling out anything I need to that might confuse the analysis. So I'm just essentially getting the raw text. Then what I do is I start to enrich the data with other information besides just the Gong transcript because I have the Gong transcript. But one of the things I knew I wanted to build was after the call is done,

• 15:26Speaker 00

  I wanted to be able to tell the salesperson that was on that call what transpired. I want to make it easy for them to write a follow up email. I want to be able to identify who their supervisor was, right? But that wasn't directly pulled in through Gong. However, we have other data sources that essentially can connect that information. So we have a Google Sheet here. For example, if you look up this ID, it connects the ID to the brand and the brand to the user, which is a whole separate workflow that we created. So it can kind of connect the dots because when you're running an automation, you're not always going to get the data from the trigger. Sometimes you have to round it out and the way you round it out is using things like lookups on Google Sheets. So you're pulling everything in.

• 16:11Speaker 00

  It's almost like you're going down a path. You're on a hiking trail and you want to be able to pull the supplies you need along the way before you get to the destination. And when I started, I had a backpack, but the backpack didn't have water in it. And now I have water, right? Because I grabbed it from here and you're kind of going along a journey. And I personally, one reason why I love Zapier versus other tools is the way my mind thinks is in a very sequential framework where there's other platforms like N A and, you know, or BotPress where it's basically like, looks like you almost like an octopus, how it's branching out. I just have a hard time thinking that way. Now, over time, I've had to because I'm basically describing the difference between automation and agents. Because agents are not deterministic.

• 16:51Speaker 00

  Agents have different ways and my brain has struggled with understanding agents and I'm finally getting there. But basically the progression I see people having to take in AI is you start with using AI as a tool. You know, Chat GPT, give me a recipe for lasagna. Then it's okay, automation, which we're talking about now. And then you get into the world of agents where it's not just always going from step one to two to three. It might go from step one to three to eight based on what you're trying to accomplish.

• 17:16Claire Vo?

  Well, one tip for you or one tip for the listeners here I found is we'll go through this whole thing. And a good exercise I found is taking a sequential step based automation.

• 17:28Claire Vo?

  And trying to use, for example, Zapier agents and just describe that automation in natural language and steps and see how close we can get, even that replication across modalities can be a good way to to test your exercise.

• 17:41Speaker 00

  Yeah, just test it out. Hundred percent. Yeah. And it's looking up the information. So it's able to basically grab the information. And then after I feel like I've had all the information, the next thing I'm going to do is this where I'm starting to pull in the L L Ms. And an important part here is first and foremost, knowing what L L Ms to use. And one thing I've had a hard time with is actually just, we have so many automations now. I think we can do a better job, the organizational design behind it. Because what happens, I built so many things and I don't always do proper handoffs. So for example, here, it should always say use the latest stable version, but it didn't. Right? So, so I'm going to change it now here live on the spot because I want to be using the latest version. You also want to make sure you're using the best model.

• 18:24Speaker 00

  I still think G P T four turbo is probably a good model for this. But you could see in the platforming Zapier, there are multiple different versions that you can choose from based upon, and obviously they all eat up different amounts of coins. And but it's pretty incredible in terms of all the models. Now with G P T five, it's supposed to be able to choose for you. But it's unclear to me how that works in the context of an A P I. And for some reason, still in Zapier, you're still able to choose. And, you know, I spent a lot of time testing. I'll go in the chat G P T and test the sample input in a variety of different models to make sure it's like, whatever's the best output, the quickest is what I'll tend to use.

• 19:02Claire Vo?

  So you're still using classic G P T four turbo, a good old classic, classic favorite.

• 19:08Speaker 00

  Right.

• 19:08Claire Vo?

  AI is supposed to make work easier, but I've been there. Weeks of setup, endless back and forth with engineering, and yet another tool the team never really adopts. That's why I use Zapier's AI orchestration platform. It connects with nearly 8,000 apps, so I can finally put AI to work without the drama, without the delays, and without pulling engineering in every time I want to automate something. With Zapier, you can roll out AI powered workflows that do real work across your whole company in days, not weeks. I use Zapier every single day. It automatically responds to leads with enriched personalized data. It checks my calendar weekly and offers smarter ways to manage my time. And it even drafts emails for every new request that lands in my inbox.

• 19:50Claire Vo?

  All of that running quietly in the background, so I can focus on the work that matters. And Zapier is built for scale. With enterprise-grade security, compliance and governance, it's trusted by teams at Dropbox, Airbnb, Open Door and thousands more. Go to try.zapier.com/ how I AI to learn more about how Zapier can bring the power of AI orchestration to your entire org. Let's talk a little bit about this, this prompt. So tell me what first kind of summarization exercises you want to do here.

• 20:22Speaker 00

  Yeah, so basically here, and the reason I can use a model like GB, like, like a GPT four is, and, and, you know, part of it again is just keeping up with continuing to update the models, but I don't fix things if they're not broken. So this particular zap works perfectly for us and it gives us everything we need. And we don't need more rigor in analysis here because it's just some core things that we want to identify. So I'd rather not spend the extra money and even go through it. But at certain point, if it didn't work, I would look at models. So if we're going fast enough. What's interesting is that older models tend to work faster and faster and faster over time. And they actually error out less. And, and sometimes the older models get updated as the new models update as well. So it's not like you're, you're driving in, in 84 Chevy, so to speak.

• 21:06Speaker 00

  So here, this is a key step. This is called core summary generator. And what this is asked to do is analyze the customer success call transcript between Suzie and our client to gauge the health of customer relationships and identify improvement areas. Start summaries with the customer's company name, key participants, and then kind of going through it as to the key stakeholders. And then it gives a call overview. We've described the call's purpose, the main topics discussed and the outcome. Excludes small talk. And then I have a variety of instructions. Assess the overall customer sentiment, noting any frustrations or concern. Provide a sentiment score from one to 10, where 10 reflects high satisfaction and one indicates potential discontinuation of our services. This is the key thing because it allows us to quantify customer sentiment over time.

• 21:50Speaker 00

  And we actually benchmark this against actual churn. So, and it's been highly predictive in terms of the, if you take the average sentiment score of customer calls over the past year, it's a huge predictor of if the customer is not just going to churn or are they going to upsell if they're really happy. Also, one great thing the customer successfully did on the call, kind of identify that. And what are some things that they actually could have done better? And then list the next steps. So this is basically just an overarching prompt where it'll take a transcript and it'll identify all this information for me. And then that content I can do a variety of different things with, but it's a huge part of the overall app.

• 22:29Claire Vo?

  So one of the things I want to call out here as I was reading your prompt is in an ideal world, all your best CSMs are doing this after every call in a perfect way, with great, you know, objective self-evaluation, all this kind of stuff. And the reality is, we're all so busy that, you know, your customer success folks, your sales folks are probably going meeting to meeting, meeting. And at the end of the day, trying to figure out their notes and put little things in. And I just think what's nice about this is you can make the customer success or account manager's job a lot easier and let them be exceptional at their job by automating some of the work that they, they would do. And so I think it's a really good hygiene step for anybody to think,

• 23:15Claire Vo?

  you know, after I'm coming out of a meeting, if I were to do my best job possible, what are the five things I would do coming out of each meeting? And then just automate that for yourself and then you know that every time you're going to be doing that.

• 23:25Speaker 00

  Yep. So the bunch of other steps I have, and I'm not going to go through all of them because there's a ton of them. But basically it, it looks up the user on Slack. So I understand the user meaning that our employee on Slack. It identifies that people who aren't from our companies, you can kind of exclude them. It's able to find the user here. So I use Slack as a, as a lookup sometimes because our company's entire directory is on Slack. So if I'm trying to get someone's email address in automated fashion and I have their name, I can actually use Slack as a lookup tool in Zap without even actually posting anything to Slack. So sometimes these tools actually can be used for other purposes. That's not their core purpose. And then basically the first, one of the main things I do from this is I send a channel message.

• 24:09Speaker 00

  So basically after the call is done, you could see new customer success caller as the account, the opportunity, the leader of the call from our company, the date of the call, and it basically has that summary that gets sent out. So we have a, we have a channel. That's a constant feed that I, obviously the CEO, I'm very attuned to and I'll share it right now, where basically every time a customer call is done, it just pops up on Slack. And I'm able to really, you know, we have 300 employees at our company and I'm really able to get a sense of the kind of pulse of the company, what customers care about, just based upon looking at something like this. And it's, you know, that alone, if that was the only invention that came out of a hi,

• 24:54Speaker 00

  it would be pretty incredible if you think about it. And this is just like one of many things that we do. So I'm going to pull up in Slack right now and you can see here, this is the sample call and it shows you the key stakeholders were, what the call attempted to establish, what was the sentiment score, got an eight, right? Opportunities for upselling, feedback and next steps. And it has basically a transcript here and it's great for us to do. If a customer is not happy, right? If they, you know, for some reason, score below a seven, we have a churn notification channel, where basically it's called churn early warning system,

• 25:36Speaker 00

  where it'll tell us if a customer is not happy for whatever reason. And we've had to modulate it because sometimes a client will say they're not, it'll say the client's not happy, but maybe they're just not happy with how their business is going. So it's not always like a science. And then in the channel, sometimes the rep will say, oh no, they're fine. It's just this, but we have in many instances. And to your point earlier, like sometimes the rep might not want to tell anybody, right? Maybe it's a Friday afternoon and you just don't want to deal with it. And then what happens is we end up forgetting about it. And then the customer churns three months later and we're like, why didn't you just tell us? We don't have to do that anymore. We don't have to ask somebody how that call went with Procter and Gamble. It's just here.

• 26:17Claire Vo?

  Yep. Okay, great. So. You take the transcript, you post all of them. So everybody in the company has access to customer calls and summaries, which is just great for general sentiment analysis, knowledge sharing, transparency. You take any ones where the sentiment analysis is low and you put them in sort of like a warning area churn alert channel where I'm sure you're paying a little extra attention. So you can get ahead of potential churn risks, which as a B2B girl, I really, really love. And then, so that's, that's a little bit more like the account ops side of things, but then you take off a bunch of marketing.

• 26:52Speaker 00

  Yeah, there's a bunch of other things. Yeah. So this next one, again, is all part of the same automation, is another LLM call where we're basically describing what Susie does. And we're saying, analyze the key areas of interest data in the transcript and output a bunch of keywords that we should be buying in Google. So if customers are using words that we, that are describing what they're interested in or what we sell, and we're not running Google keywords for them, we want to. So basically, these keywords will be mentioned, we extract them, and then we run an automation to add those keywords to our Google campaigns automatically.

• 27:30Claire Vo?

  So not only are you taking sort of, this is, I love this one. So I want people to pay attention. So not only are you taking the account level, specific context, but you're saying, our customers will tell us in their words, what they're looking for, what problems they're trying to solve. These customer calls are a rich source of market insight. And so you're going to use these customer calls to actually extract out market surface areas, keywords, places where you can put marketing dollars against, and then reach customers similar to the customers that you're successful with, which is a really nice closed loop solution. And again,

• 28:07Speaker 00

  That's right.

• 28:08Claire Vo?

  You know, we were talking about how this note summary is the way, in an ideal world, a customer success manager would provide notes. In an ideal organization, your, you know, paid search manager would be monitoring all these calls and doing all this for you. But we don't live in ideal worlds and people are busy. And so again, this is, is not only designing from the point of view of like, what would a person do, but also what would a team do?

• 28:34Speaker 00

  That's right. The other thing we do is we've done a coach into this. So the next step essentially is called individual call feedback. And what this does is it actually creates a feedback note to the person on the call. So this just goes to the sales rep on the, or sales or customers just saying, here's what you did. Here's what you did right. Here's what you did wrong and actually sends it to them right afterwards. So they understand how to get better. Which is something that we would have had to hire somebody to be on their back and tell them, which they know on their own. What's interesting is like, the people that really want to get better, this is AI is an incredible tool because they're going to want this feedback. And the people who never really want to hear from anyone to begin with, they're not going to want to hear this, but

• 29:17Speaker 00

  they wouldn't have been good in either way. So that kind of goes to the point that like, it's going to make the good people that much better. Right? And we add this to a data set. So we have a feedback called data set. So we can actually see are there trends? Like is AI detecting if this person always cuts calls short, or they always interrupt the customer, or they don't talk about? And then when it comes time to reviewing them, it's all data driven. It's not just myopic. If managers change over, we have all this information and the good ones want this information.

• 29:48Claire Vo?

  Yeah. What I was actually going to reflect on is you're talking about this from the point of view of the individual contributor, the CSM, the AE. But what I was thinking is so much of AE and CSM performance is really gated on, do they have a good sales manager coach? Do they have a good SVP sales that can actually provide them targeted coaching on all of their deals? Right when it's relevant. And this sort of like evens the playing field. Your manager could be great. Your manager could be terrible. In every call, you're going to get kind of objective feedback on your performance. And so, again, it helps up level the performance across the organization.

• 30:24Speaker 00

  And it's democratized. You're right. A hundred percent. The other thing we realized, going back to problem solving is we heard from our sales team and our customer team, you know, it takes so much time for us to write a good follow-up email after the call. So now we add a follow-up email writer where essentially you write an email that they would want to send as a follow-up to the call. And actually just and designed very well and it's sent to them for them to basically copy and paste it and send it and it's just a way for them. So right after their call, they'll get the feedback in their inbox and they'll get this email and they can copy and paste and send their edit and you know, we could have made this automated, but you know, that's where the human in the loop matters, right? We don't, what if the context is wrong? What if they don't want to send the feedback right away? What if they want to copy somebody

• 31:07Speaker 00

  new? So that's why we have to have a human in the loop here. So the churn only warning detector basically sends through two different paths and these paths essentially um kind of dictate who we should notify and who we shouldn't. So we've also now started to do much more marketing driven things from this data. One of which is we start to create a database. This is called customer profile database. And what customer profile database does is essentially structures data after each call with things like what's the role of the customer? What product areas of Suzy are they most interested in? What business trends are they most interested in? And we have a structured database across all the

• 31:47Speaker 00

  calls which gets fed into a GPT. So if a salesperson is going into a call with a brand manager of an automotive company, they could say, what are the things that brand managers of automotive companies are most interested in in terms of trends of interest of our product? And it'll tell them right away because the data in aggregate is stored with a different tool that we deploy. So again, not only do we have the automated things that are happening, but we have this aggregate database that we unlock the value of on an ongoing basis.

• 32:16Claire Vo?

  Okay, I have to ask you a question again as a B2B enterprise girl. Are you using a CRM? Like, is this data going into Salesforce? Are you like?

• 32:24Speaker 00

  Yes.

• 32:25Claire Vo?

  Look, it can all go in Google Sheets. We don't care. I'm just curious.

• 32:28Speaker 00

  Well, I mean, it's, you know, today goes in the Salesforce, but you know, I think the reason Marc Benioff is leaning into agent forces for that reason, right? It's like, what's the point? Right? So like theoretically, everything I'm building right now is a better, what I just showed you, I believe is a better version of Salesforce. And guess what? The salesperson doesn't have to enter a record. It's entered and the manager's getting information and they can chat with the data and they can pull reports and aggregate. That's basically what Salesforce was built for. And you know, from a meta standpoint, our company is facing the same thing with market research where like we built this. So we're all trying to figure out how to disrupt ourselves based upon what's happening. But you're right. I mean, and that's sort of the fundamental issue that exists today.

• 33:10Claire Vo?

  What I was reflecting on though is one of the challenges with Salesforce. Well, you know, one of the reasons Salesforce did so well is because of the flexibility of implementing your own data schema and kind of

• 33:19Speaker 00

  Yeah, yeah, of course.

• 33:21Claire Vo?

  And one of the limitations is like, gosh, you have to go through your Salesforce admin to like, set up anything and get it.

• 33:26Speaker 00

  Right.

• 33:27Claire Vo?

  You know, and then

• 33:28Speaker 00

  And the charts and graphs weren't great and no one knew really knew how to, I mean, you just sometimes want to know like, what's the status of the P and G account? It's what you want to know and it's just good luck getting that done. Or right now you could just literally just speak it or type it and you get it. And that's kind of where we're all heading to.

• 33:44Claire Vo?

  Yeah. And then what you're showing is you could create these one-off loosely structured Google Sheets, for example, for different various lookups. They don't have to be perfect. They don't have to be hardened in your CRM, but they're useful to your team. And I think

• 33:57Speaker 00

  Well, it's structured. It's a structured database, which, you know, I think, you know, for RAG, structured databases work much better. And this is a structured database and that's really all you need. I think a B point here, it goes back to what I mentioned earlier is you just have to find, it, it's not about the tool, it's about the data. People are so focused on the application layer, it means nothing without the data. And to me, it's like, this is the ultimate source of data and this is the treasure trove. And this is people in the wild saying what they want. So I want to build everything on top of this data. So that's why when, when we were prepping for today's interview, like we'll show a bunch of different things. And, and the way I look at it differently, I'm going to show you one thing that has many different tentacles, based on the most important thing, which is what our customers are saying.

• 34:41Speaker 00

  And that's a different way of looking at it.

• 34:43Claire Vo?

  Yeah. And I want you to show one more sort of marketing use case off this

• 34:48Speaker 00

  Yeah.

• 34:48Claire Vo?

  master workflow. But while you're

• 34:50Speaker 00

  Sure.

• 34:50Claire Vo?

  that up, what I might encourage people to think about is think of yourself as a single workflow. Think of your team as a single workflow, maybe even think of your company as a single workflow and figure out how that whole thing should work. And then work your way into some of these automations is really interesting as opposed to these little micro task kind of style things you can really ladder it up to. What's the step-by-step process this, this team should follow? Given a certain task. And so I think it's really interesting that you have this, this mega automation as opposed to these one-off, one-off things.

• 35:25Speaker 00

  So the last one I'll show you, which is, this one was controversial at first and We required massive testing to push it live, which is, so we speak to somebody, a financial service brand, and they talk, Suzie, the market research company, right? So we compete with companies like Calltricks and SurveyMonkey, et cetera. So we're gonna have a, we had a call in, with a financial services company and they wanna name a new product, say it's a new credit card or something. That's a use case that other financial services companies might wanna use us for. Now, obviously, we can't share that X bank is thinking about renaming something. So we, but we wanna share that Suzie can do this new use case.

• 36:08Speaker 00

  So what we did is we built an automation where it basically extracts any identifying information from the call. So basically, that includes the brand, the brand name, any specific strategy that the company had, anything that's identifying to them at all. We redact, and we had to test it over and over and over again to make sure that nothing could get through. That could be, we'll lose customers and we breach confidence. We can't do any of that, but at the same time, if a salesperson just talked to a beverage company about, you know, testing packaging, they're very welcome. The next call, say, yeah, I just spoke to another company about this. And that's kind of what we wanted to have a programmatic approach to.

• 36:48Speaker 00

  So what this does is it'll take those transcripts and it'll write a blog post that fully redacts all that specified information, but focuses just on the idea of what we talked about. It'll create a graphic, a headline, it'll even create a custom CTA at the bottom, and it will, and it'll optimize it for SEO and it publishes it on our blog. And it publishes it 21 days later, which is just something that we want to do to even make sure to the nth degree that any privacy or anything. So we, but now we have 10,000 blog posts that are created on the calls that we're making without any human intervention. It just goes, it goes and goes and goes, every single use case that you can think of.

• 37:33Speaker 00

  And now we're running ads against these through Google Dynamic Search Ads. So, you know, and we're starting to get now, it takes a while to gain SEO traction with stuff like this, but even before that, now if someone searches for anything that Suzie has possibly talked to somebody about, we have a blog post up there and we run ads against it.

• 37:51Claire Vo?

  This is, this is amazing. I love this. This gives me so many ideas. And what I like about this is it's taking, again, your richest source of insight about not just what a customer wants, but what the marketer wants, what the market wants, and creating assets that then you can use to go reach similar customers with similar problems. So again, like your most successful customers are going to look like your most successful customers. And so you want to go find more, more of those folks. So again, to recap for everybody, a single gone call generates a summary, a Slack post, a churn risk alert, a follow-up email, a coaching email to the CSM.

• 38:34Claire Vo?

  It enriches a bunch of data. It sends out all the automations. It identifies keywords for you to bid on and then it generates content for you to both bid on and send paid traffic to, but also generate to get organic traffic going off one call. So the other thing I want to call out for people is in this age of AI and automation, you can take a very simple asset and extract the like nth degree of value out of that asset, which I think is such a useful and helpful workflow for people. So Matt, we had, this is a how I AI first. You have created such a big workflow that we have only shown one.

• 39:16Claire Vo?

  And I think that's enough. And we'll have people reach out to you. I know you have a couple of other really interesting workflows, but we're going to get you back to building Zaps and running this amazing team. Before I let you go, let me ask two lightning round questions and then we'll, we'll get you out of here. One is, you know, as I've been reflecting, this is a good reflection of how great individual contributors work or how great teams work. How has this changed how you think about building the shape of your team and your startup right now?

• 39:45Speaker 00

  Yeah, I think it's far more individual contributors, far more people who want to put their hands on keyboard, people who are willing to learn, people who are motivated, ambitious, that are, that are proactive at finding solutions. I think those are the people who are going to drive the next great businesses, not order takers, not people who walk in the work every day and wait to be told what to do. You could just you could you could just solve what I'm able to do if I tell AI what to do. So I don't need more people to tell what to do. I need people who are going to come up with new ideas and solutions and be proactive.

• 40:19Claire Vo?

  Yeah, what I say is this is the age of the super IC. Like, if you can be a super IC, you are going to go so so far. You know, second question, who do you think should own this inside your team? I know you're building a lot of it, but is this a role? Is this everybody's job? Who do you think needs to be thinking about building these kinds of automations?

• 40:38Speaker 00

  Well, I think that you need like a couple of go to market orchestrators that are almost like general contractors that are looking at the blueprint of all different automations. But then I think you need people who are owning the output of those automations. And so the marketing team should know the output of the blogs. And if that's not working, they should go to the, you know, the automation team and say, well, this is breaking. How do we make it better, etc. I think that's the best design. But it does require definitely new roles in the organization.

• 41:06Claire Vo?

  Yeah, for sure. And then, of course, the last question, which is prompting techniques. When AI is not giving you what you want, what do you do? Maybe in chat GPT, like, do you bribe? Are you an all caps person? What do you do?

• 41:21Speaker 00

  I have a framework where I first set what I'm trying to accomplish. And then I kind of set the framework for the prompts, like, almost like guardrails. Like, here's what not to do. And then and then I think for me, telling it what not to do is a great way of kind of eliminating the issues I see until I get close. And when I get close to it, outputs on the actually want, then I refine what I wanted to actually do. And I think that's generally how I go about it.

• 41:49Claire Vo?

  Okay, so you're doing guardrail prompting. Do not do in addition to this is what you to accomplish. Well, Matt, this has been amazing. I love this. I'm actually going to go steal a bunch of your ideas for my own.

• 41:59Speaker 00

  Please do.

• 42:00Claire Vo?

  Enterprise pipeline. Where can we find you and how can we be helpful?

• 42:03Speaker 00

  You can find more and more about me at mattbritton.com. I just wrote out a new book in May called Generation AI. So definitely check that out. It's about generation alpha and the AI generation. And then you can learn more about my company, Suzie, at suzy.com, S U Z Y dot com.

• 42:20Claire Vo?

  Well, Matt, I really appreciate it. Thanks for the time.

• 42:22Speaker 00

  Thanks so much, Claire.

• 42:25Claire Vo?

  Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiaipod.com. See you next time.