The GTM System That Actually Works: On Marketing for Marketers

Welcome to another episode of Marketing for Marketers, the series where we interview some of the brightest minds in marketing. That's usually where people smile because they're like, ah, I'm the brightest minds, that's interesting, to unlock the strategies that actually generate pipeline. So my guest today is Spencer Tahil. He's a seasoned GTM and RevOps strategist with deep experience in systems thinking, cross-functional growth, and AI enablement, which I think we can all agree is very, very apt at the moment, very, very hot. So from building scalable marketing and sales ops infrastructure to leading RevOps at high-growth companies. Spencer brings a unique systems-first lens to everything from AI to attribution. Spencer, first off, welcome. I'm very, very excited to dig in what it really means to build for scale. We see a lot of stuff on, on LinkedIn, the majority of which is probably rubbish or jargon. So I'm really interested to get into the meat of things that say, especially when AI and GTM alignments are shifting so rapidly. How are you today though?

I'm well, thanks for having me. Uh, appreciate it. And, uh, definitely echoing that comment on LinkedIn. I think there's a lot of rubbish and a lot of American English slang of just trash floating around on LinkedIn. All of my clients are in LA, so it's, uh, I'm usually up at that time and yep, nope, it's pretty much trash. Everything on LinkedIn other than the, the occasional deep dive into like an AI prompt or something like this. So there are some good people to follow, but other than that, Thank you. Nailed it.

So we're obviously going to be talking everything systems thinking, cross-functional growth, and AI enablement, which is something that we're massively focused on at the minute. We have this big, I think they're calling it an AI push program internally, which is essentially enabling and empowering and giving confidence to everyone internally to adopt AI, implement AI, work with AI, and everything in between, which is exciting. So I'm very, very interested in this conversation. We've got 8 questions. We don't have to get through all of them. The first of which is a pretty, well, it's a big picture question. So in terms of that, what do we mean by systems thinking?

So systems thinking, it's, I used to study engineering. A lot of people don't understand that and I don't really make it public, but systems thinking is understanding not just the input and output of a specific system. And it's understanding that there are different variables that can affect the final outcome. So when we start to look at something like marketing or we start to look at things like management or pipeline or revenue activation, there are different systems that are coordinated together to get the output being business revenue, right? So when we start to break things down on the management of the pipeline level or the revenue activation level, it's important to understand that the pipeline is not just a single system. It's not a linear, it's not a line. Right? There, there could be different marketing funnels. It's called a funnel for a reason because you go from the top and it slims down to go to the bottom. But once you get to the bottom of the marketing funnel, go, it goes to a sales funnel. So it's, you know, it's almost like an upside down two triangles stacked on top of each other. So it's important to understand that there's a marketing system, a sales system, a pipeline management system, a revenue system, and then there's the actual customer success or the onboarding systems that are all part of the greater system, for the lack of a better word, right? So when we start to think about AI, go-to-market, revenue operations, and usually systems thinking, it's important to understand how everything is coordinated together in some sort of way. And just as like a really good example of this, I do a little bit of coaching on the side, and a lot of my students ask me, how do you start to understand how a CRM works? You know, this is a very prominent question in the B2B world is how do we set up the CRM to activate the most pipeline? When I do the audits, there's only 3 things that I really look at to start, and it's to understand the relationship of associations. And by that I mean we look at the 3 main things that we need to close a deal. Usually I'm just saying B2B, a contact record, a company record, and a deal record. And there's different levels of associations. There could be one-to-one, one-to-many, or many-to-many. And once you start to understand how a specific business operates from its core system, you can start to branch out and build the other augmented systems around it to be able to help enable success. So for instance, marketing can give sales more information, that's called sales enablement, and then sales can give revenue teams more information about attribution. That's usually dashboarding reports. So in this way of thinking, you can see that we've just gone from, you know, line to actually having different multifaceted records or different parts of the puzzle all connected together. And I would actually probably describe that as like systems thinking in business.

Interesting. Okay. So you, you spoke a lot about alignment and the relationship between marketing and sales and revenue teams. You spoke about the two triangles, the funnel and an upside down funnel, whatever you want to call it. The bow tie. I've heard that before in terms of that sort of diagram. So you've worked across both strategy and execution. So what With respect to that bowtie, what's the biggest disconnect you see in terms of what actually gets built?

I think it's— I'm probably going to piss a lot of people off when I say this.

Fantastic. We'll clip this bit up.

Biggest— I think the biggest disconnect between marketing, sales, and revenue is that sales overpromises and that the teams usually don't deliver.

I agree. If anyone in our sales teams listen to this, I am kidding.

Yeah. I mean, listen, like you see it everywhere. You see it in this consulting world. You see it in marketing agencies. You see it in people that, you know, unfortunately, you know, you have like offshore talent. You know, this is just how the world works. And at the same time, even if you're having onshore talent, the problem still exists. You want to close a deal, you want to get that million dollars, you want to get that $10,000, whatever the deal size is going to be. So you're going to promise the world, right? I think the real power and the real disconnect is when people start saying no. And that actually becomes the savior of your pipeline, because when you can start to prioritize the accounts, when sales team knows what they should be doing, they understand the product, they understand what the marketers need to be able to do and what type of information they need to be able to go and get them to enable success. The sales team inherently says no, nope, that's not a good fit. Nope, that we can't activate that. That's not worth our time. That's a tier 3 account. That's not a tier 1 account. So it's the power of saying no. The application and that coordination to follow up on a go-to-market strategy plan becomes a lot easier. Because you're now, you're working in your zone of genius. You're working within your own mind. You're working within the strategy that you've put together, the one that you know that you can actually succeed with doing. And you see that with a lot of like really big companies in like the Claygency world, right? There's a lot of different agencies. I'm very close with a lot of them. What a lot of people are doing is they're putting together programs and, and what they do is they put people in different boxes, tier 1, tier 2, tier 3, and then it's like enterprise grade. It's the same with SaaS companies. It's, you know, you're buying a tool, you're buying all the features. You might not use all the features in, you know, let's say a Salesforce or HubSpot plan, but that's what makes it good because you have access to all of it. If on the agency model or on the business model, when you're deploying a go-to-market system, when you're going from strategy to execution, you need to be able to execute on what you're really good at. And if sales team over-delivers and, you know, the operations can't catch up, this is when you start to see bottlenecks. And this is what keeps a lot of agencies and a lot of consultancies down on that, on that level where they can't scale because they can't operationalize, they can't understand the fluidity of the situation of the strategy, and they can't adopt the systems that they've actually built to scale with the strategy. So you should build the strategy around something that you really enjoy and that you're really good at and that you're passionate about, for the lack of a better word, but also operationally that you can in a way systemize. I, I do discovery, I do a phasal approach. It's phase 1, phase 2, phase 3, and I do 30-60-90, but I might not do the exact same steps in 30-60-90, but I know within the first 30 days I'm gonna do some component of auditing, some component of setup or architecture design. In the second phase, I'll do some sort of consolidation, you know? So I think when you start to operationalize the strategy, and you can start saying no, I think that's where the disconnect is. And I think to start thinking in this sort of way, it takes just experience. It takes practice.

And you, you spoke about that 30, 60, 90-day, the auditing, building that architecture. What, what actually, I think a lot of people are scared or anxious or feel this external pressure in terms of actually starting. What in reality, what systems need to be in place before AI can actually be useful? Processes.

I think, you know, I, I have Claude on my phone. I'm not gonna make a recommendation, but if I had to make one, that would probably be my, my LLM of choice just because it feels good. But I think understanding where AI has a place in the world and where AI has a place, especially in business, and if we're gonna stay on the top of a go-to-market strategy, it's understanding that AI will always be smarter than you. We have the internet in our pockets. I grew up telling, like, He told me, you won't have a calculator. You need to do long division by hand. And it sucked. And now I have the power of the whole internet and the whole community on my phone. So once you start to understand that AI is vastly more powerful than you, it's vastly smarter than you, you can put your whole ego aside. You know, like sales leaders, executives, even consultants like me, like I can spar against the minds of 20,000 people to dissect a very specific process at HubSpot. I don't know that a lot of people understand that you can screenshot things and you could put it into an AI model and have it dissect it and you can talk. I open up my phone and I turn on voice and I just talk to my phone and I say, hey, this is that. That's how I use AI. But when you start to look at go-to-market strategies, it's describing the context, it's describing the limitations, it's describing the goals of the situation that you want to actually operationalize or that you want to deploy in the process and letting the AI model or where whatever system that you create, the system prompt, the user prompt, whatever you're gonna use AI to do is that it's like you're teaching a toddler. You have to tell it, hey, you know, you are, you are a 5-year-old. You have the ability to do these things. You don't have the ability to do these things, but I want you to take the circle and I want you to put it in the circle hole. And if you try to put it into the square hole, I want you to take it out and put it back into the circle hole. Here are the good example videos of how you can know that the circle is circular and the circular goes into the circular hole. That explanation is a few different concepts of prompt engineering where we can apply that logic into teaching AI to do very specific tasks. If I want to emulate my voice to do marketing content, for instance, which I'm actually working on now. If I wanna emulate it, I'm gonna take 100 of my sales calls, 100 of my coaching calls, and 100 of calls that are recorded like this. And I'm gonna say, triangulate all of the information about my voice and the way that I speak and the way that I enunciate. And then I wanna apply that knowledge base into whatever I'm doing. So again, going back to the systems thinking, we have the knowledge base and then we have the application layer. And then from there, in the go-to-market strategy, if we wanna apply AI, it's just a matter of saying, Behave like X, meaning the knowledge base, behave like X with the limitation of doing Y and the goal of doing Z. Behave like Spencer Tahil, a seasoned XYZ with the limitations of only writing content for LinkedIn. I want you to make a goal to go talk to the CEOs and CFOs of SaaS companies and marketing agencies, and I want you to do it with this tonal analysis. Here are 4 examples of the articles that he's written on LinkedIn. From there, it's like teaching it to a very, very smart toddler. And that's how you would basically apply AI procedurally from beginning to end. And that's how I'm doing it realistically.

Okay. So you've given us some really, really good examples there. In terms of tooling then, how do you approach building a RevOps or GTM stack that's scalable, measurable, AI ready, like any key principles, mistakes to avoid?

Let me just think of like the best way to like formulate this. If you hear me saying HubSpot, HubSpot, it's because I, I do a lot of work in HubSpot. I use Salesforce too, but generally speaking, every business should have a CRM no matter what, right? Like if it's Google Sheets, if it's anything but a sticky pad on your desk, I think you're doing great for yourself. If you want reporting, if you want something that's measurable, then you need to know what you're measuring. And so I think defining firstly, defining what success is going to look like and what the stages of success are going to look like. So in revenue operations in HubSpot and Salesforce, we have all of these different types of report. It's like you have 1,000 leads and then all of them are in cold stage and then, okay, we've had meeting discovery or we've had demo booked or whatever the stage name is gonna be. The name of success, it's how far along the succession pass path that individual or company is. And I think roadblocks to definitely avoid when you're trying to get the revenue operations and the go-to-market operations stack correct is not knowing what you're measuring. Because when I go and talk to the sales team, for instance, when I go and build a go-to-market stack with something like N8N, Zapier, Clay, Dealfront, any, anything like that, realistically, I need to understand what data matters. It's a matter of need versus want. And this is like something that my grandmother always taught me. Like, she's no longer with us, but like she always said, do you want that toy? Do you need it? And I take that with me and it's like I ask my sales, the sales guys or the marketing or the growth teams or revenue teams. I said, is that data what you need to close the deal? Or do you just want it? And when you start to discern what you need for a deal, then you can start measuring it because it makes sense. The revenue team will understand that actually they don't need to spend as much money on a stack if the only data that they need is company name, first name, whatever. Sales enablement comes into play and everything gets fancy. You go and enrich everything and then you pay for the data that you don't need, right? You want it. It's fancy, it's shiny. So definitely roadblocks to avoid are spending a ton of money in your go-to-market and revenue operations stack and paying for features and paying for data that you don't need. Do you want it or not? That's the question you should be asking yourself. And it doesn't matter the stack, it doesn't matter the way that you think. It comes down to that way of thinking.

Yeah, it's interesting. I think we're going through an exercise at the minute internally where we're I think most companies do this sort of every year, but we're looking at our tech stack, looking at our systems and thinking, if we got rid of those three and replaced it with this one, would it bring teams closer together? Would it create more unified thinking? So we don't have to try and connect multiple systems in three different ways into a new tool that we're bringing in to unify things. Like, it's just overly complex. We're going through that at the minute. So this, it makes a lot of sense. And I think with every new employee, every new senior leader, you're going to have different data sets layered on top of another. And it's that age-old thing of why are you doing that? Or why do we need this? And it's, oh, it's just, you know, when I joined, they were doing it. So I've always done it. It's like, do you need to keep doing it? Does it actually influence any decisions? Yes, no. And more often than not, it's no. We spoke about measurement there. You said once you understand the need in terms of the data that you need, you're able to measure it. So measurement is always a big, big challenge in go-to-market setups, whatever type of measurement I guess more so for marketing attribution, things like that. But what is your approach to attribution, performance tracking, and showing impact? What is actually worth tracking and what's noise? I appreciate there might be slight repetition on— if you know what you need, you measure it.

We— so I will specialize in this answer predominantly in somebody that's working in outbound, inbound, outbound, and funnel activation. My definition of success might be a little bit different than somebody like an ads manager, right? So just focusing on the idea that I'm the one that's probably coordinating all the outbound and ABM efforts, that's where my, my expertise would lie in that regard. The general landscape of how to set up the report and what matters and how to ensure proper tracking throughout the funnel. It comes down to setting custom fields and custom properties. So like, I love HubSpot, but you can do the same thing in Salesforce and Adio, whatever CRM that you're going to be using. If you understand what the lead source is natively, every CRM will have lead source or they'll have a source column or whatever the property name is going to be. If you can set this, but expand it somehow, like 3 different ways, it just says original source, like original source might be LinkedIn, but what does that tell you? It doesn't really tell you granularly where that link, like LinkedIn organic, LinkedIn ad, whatever. Right? So what you need to, need to put are UTM campaign, LinkedIn campaign, ad campaign 1, variant 2, put out on this date, right? Here's the ad copy, here's the hook. And I think when you start to understand the granularity of the situation, whether that's going to be in ad copy, ad management, SEO, what, whatever field that you're going to be in, your definition of success is gonna be different, but your way of measurement should always be the same because there's no way to compare apples to oranges if you only— you have to have the same fields. So I think that also going back to the idea of knowing what you want to measure and what is important, if you're tracking attribution of an outbound email marketing program, like something that I'm running for people, then you need to understand, hey, are you running signal-based campaigns? Are you running cold email-based campaigns? Are you doing a mixture of both? And granularly, What does that look like on the reporting front? Does that look like you need to have the campaign ID that's sitting in something like a sequencer, like Instantly Smart Lead, something like that? And then you need to see the variant. Do you need to see the hook? Do you need to see the call to action? What is the angle or what is the offer that you're going to market with? Is there a promotion in there? The same type of stuff that a lot of marketing strategists are going to have closed-door conversations about on let's A/B test this, you need to measure that. What's the point of an A/B test if you can't measure the actual variables? So this, for example, this was actually a conversation I had yesterday. I was with a client and they— what my— our hypothesis that we're testing this coming week or these next 14 days, we're sending emails, right? They have a target list of accounts and they said, well, why aren't people booking calls? Because I'm not testing that. Like, yes, like we're in the testing period. I want people to, to book meetings in, right? That's the whole goal. But I can't have somebody make an action unless they're opening the email. And so what I will always do, a really good A/B test example for outbound email marketing programs, uh, to test for opens first rather than actions, because they can't take actions unless they open your email. So you need to test what variable? The subject line. The subject line is most likely going to get them to open the email. So what you should do first is you should, in my philosophy, is you should test for opens and test 3 or 4 different subject line variants. Get a concrete knowledge base of what is resonating with your audience first, and then keep every other variable the same. Subject line variant 1, variant 2, variant 3, and then the same body email, same CTA, same body text. Some syntax in there, but whatever, just general gist of it is the same, but the main variable that you're changing is going to be the subject line. Once you have a concrete measurement of that, you can go on to test other variables and you can do that through setting everything up through custom fields, going from either something like Clay into HubSpot. You can do kind of like webhooks from Zapier into Salesforce. It doesn't really matter what your stack is, but going back to the idea of knowing what you want to, uh, to, to measure. And what you want to compare across the different initiatives, whether that's going to be lead source, UTM campaign, whatever it's going to be, ROI, that's really important for like revenue operations leaders too. So I hopefully answered your question there. I'm not sure if I hit it on the head or not.

Yeah, no, you've given some really good real-world examples there. I think in terms of real-world scenarios, then we've chatted through a lot of hypotheticals. We've mentioned a few different tools. Let's talk real world then. So give us an example of where you've built or optimized a GTM or RevOps system and result, the impact. So give us the problem, the solution, and the impact for a system that you implemented.

Just scrubbing my brain of all the confidential information. Okay, so there's two, and I let you choose depending. Do both. That's too long to do. Okay, fine.

No, we've got time. We've got time.

So real world situation, two, there's two real world situations of the client base, like of my client base right now. Usually I'm working with marketing agencies over the $25 million a year mark and anywhere between and, and $35 to $40 million. That's usually where they lie. Uh, and then the secondary person that I usually work with are Series A, Series B startups that have the funding and they want to go to market quickly, but they don't know what their messaging strategy is going to be. They know that they have PMF. Product-market fit, they don't exactly know how to package it, right? The larger companies, they know how to package their offer. They know how to do the promotions. They have the ad spend. They have the ROI of the other programs that they can push into initiatives. Series A, Series B startups, they have the product. They just don't have the packaging. And so when we start to look at the two different examples, you could feel free to interrupt me. The first example that I was able to design a go-to-market and revenue operation system for was, I think they were like a $35 million a year marketing agency. So pretty big, very well known, has a ton of case study examples on their website, very well known in their general area and nationwide in the United States. What they really struggled with was a disjointed system. So the situation at hand was that we had two CRMs basically. Uh, we had HubSpot and we also had Close, and this could be any mixture of information, right? This could be Salesforce, HubSpot, whatever. The whole point here is to understand that the situation was that leads and contacts, leads and prospects were all separated across all environments and they weren't referencing each other before going and being sent out on their outbound email campaigns. So what that leads to is that a lot of people that are being email marketed to and cold is that Some of them were in the pipeline. So basically my job was to come in and to understand, my first task was to understand the whole situation between the coordination, like the associations that I was talking to you about before, like contact, company, and deal, and then to back up and blow it across 3 different systems. So in HubSpot we had company, contact, deal, and then in Close we had lead, account, and booking link, and something like instantly we had just a contact database that was being fed through something like Clay. So this was the go-to-market. It was Clay, Instantly Close, HubSpot, and then we had some Zapier in there. So how I had tackled this systematically was first dive in with the marketing operations team, and then I went to go talk to the sales team. And then after that, I went and also dove into the infrastructure of kind of the go-to-market stack. I was auditing everything within the first 30 days. Taking no action. So after I understand the whole situation, it's like pages and pages of documentation and audit documents, and then I would come up with a consolidation plan because first of all, why are you going to pay for two different CRMs at the same time if you can have all of your information split across three different environments to be split across just two, right? We want to consolidate as much as you can. You don't want to waste as much money in the whole system. So kind of like trimming the fat. So what we were able to do throughout the go-to-market and the revenue operations environment was to completely get rid of the secondary CRM and actually migrate all of the existing 60,000 contacts or records that were in that CRM and just use it as a reference database. We weren't actually setting it up to recycle the leads, like to hit our target addressable market. But we were able to start referencing that database in our other two databases. So that's Clay, our sequencer, and then HubSpot, our deal activation pipeline. We were able to start referencing it across the whole board. So what basically this allowed us or the company to do was to be able to never contact anybody inside. Like we set up very strict block lists and we changed over from having two databases with no filtering criteria into two databases with active filtering. And when we start to do go-to-market motions, or whenever I design it, I like to think in active filtering criteria. And basically that all that means, if you've ever opened CRM and you have a list of 20,000 contacts in your CRM and you're like, ah, I don't know what any of these contacts have in common, go back to the idea of what do we wanna measure? How do we wanna group people together? It's called segmenting. And then how do we want to actually include and exclude the people that we do and do not want to talk to? So setting up the static lists and the active lists inside of HubSpot was a really big migration effort. And then using that to feed into something like a sequencer inside of Clay and kind of like enrich all the information inside their CRM for sales enablement. And then being able to go and sequence the people that they hadn't been talking to or that they wanted to talk to, or better yet, a really good example was we have the company, it's in pipeline, but the deal has stalled for more than 14 days. We haven't heard that that person attached to the deal hadn't been responsive in 14 days. That's a really big problem. If you have a champion and they just stop responding, like you're trying to close a deal, right? What do you do? Do you just keep bugging them? Do you go on LinkedIn? Do you text them? Do you call them? Like, what do you do? So systematically, what was able to be set up was inside of HubSpot. If a deal has a contact that has not been contacted in 14 days or more and 20 other filter criteria, basically. Go and send a webhook to Clay to go and find 2 or 3 other people systematically that work at that company that may or may not be related to this person based on that job title. Then you can start to go to the multi-touch sequence and you can start doing account-based, you know, sales strategy. And now you can go and explore, be like, hey, I was talking to Jamie at Dealfront, but maybe he's not really like, just wondering if he's okay to, you know, like I haven't heard from him, just hypothetically playing. But you can see where the enablement comes into play when you start to consolidate everything. So at the end of the day, like the result there was consolidation from 3 systems into 2, active filter criteria from the main source of truth, which is the CRM, into the sequencing engine, and then automatic enrichment to enable sales from the single source of truth, which is inside of HubSpot. And then of course consolidating all of the automations and workflows into active filter criteria inside of HubSpot, which should and always be the cleanest place data hygiene-wise and the single source of truth hygiene-wise as well. So going back to like revenue operations, so— Go on, go on.

I've got a timer at the top of my screen. We've got time.

Okay, so the second one, I might be a little bit excited because I actually got a testimonial on this one. It was a project we, instead of doing it in 30, 60, 90, I decided to test something. We were doing it over 8 weeks instead. Different stack, same system thinking. In the last example, it was a larger company, a ton more budget. They have Clay, they have HubSpot, they have everything like that. But what happens if you're at a Series A, Series B company and you don't have hundreds and hundreds of dollars to spend on tech stack and you just want something more lean, something that you can more control, more modular or something like that? Enter Adio and Clay, as well as some custom signal-based engines that are both integrated directly in something like a system like Clay, which is my GTM tool of choice, as well as like Google Alerts. So you're like thinking, what does a Google Alert have to do with, with go-to-market, right? So in this example of like the practical application of go-to-market strategy and execution, we can start to look at not just the situation at hand, but also how it was adapted based on system thinking. Yeah, at the Series B company, they were a cloud spend optimization platform. And they wanted to target CFOs at their target accounts. So their target account list was maybe anywhere from 600 to 1,500 accounts, like not very big. Basically anybody that is paying for Amazon Web Services, think about that, right? And if you know anything about cloud spend costs, they can get insanely expensive. And what this company does, the, the company that I was working with was actually help you decrease your cloud spend bill by like renegotiating your credit costs. And however they used to do the business, it was like exactly however they actually did it. But basically they didn't make money until you guys saved money. It's a really good business model. And who cares the most about money? CFOs, the financial controllers, right? So it makes sense that when you want to set up a go-to-market motion, that you want to target people that are going to make the most influential change in the quickest amount of time. So based on that and based on kind of the discovery of the situation and understanding what this company wanted to do and how, what their sales criteria were going to be like, what they needed was a desire to decrease costs. And nobody does that better than somebody that's in charge of the money. So our market signal here would have been a Google alert. When somebody comes in at a Series B company, somebody just gets hired Right? A new CFO at a Series B company gets hired, or better yet, oh, or financial controller gets hired and it gets publicly announced somewhere, anywhere in the world, right? Or a Series A, a Series B, or a Series C company raised funding. Because usually when somebody raises funding at a tech startup, they're usually going to start spending cloud spend. And that was one of their triggers, their market triggers. That they had mapped out and they had given to us on the strategy side. Long story short, what was end up happening was that I needed to set up the CRM to be constantly updated with information about CFOs, financial controllers, what they're doing, if they've moved jobs, what company they've worked at, if they've got Series A, Series B, type of series, right? Total amount of funding, whatever it's going to be, and then whatever the industry is going to be as well. In Adio, it was pretty simple to set up. You just write a custom POST request and you send all of the information directed to Adio, parse that webhook. But before you actually send the information off into the CRM, you need to understand what information to send. What are you going to measure? What do you need to measure and what would you like to measure? So it depends on what the sales guy said. And basically he said, oh, I want to get a notification. I want to understand the second a CFO in our niche comes into market, or that it would be a good time to reach out to this person. Enter the signal-based engine of go-to-market strategy. We were able to set up a Google alert, multiple Google alerts. Any, any time a new CFO was mentioned, like hire CFO keyword, CFO, financial controller replacement, hired, whatever, and also set up auxiliary Google Alerts to come in and monitor Series A funded, raised XYZ, whatever, Series A company funded, and then the, the value amount. And by setting up these Google Alerts, what we're able to do is we're able to set up an evergreen type of playbook where now you don't have to turn the engine off and you end up getting 15 to 25 prospects per day that are pre-qualified on the need basis. Are they working at a Series A, Series B, Series C startup? Are they at a cloud spend company? Are they an OC, a new financial controller? Okay, those are the things, 3 things that you need to close the deal and everything else that you want, you want to know where they are, their history and all that. So looking on to what actually happened, the Google alert would be set up for Series A, Series B, and Series C. And an unforeseen circumstance that I ran into was that I forgot that there's Series B baseball and Yeah. So you know where I'm going with this. So I didn't build my prompt well enough the first time because I said take everything that says Series A, Series B, Series C and go down and go find the company that was mentioned in the news. And I completely forgot to put on the guardrails of the system like, hey, don't shove the circle into the square block kind of thing. I said only. And then I had to make a whole filter in the prompt saying if it says it must include the word funding or raised, if it mentions anything outside of this, including baseball, XYZ, do not filter it through. So now we have, you know, a whole list of all these different companies that coming through Series A, Series B, Series C, new financial controllers, and they're all going into one master list. And in that master list, they're all filtered already to say, yes, we, we meet the criteria to actually be processed. What we do then inside of Clay, once we have the clean data list, all of the qualified data, We go and enrich the data. We get the email address, we get the company, we get the, we qualify and we, we, how do you call, you, you make sure that the data is correct. If, you know, if BBC says you raised $500,000, but Crunchbase says you have $650,000 or million or whatever the funding amount is, you have to understand like, okay, you have to give the range because these are sources, these are discerning sources. Outside of that, it's just a matter of sending them down one of two paths or both. Send them down an auto sequence for outbound, reach out to them omnichannel with email and LinkedIn. Sometimes you want to send like a handwritten or to their headquarters or their venture capital saying, hey, congratulations on this funding. You can do that as well. Or you could just send it directly into Adio or HubSpot where the sales team would then get a Slack notification I've built into their environment where it just says, hey, a new CFO at XYZ company just came into market. Here's the news, link to the news headline and the link to the Adio or the HubSpot contact. If you wanna write to them directly, here's their email address and that's their Slack, they get it on their phone. So we're no more than 3 feet away from our phone. You have to make it easy for people to take action. So that's kind of like the second situation of how you would build it like A to Z. And all of that was done in about 8 weeks from strategy discovery, audit consolidation, action. Routing, lead assignment, everything. And then to this day, I actually just talked to them. This was a few months ago. I just caught up with them and the system is still generating around 20 to 25, like, in-market leads that they can contact every day because it's evergreen.

Interesting. Well, I appreciate you going through two of them in such detail. I think the, probably the most valuable thing from that section is what we're gonna be able to do with the transcription and the clips and what we can do at actually digesting those individual stories and fleshing them out. So thank you for that. Two kind of, I guess, wrap-up questions. Looking ahead, looking forward, how do you, what's your view, opinion, prediction on how AI changes the way we build and scale these GTM systems?

Faster. It's faster. It's more expensive. It's kind of scary in a way, like just to go from like hyper-excited answer to kind of like something that's like more on the ground. The shift in using AI to do any of our tasks, whether that's gonna be automating meeting notes, whether that's going to be reaching out to people automatically, whether it's gonna be automatic qualification, it's only as good as you train it. It's only as good as you can process or optimize the system. Right? And what I mean by that is it's— I could not do what I do today a year and a half ago. There's no way. There's absolutely like, it is so fast. And even somebody like me, I feel 4 months behind. Model context protocol, huge. I still haven't built one in, in-house. Like we're still configuring everything. And what I would say is I'm, I am super bullish on the idea of AI development and AI deployment especially, but I would err caution. I would say you don't need to AI-ify everything. And I think you're going to spend a lot of cloud spend and you're going to spend a lot of credits if you do it the wrong way. If anybody you know has ever built in like a coordinate, an orchestration layer like Clay or anything like that, you know that you take your eye off of it for one second and you burn 50,000 credits. And I think that without proper training of humans that are operating these systems or guardrails that are keeping away the Series B baseball games in my earlier example, in prompt engineering, without the proper guardrails and the limitations and the goals and training, it's going to amount to a lot of fluff. And that's where you go back to your original intro idea to being on LinkedIn and you just see like all of these workflows, they're everywhere. They, you're like, oh my God, it looks so pretty. But like, what the hell does it do? AI is a shiny object. Not everything needs to have AI in it. And I think this is where a lot of AI mandates are failing because they're not saying, okay, we want to have AI do this because of this reason. No matter the size of company, I like to use AI because I'm lazy and I'm dumb. And like, that's the truth. And like, I don't think I'm lazy and I'm dumb, but I don't wanna waste my time doing mundane tasks. And I don't think that you wanna wake up every morning and make your coffee. If you can have an automatic coffee machine that wakes you up, which sends an alarm to your phone at 6:15 in the morning, it wakes you up, and at the same time it starts brewing your coffee 5 minutes later. You don't have to use your brain. So that's the whole point of AI is to actually not use your brain because you're using the brains of 20,000 other people that have been trained specifically in that model to be able to do whatever task that you're trying to do. So it's up to you to tell it what to do and at what depth you want AI to scale or build or test GTM system, business systems, your own systems, like can do whatever you want. You're the controller. And I think a lot of people think AI will replace them. It's not the truth. AI will replace people that don't adopt AI. If you know how to use it, you won't be replaced. So I would at least start somewhere, like understand what you're really good at. Systemize it. And then from there, take everything that you don't want to do about your day. Take a sticky note. Like, I do it. Take a sticky note and write down every single time something pisses you off in the next 7 days. Keep it on your desk. Keep a pen next to it. Be like, I hate writing meeting notes. I— it took me 17 minutes to write a follow-up email. And I swear to God, take that list and give it to any LLM, GPT, Claude, Gemini, whatever, and say, I want you to come up with some sort of workflow automation that can help me get rid of this every day. I'm wasting so much time. You become more effective, you become faster. And that's how I think you should use it. I don't think that you should use it to build a, like, you know, great AI orchestration layer, but like, it's amazing. Like, you can have AI employees, but like, what are you paying that person to do that? What are you paying that team to do? You're just paying for cloud spend. So, I think it's really important to understand. I'm super bullish on AI development and deployment. It's so fast. I don't think I'm going to be replaced, but I think realistically, if you're not at least using 1 of every 10 components that are available to you, you're just putting yourself behind. If you're not understanding how to prompt, at least at a basic level, you're behind. If you don't understand how to coordinate systems on a GTM and revenue operations front, you're a little bit behind. If you're not applying AI to your standard workflow, and by standard workflow, I mean your humanistic routine, like you wake up in the morning, you do this, you do that, you're behind because you're, you're using your brain too much. And our brains were meant to think and create, not to store information. So why would you not take all of the information inside of your brain and try to put it in some database somewhere? And then reference that database. You have the idea, write it down on your phone and then forget about it. Don't store it because then you're gonna be like, I can't forget that. And then you go to sleep and you wake up and you're like, shit, I forgot that thing. Write it down, write it down in that database. Right. And I think that's how you should use AI, honestly.

Yeah, that's interesting. I think a couple of points there. So you called it the shiny object thing. I know, you know, the industry globally, whatever, we go through these cycles of shiny objects and it's just, this is the latest shiny object that everyone's focused on. There's so many memes and funny videos on TikTok and Instagram about that whole theory of this is just the new shiny object. But it's— I quite like what you said about the overwhelming pressure, or that, that people feel like they need to AI-ify, uh, everything rather than specific scenarios, use cases, workflows. And I think I really liked your tip on write down the things that frustrate you most about your typical week and implement AI to alleviate those frustrations. I think that's a really, really nice way. Another one that I like is if you can see an opportunity to scale, you just don't have the resource, that's where we're also doubling down on AI. So we're— if you're in content specifically, you're already creating something that we would call it like enriched content. So this conversation, it's enriched, it's a human speaking, it's spoken word, that's already enriched information. But what could we be doing with that enriched information if we were using the transcript and then putting that into some sort of workflow? So that's— we're using something that's already high quality and turning it into other things that we might not have the resource, uh, or capacity to do. So I think that's an, uh, for con— it can be very, very, uh, very, very powerful. Um, but we're not necessarily looking to, I don't know, AI the naming of every single clip or video because typically AI names and titles can be very, very obvious and overuse colons and em dashes and all manner of grammar. Okay, then. So last questions for marketing or ops, just getting started with building these systems. What would you say? I think you kind of already answered this, but what is like the literal step 0, step 1?

Take a piece of paper. Put it on your desk. Every single time that you have something that frustrates you, write it down. Every single time you have an idea, put it on a different piece of paper. Now you have two pieces of paper on your desk and you have an idea paper and you have a frustration paper. Do it for a week. Get, get GPT on your phone, get Claude on your phone. Don't interrupt your workflow. Do not make adoption hard, right? When you wanna build a new habit, whether that's gonna be learning or adopting a system, do not disrupt your habits. Habits are the hardest things to form and they're the easiest things to break, right? Wake up, brush your teeth, have a cup of coffee. Okay, wake up, brush your teeth, go on your phone with your other hand and look at the news. Next day or something else, whatever. Have your— you're gonna wake up, brush your teeth, read the news on your phone, and then while you're having your coffee, you're gonna sit at your desk and do whatever you're gonna do, right? Or you're gonna wake up, brush your teeth, read the news, have your coffee, and text GPT all of the frustrations that you're not looking forward to on your day. Don't interrupt your routine. And I think that's the easiest thing to do in life. Like, smarter systems are only as good as you can make them and as clearly as you can think about them. If you're not thinking clearly, if you're not making the procedural steps to actually generate a, like a process that you wanna follow or that you wanna apply, you're just gonna get garbage in, garbage out. And that's exactly what you're just going back to say is if you have enriched content, if you have a really great transcript, like from a podcast or something like this, a webinar, and that's a really highly enriched content, spoken word, and you give that to a machine and that system is really good at parsing transcription information. Garbage in, garbage out. Really good in, really good out. Right? So it depends on how you want to use it. But if you are just starting and you really just wanna start thinking about systematic change and smarter systems with or without AI, it's really understanding where the bottlenecks are. The bottlenecks and the stages of success. What do you consider your success point? Don't give yourself unreasonable goals. I wanna work out 7 days a week. You're gonna get to day 3 and you're gonna be dead, you know? And I, that's what a lot of people do. It, it's just what I do. I, I'm guilty of it. Like New Year, new me. I'm gonna go to the gym 6 days a week. I get to the 3rd day and I'm like, ah, you know, like it's not gonna happen. So I think mindset, if you wanna shift your mindset to becoming a system thinker and more forward thinking, especially with AI, I would start thinking about how you can reduce the mental load throughout the day. And then from there you can say, you can start chunking. And this is a really big thing that I started doing years ago. It's literally called chunking. And if you look at my calendar, I have 3 to 5 hours of just straight work. I get my giant bottle of water. I have a secondary bottle of water next to me and I just chug water and I sit at my desk and I focus. If you're able to do that, you're doing great. A lot of people can't. And a lot of people get distracted. And that's why I think, like, understanding what you are and where you are not capable of doing is very important for your individual self. Everybody is different. Don't compare yourself to others. But what I would say is, if you wanna learn something, sit down and learn it. Don't interrupt your habits. Build on them. Integrate what you want to do into your existing routine. And then plot out a way to systemize it, whether that's gonna be in your life, whether that's gonna be in business, whether that's gonna be adopting an AI implementation plan, don't interrupt because interruption really creases over adoption. When you have bad adoption, you waste money, you waste your time. And so that's how I would like start thinking about it. It's not a complex thing. Just start looking at your calendar. Start a calendar if you don't have one. Understand that, hey, like maybe during 11 to 2, I'm gonna get coffee, or like from 6 to 7, I'm gonna have my coffee, but in that time, I'm gonna at least open my Gemini app. Or something like that, right? I'm gonna have my coffee and I'm gonna do this. I'm gonna go for a walk and listen to a podcast. Build on what you already have. And if you're making a business plan, don't burn out and try to do too many things too fast. Don't be your own salesman and overpromise what you can't deliver on.

Pace.

And it's something that I personally struggle with is pacing. And this is like my whole thing this year is Don't take on 10 clients. Take on one more. Do it well. Keep the mind clear. Once the mind is clear and you feel stable, keep going. Don't push yourself so hard that you have to burn out. Right? This is like something that in the agency world we all talk about. Like, I have to take a vacation. I have to take a vacation. I'm gonna burn out, dude. You already burned out. You're already there and you can't relax. You need to disconnect. Everybody has to remind everybody, be like, dude, like you're working, you need to chillax, dude. It's not that deep. And that's how I would say it's like you have to think as a marketer, you have to think as an operations person. If you're working so hard that you have to burn out and that you have to recover, it's probably too much pressure. If you can dial back the pressure and then try to systemize it with AI like that, realistically, right? Like I don't write any of my meeting notes. I don't put any of my tasks in ClickUp. My bots do it for me. And I think that's how you can start thinking about it, like fundamentally on how extreme this paradigm shift can go. So that's kind of like my 4-1-1. It's just like clear mind gets results, garbage in, garbage out. Don't interrupt your routines and build on the habits that you already have. And I think from there you should be able to systemize anything. Knowledge dependent, of course.

Yeah. Do you know what, actually that's a far more well-rounded answer than I thought in terms of, I thought it was just gonna be a case of know your processes, and that was going to be the answer of like where to start. But actually, that was a much more real world in terms of like talking about habits and talking about mindfulness or headspace. So it was, it was almost a little bit Atomic Habits, James Clear, you know, that sort of like theory of things. So they're really, really good finisher, really, really good roundup. But I appreciate you, uh, jumping on with us. We're, we're coming up to time, unfortunately, but that is all 8 questions, and I didn't think we would get through all 8. So I'm pleased. It's a very, very well-rounded episode, which will go into a new agent that we're building and be turned into a long-form blog. So, uh, we'll, we'll have to either get you to review the, uh, the prompts and the workflow after it's produced or get you involved in the actual build of it. But yeah, that was a, a really, really good chat. So I appreciate you jumping on.

Cool. Thanks for having me. Appreciate it. And, uh, yeah, awesome chat.

And for those who wanna connect, find out more, have a conversation, catch up with Spencer, obviously all of the details will be in the description, whether it's YouTube, um, or stream. And we will catch you in the next episode.