Spencer Tahil at Lead Summit 2026: What's Actually Working in GTM Right Now

We are gonna spend the next 20 minutes or so just talking about modern-day AI-enabled outbound. A little bit of background on me, I'm the founder of Growth Alliance. Currently we have 5 clients spanning Western Europe as well as the Americas. Currently we are building AI-enabled account research platforms and revenue generation engines for B2B teams. So if that's one of you, maybe this is a really good talk for you to pay attention with. You will notice that I'm pretty direct on how I present things, how We work usually internally as we take on around 5 clients, 5, 6 clients at a time. We run a fractional model with them and we generate an engine that can penetrate those larger mid-market or those enterprise cap accounts. If that sounds like it's for you, I'll keep us going. In, in the hemisphere of account research, right, and account penetration when it comes to enterprises, I've noticed that there's pretty much a broken process right now. Many of you guys might have heard tools like ZoomInfo or Clay.com or maybe even Apollo Or Outreach.io, maybe even LinkedIn Sales Navigator, right? So what it was like in 2018, even 2022, around like 4 years ago, 2 sessions before, you know, 4 years apart, like this industry is evolving. Unfortunately, the technology is advancing, but the adoption of it is lagging behind. The processes and what we're all taught to do is usually falling into 1 or 2 buckets. You jump from agency to agency, you jump from company to company, you follow a playbook, And at the end of the day, it usually comes down to one of two things. Right now it's you're pulling a list, you're going to LinkedIn Sales Nav, you're sending it to everyone super fast. You don't really have to think about it, so on and so forth. But if you're going to do this, you're probably going to be sending out messages on LinkedIn Sales Nav. You're going to be sending emails from your main domain. So let's say like you're @growthalliance, that's your main domain. The second one is going to be roughly 10 times the amount of time, right? You're going to be an executive account executive, um, and you're going to be researching every single account, doing it by hand. Making it sure it's super relevant, but you get capped at scale. So the problem in the market is not so much that the technology doesn't exist. The problem in the market is that the process hasn't been updated right now. And the reason why I asked the technology adoption and the rank— the rankings that you guys had put in the comments right here was so that I can show you exactly how I'm doing it today for around 5 to 6 of our clients and their clients. So the research isn't the pre-work. Research is the work. And right now, the best research is not personalization. Nobody cares about your sister's dog and your new wedding. I'm sorry to say, right? Like, yes, you can congratulate a client. You know, you're trying to get into Google and you see your marketing manager, the marketing manager you're talking to, they just got married. You can congratulate them, of course. But the best research is having more context over time inside of your centralized database. Usually that's inside of HubSpot, that's inside of Salesforce for a lot of enterprise teams. But a lot of people are still using Slack. Hey, Wesley, I noticed that like this person at this account just changed jobs, but how does that data actually compound? What is the context that you're going to put into your backend system to make sure that you can go to that target account at Google or at Amazon or even ZoomInfo and try to get inside of that account? At the end of the day, I'm a salesman. I'm a marketer first, though, and There are some awesome tools out there. I'm partnered with a few of them, but the data only tells you 2 things right now. It tells you who matters and who in your network actually can get you into that account. That being said, a lot of the different frameworks that I use internally right now, so just it's 5 different layers, right? 4 fit dimensions, 1 composite score. The strategy of piercing an account Regardless of the technology that you're using, and there are some amazing presenters that are before me and, and after me in this panel at Lead Summit, they're going to walk you through their own processes. At the end of the day, the tools that they use and the tools that you're going to use in your business are inherently going to be different because the context of every business is different. So I don't want to sit here and I don't want to tell you what exactly to do. This is exactly how I think of it. This is the framework that I apply. And I'm going to explain to you every single thing that I do irrelevant to the tool. So in case you've heard buzzwords like Claude Code, N8N, Supabase, like Clay, all of these different things, you can go into all of these tools and apply that framework. So the 5 layers that I apply in the order is account universe and ICP filter. So we're looking at that account, like let's say you're targeting the United States, that's not good enough for me. and for the Cognism team and so on and so forth. I need to know exactly who's in the target account universe, who you can serve, and why they're qualified for you to do business with them. The second one is going to be AI-powered company enrichment. So I want to look at that company, I want to look at the firmographics, the demographics, I want to look at the genetic makeup of the company, and then I want to map the stakeholders and the relationships between that company and my company. So unfortunately, my firm is very small, probably in relation to all of your businesses, But it is my job to be able to say, I know all of my partners and all of my partners come from Y Capital or they're coming from Sequoia. I wanna look at their investors and I wanna ask them for introductions to people like you, right? Being able to map the relationships is part of the modern-day go-to-market strategy, whether you wanna admit it or not. Also understanding who is in seat, if it's relevant to talk to them, and if it's even a good time to talk to them. This is where signals get layered in. So there's really awesome tools. You see, like Signalbase is one of them. Centrion, you maybe heard of Sixth Sense, Commonroom, all of these different tools, maybe even NativeClay. These will tell you when it is a good time to reach out to people. But what it doesn't do, it doesn't compile all of the different scores and all of the different fits, composite them into one working model, and then route it to your sales team. So this is part of the issue is that you must start thinking in systems thinking approaches. And this is where coming from the revenue operations side, where I've come from, kind of has its benefits. So the proof here is that first layer, second layer, third layer, fourth layer, and fifth layer, they all have different, you know, metrics and KPIs that we're roughly going to go after at this point in time. First layer, we need to filter down a list of 3,300 accounts in North America in the Northeast region, we need to filter that down. And we need to use something like PitchBook or ZoomInfo to help us do that. Maybe you guys have built lists beforehand, but we, for instance, helped an M&A advisory firm acquire a universe, was compressed into a high fit tier. Maybe you are a salesperson, maybe you're a sales director in here, maybe you're in marketing, maybe you're in revenue operations. You will understand this word, segmentation. So the smaller we can get the segments, the more precise that we can be when we go and go to market and try to penetrate those accounts, whether or not that segment is going to be all the CMOs, or all of the businesses in North America or all of the businesses in Germany. We need to make sure that the segments align so that we can put relevant information in front of them. The second layer is multi-source waterfalling. Maybe you've heard this terminology before looking at clay.com. Maybe you've heard this source anywhere on LinkedIn where people are talking about using N8N. We want to enrich these different contacts and these different companies, but we want to do it systematically. So there are 1.5 million data providers in the world, probably right now. Most of them, notably, you'll see down below in the middle of the screen, it's Clay Cargo, Signalbase, Findymail, ZoomInfo, Apollo, Lucia, you know, give or take a few, right? But that's not where it all stops. Once you have the data, once you know the email address of the person that you're trying to email, or even the phone number, I mean, I get a phone, I get a cold call almost every day, even though I'm sitting in Europe. How do we reach out to them? 75% of B2B business deals are done, are done through referrals, and that's realistically a fact here. So you can use tools like The Swarm or even Sales Navigator to figure out who's your first-degree connection, your second-degree connection, as well as your third-degree connection, and then map your target accounts versus who you actually know in your network. Going back to that strategy beforehand of You know, cascading down. The 4th layer, it's just a matter of layering in and contextualizing everything that you do. So it doesn't really matter that you have all this data in the target account universe. You want to make sure that the hiring signal, the intent signal, the event signal, everything is its own sequence and everything that you do is relevant. It doesn't matter if you're selling a Ferrari in an e— if you're giving away a free Ferrari in an email, if that email doesn't land and that person doesn't open the email with a great subject line, right? And coming from that email marketing world where it's like spray and pray strategy, it makes sense. Your offer has to be killer. They have to be in need. They have to be relevant. And you have to make sure that they open that email to see your red Ferrari, right? You can use Clay, Signalbase, and Octave to be able to do really, really great contextualization. And of course you can also use like Claude, Claude Code, and all of these different things. The 5th layer is enrichment, scoring, routing, and actioning. This is where a lot of handoffs happen between the marketing team and the sales team and the sales team to the revenue operations team. A lot of companies and a lot of marketing agencies will only do half of the, like solve half the problem where they're, you know, going out and list building, coming back and then assigning all the leads, sequencing them, but they don't really care about how that is attributed or how that affects the account, you know, the account executive universe. How do they get routed? How do they get actions? What are the tasks that the sales team need to do to be able to convert Google or Amazon? So everything needs to have happened sequentially. And you can see that there are multiple tools that you can use. And of course, if you need something specifically planned out for you, you can always text me post-show. So we're going to dive into the 5 different things that I was talking about. The first layer is going to be an ICP scorer. We want to start at the top. And on the right-hand side, you'll see kind of like a little blurb of a little agent that I've built in a tool called Cargo. We're not going to go into the tool right now because it's a little bit technical, but the first layer is account universe and ICP filtering. What we want to do is I want to take 10,000 target accounts in Germany or 20,000 target accounts that are using Shopify inside of the United States. And I want to filter that down to make sure that there's a fit, like a very generic fit for, you know, let's say like employee fit, ARR fit. Let's say they have a budget of They have an ARR of $10 million and I charge you $100,000. Maybe that's a pre-qualifier for us doing business together. Maybe it is the same for you. So having a preliminary filter that scores ICP across just a preliminary data, you know, filters will keep you safe and it will keep you in your lane for not going past your minimum level of engagement. If the smallest package you sell is $5,000, then you have to generate ROI possibly out to like Starting at $1 million. Depending on budgeting, you might have other concerns, but of course that is context in your own business. I know for myself, for us to produce a really good ROI, we have to produce around 350% to 500% on whatever I charge. So I have to reverse engineer what that pricing is going to be. For here, you can see that the tech stack fit also gets assigned points. If they are using HubSpot, they're a really good fit for me. If they're not using HubSpot, then they get They're, they, I need to see if they're using Salesforce or something like this. Industry fit is also really good if you guys are going after fintech or if you're going after marketing tech, depending on what you do, it's going to be different. I just want to reiterate that. The research agent is the next step that we're going to dive into. And I'm sure you have many questions about how this is done, what platforms to use. And I'll just preface this by saying it doesn't matter. Just make sure that your research, whatever is compiled to, Is contextualized enough for you to understand it. And that's the one thing that I want you to remember if you walk away from this conversation. It doesn't matter all of the research that you do. If you don't understand it, you've wasted your time. So make sure that whenever you hand it off, that it's digestible. Here you can see that there's 2 different tracks that I might be willing to target at Growth Alliance. You can see that Track A is venture capital, you know, private equity, even family-run offices like dentistry. And then the track B is probably hopefully people like you, like watching this. It's SaaS, agency services, industrial. And then depending on your, what your pricing is going to be, I would mix and match the ICP. Before I even start to penetrate a large organization, and I'm talking, let's say 50 to 5,000 employees at this point in time, I need to understand the genetic makeup of your company, what you guys have done in the past 18 months. I need to understand what tools you might be, you know, using. I need to do deep ad intelligence research as a marketer to understand, hey, maybe they're spending this much on ads. They have 18 ads rolling, but I need to do that research before I go and open the door to you. This is why when people cold email you, you delete it immediately because it shows how lazy they are. So again, the research is the work. It is not pre-work. The research that you're doing can be compounded and you want to use that to contextualize. Everything that you say to that client or to enable, enable your team. Intro mapping. So this is the 3rd layer. This is very, very powerful. I'm partnered very transparently with The Swarm on this. And basically what we're able to do is we're able to say match company versus the target. So let's say I'm going into a large enterprise account. You want to map who you're going to be talking to, who holds the paycheck, and who the main beneficiaries are going to be. Sometimes if you look inside of your Salesforce account or inside of your HubSpot account, you'll notice that the very first contact with the company in the original, you know, the original deal sequence was not with the person that was signing the actual contract. You'll notice that maybe you went and talked to the engineering manager. At the end of the day, the VP of engineering was the one that signed a contract. That's the difference between penetrating an account directly versus going bottom up. Or top down. So depending on your market penetration strategy, you're going to want to map the intros and understand the org chart that is at that company. There's a second layer to that strategy as well. And I'm so— I apologize if I'm speaking kind of quick because it's like pretty interesting to me. The second layer that you can do nowadays is you can not only just map the organization using who reports to who using AI and matching and saying an engineering manager would report to the engineering director and that would roll up to VP of engineering. And then that person to CTO. Now what you're able to do is see all 4 levels of that, those, those account stakeholders in those companies, and you're able to compare and contrast it to your own company. So at Growth Alliance, we have 4 employees. It's myself plus 3 others. And before I go and write a cold email or I give a cold call to somebody or I DM them on LinkedIn, the first thing that I'm gonna do is I'm going to look at who in my network, myself, my team, my partners, Any investors, any of my clients that I'm on good standing with have a mutual connection to somebody at that target account. More than likely or not, we're going to have some connections on a second or third degree. It's very rare that you're trying to prospect into account at the B2B level and you know absolutely no one. This compounds over time with more people inside of the network graph. If you have 5,000 people on one side of the equation, you have 5,000 people on the other side of the equation, you can see the math, it compounds, right? So intro mapping has 2 aspects, and this is what I want you to understand at the back, in the back of your head, is that this is very fundamental now to penetrating larger accounts. You have to think back to the last time one of your friends or one of your business associates or colleagues said, hey, I actually just joined this last company. I've used you guys. We work together. Hey, are you interested in coming to work together? That's one way, right? That's still, that's still a relationship. Or anytime that you've sent an email and it's bounced back and you've gotten their new email address. Okay, they're working at that company. Let's go and try to promote our product or our service to them because we had that personal relationship. It's always great when you've got a guy on the inside is what I'm trying to say. So intro mapping will help you in the long run to be able to understand what strings you can pull from a sales perspective when it comes to penetrating accounts. The 4th part of this large equation, all of these things that I've talked about beforehand. Have alluded to data in the ether or in, you know, in the space of marketing or in the space of sales and deep research and all of this. But what it doesn't tell you is the timing. You might have heard of this on LinkedIn, signal-based outbound, inbound-led outbound, whatever it's going to be called, leadership changes, job change detection. A lot of different providers will give you this information, and as it is very good information, We can classify that under signal intelligence, right? On the screen, you can see on the bottom part of this like graph thing that I've created, leadership changes, new CRO, and what is that, 0 to 3 months and they've just hired a CRO. I need to go talk to them like now. That CRO is going to put together a 9 to 12 month strategy plan. I need to get on, on the books, right? Team expansion plans. Are they hiring a VP of RevOps? For me, it makes a lot of sense for me to be involved. And of course, funding round. If you got— if the CEO or the CRO has just closed a funding round, I want to go and contact them. Tech stack changes as well. The more dynamic information that you can pull again from different data sources, the more context you can add to your database and the more you can weaponize it, so to speak, right? So signal detection, no matter the platform that you get it from, whether that's going to be Signalbase, ZoomInfo, that's going to be PitchBook, you know, acquiring data, if it's going to be Sixth Sense, Commonroom, whatever it's going to be, I would recommend that you try to contextualize in your past, you know, deal cycles. When did they actually get in contact with you? I mean, it's all past. You can go look up the changes of information that are there, but maybe your deal cycle has been the last 6 months and you've just closed this giant client and you've got 10 of those clients in your CRM. Let's go retrospectively. Do a retrospective of what happened in the, you know, in the ether of that target account that over time in the market has also happened alongside of your sales process. You can backtrack all of the signals that that company has elicited in the marketing and PR publicly, anything like this, and match it up against why they might be buying from you. This is the very first step that I would recommend all of you guys take if you have that really great pipeline already and you already have historical deals. If you do and you're able to match up what is the buying behavior of our target accounts and the personas at those target accounts, then it means that you can go and listen for those signals over time and you can purchase, you know, subscribe to different tools that are going to give you your relevant signals. The issue that I have with this and that I would just very transparently flag to you is that you are absolutely going to get oversold on this crap of signal detection and signal intelligence. Not all signals and not all playbooks are going to be relevant for you. So just make sure that before you actually subscribe to a tool, I would recommend that you actually have 3 to 5 different use cases before you pay for that subscription. And that goes across the board. It's my job to save you money. That's how I would tell you guys if I were consulting for you. So just be aware that not all tools are going to fit you, but for signal detection, I think a really, a really good few databases include Signalbase, ZoomInfo, SixSense, Commonroom, and the native Clay integration as well. Composite scoring is something that I've actually come up with on my own. I shout out my friend Danielle. We've had many conversations coming from the HubSpot world, but this is something that we've taken across into Salesforce instances. As well as into HubSpot instances, Adio, and things of the like. Composite scoring is something that checks for factual accuracy, branding, personalization, technical accuracy, and then it combines all of the other things that you want individually. So the smaller steps that you can break it up into, the first 4 layers of the framework that we were just discussing, each one is scored on a scale that you set. And maybe it's a maximum of 10 points. Maybe it's a maximum of 100 points. But how many times have you been inside of, inside of HubSpot and you've seen something along the lines of, my God, it's an ICP fit score, and then it doesn't tell you any context. So the composite scoring model and how you would integrate it into a go-to-market effect would be to score each effect or each layer individually, then composite these over time and contextualize all 4 of the summary files that you're getting from the other intelligence layers. Into something that's digestible for you. This is how I like to think about something before I even send anything. Everything happens behind the scenes and you're able to do the, all of the orchestration before it hits your go-to-market, before it hits your sales stack, and before it hits your CRM or your revenue operations stack. This is a very important effect for actually making sure that you have relevant contextualized content for your team as well as the target account. All of this said and done, Maybe it doesn't actually matter to you who the hell you're actually talking to if you don't have that data, right? So the committee, how I like to think of this when I, especially when I go for the larger accounts like we do in the upper mid-market, you know, probably $10 million up at this point in time, we have 2 audiences and sometimes they overlap. The first one is who gets the value? Very, very simple. Users, operators, the people whose day you are going to actually change. If you sell a fin— a fintech, you're probably gonna go talk to a CRO or you're gonna talk to a CFO. But sometimes the budget authority of those VPs, those CFOs, and those CROs, they're just gonna be the ones that sign the check. And many of you have gone into enterprise accounts, and for the ones that have not sold directly into enterprise accounts, you'll notice that these are in their, their separate boxes for a very specific reason. The people whose day changes. And the people who just own the P&L line, you must change how you speak to these different people. Meaning if you prospect, are you cold call a CFO or you cold call a CEO or you cold call a VP of engineering? You must change the way that you speak to them because their jobs and everything that they do rely on the information that you're giving to them as an expert. The angle for beneficiaries in my book usually comes down to time saved, Friction removed, workflow unblocked, things that are very hands-on that those upper managers aren't really going to have vision on. Meanwhile, those upper managers are really going to be focused about margin, ROI, risk reduction, and then board-level outcomes. For our fractional clients, for instance, I sit between the beneficiaries and the budget authority, and it's up to me to help them create those actual presentation slides like the ones that you're seeing on the screen right now. I have to look at All of the past data. I have to measure the ROI, I have to plot the risk, and I have to help them present it to the board to keep me employed, right? So depending on what you sell and what you do, I would just start to think of it as this before you even go after those enterprise accounts. Going back to it, this is part of my 2024, 2025, 2026 strategy. It's just understanding that penetration is all about networking. Don't try to force something. If you're going cold, it's gonna be a lot more diff— difficult and different kind of strategy if you're going someplace warm. A lot of people are gonna try to sell you via cold email or LinkedIn outbound that cold outbound is gonna be the way to go. You just need to send more emails, bro. Like, this is, this is not the way. I mean, we do it, but it's not, it's not the way. It's not how we penetrate large accounts. The warm path is being able to map exactly who you can talk to, who can talk to them on behalf of you, and who you can go talk to directly if you don't have that warm intro. You can use the Swarm again. They have like a ton of different profiles in their database. If you need help, let me know as well. But the cold path is gonna be research informed. So no matter what, you should already have that data that we were talking about in that framework from 1 to 5 contextualized before you go and do outbound and any sort cold calling, everything of this. Multithread beneficiaries and budget authority. It's completely different framing. So just be aware of this if you're going from zero to hero. And if you guys are doing this, I'd love to know your comments if you guys actually see this happening in the real world as well. As far as kind of like one engagement that we did, you can see that I've broken it down step by step. In less than 100 days, we were able to close for a client close to $1.8 million and generate around 6 times that in demand. They were a very large video marketing agency. We produced a target account universe of around 7.5 to 8.5 million CXOs in their mid-market. They were trying to sell video marketing services, serving clients like AAA and the like. Enrichment, we were able to run everything through a Clay waterfall. We did everything in Clay first. We were using like tools like ZoomInfo, Apollo, Findymail, ICPs, all of these different data providers that we're talking about. We were matching it to their industry and their, and their verticals. And then we transferred them to Cargo. Regardless, we're able to be, you know, tool agnostic in the enrichment universe. Stakeholders were mapped. So to penetrate the target accounts, they were looking for 3 different decision profiles. And each one of those required their own, you know, one-to-one message. First, the CMO, then the VP of marketing, then the director of demand generation. So each one of these had to reference, hey, CMO Bob, I thought that you and VP Michael over here might be interested in video marketing. We noticed that you guys were running 25 video marketing ads, but you guys were also running 100 static ads. This is something that we see convert better. So understanding exactly what to layer on top, depending on your audience, is very important. Fourth, we've, we looked at the signals. So we deployed 10 to 12 plays that were evergreen. If people were hiring for a marketing manager, we went after the marketing director. If somebody was showing intent on the website, we were going after them. If somebody was hosting a marketing event in Los Angeles or New York, we were also going after the organizers and the attendees of the event. So depending on where those events were happening and those signals were being pulled from, we were able to contextualize even better. The integration part was moving from the market intelligence layer all the way through Clay scoring. We're doing a composite scoring model and enrichment. Then we were doing the HubSpot routing. And from HubSpot, they were being directly assigned to the account executives to go and prospect into pretty large accounts. In around 98 days, the result of that was around $1.8 million in closed revenue. Their deal cycle was around 32 to 45 days. So this was actually very good for us in that. As far as a full activation plan for all of you guys listening and anybody that needs help in the future, I do have a whole one-shot Claude code model that I'm happy to send you guys or even help you guys set up. You can see a little bit of it on the screen here just as a logic base. You can see the runtime will go layer 1 to layer 5, and then the output will then route to HubSpot and Slack. If it needs a review, then it's going to go to— if the confidence level is a needs review, a human review, then it would route to my Slack. If it will automatically be assigned, it will go to my HubSpot account. And this is the same logic that we go forward and we serve our clients with instead of HubSpot and Salesforce, depending on the context of their business. So this is kind of What you know, like we're wrapping up right here. Next Monday, I'm going to leave you with some homework as I always do before we go over to Q&A. Monday morning, I want you to make four moves depending on what it's relevant to you. Please follow them. What I would recommend that you do is you pick fifty enterprise accounts. Do this by hand. Pick fifty. Do not pick five hundred. Don't pick five thousand because you're gonna get absolutely overwhelmed. I would choose one tool or even a few different enrichment tools, and I would try to enrich them through one waterfall. If you're not using something like Clay, the Clay basic waterfall works very well. If you want to do this on something like Cargo or even in Claude Code, you can do this there as well. Probably the easiest way for you to do this is just to go through Clay or Cargo. And then I want you to map the committee. Look on LinkedIn Sales Navigator if you don't have any of these tools here. I want you to look at 2 to 3 contacts per account, write them down on your, on your little notepad next to you, and try to figure out who has the beneficiary role. And who has the budget authority role. After that, if you want to sign up for the Swarm, you can pull your network in, you can find the Swarm intro paths. And then after this, you could even write a one-to-one email for them. For 50 accounts with 3 stakeholders each, you're gonna end up writing 150 one-to-one messages. What I would like you then to do, huge exercise, I know it's a big ask. I want you to take all of those, those emails or those messages, and I wanna you to put them all in some AI. I want you to analyze, go look at the website, look at all the different things that you guys have done by hand and say, why did I write this? And help you develop a skill. And Claude Code helps a lot with this, but it will help you mentally to understand who sits in what role and who do— who actually knows who in those target accounts. So that should be a really good exercise for you. And just to end us and close us out before for Q&A, please, please, please walk away from this conversation knowing that research is just your leverage. Relationships are your multiplier, but speed is your advantage. And I know that like Dan is following me up in this conversation and he's going to show you some really cool stuff when it comes to being really speedy on what he does. But please realize that speed is your one true advantage. The faster that you can go to market, the faster that you can penetrate an account and show the ROI of what you do and your, you know, your solution, the better that you're going to have adoption. You're going to be able to get in front of buying committees and so on and so forth. Modern tools today allow you to move so much faster than 4 years ago. Even 6 months ago, we didn't have the capabilities that we did in a tool like Claude Code or even regular Claude or Gemini. So please just make that aware. Don't get left behind. Do not feel like you're behind. but remember to think that research is your leverage and nobody cares about your sister's dog. Make it relevant, figure out who knows who, and kind of just double down and get in front of that account. So now I think that's the end of my presentation. I give it back to you, Renee, if we want to go for Q&A.

Amazing, Spencer. Thank you very much. I love the talk. I think you are using the best of both worlds, like the AI scale automation side of things, but also like the personalization, like genuine personalization, not the AI one. So I love it. I love it. I actually do this sometimes, but a bit differently because I'm clicking on ads, right? Because I want to see what everyone is doing. And sometimes when I see their funnel is wrong, I would shoot them a personalized video like, hey, this is what you are doing, here is what I would recommend, and here is why it would work better. And it has like 80% open rate and 60% of those emails convert to meetings. So it does work because it's very much personalized and people actually appreciate it because they see genuine personalization. So I can confirm it does work. What I'm interested in What would be the budget for all the tools that you are using? Because you mentioned quite a lot of them, they're quite expensive, or at least they can get quite expensive. So I'm interested when you work with clients, how much do they have to pay for the tools to allow this to happen?

So to give you scale, we are working with a pretty large agency right now. They do north of $100 million a year. They have a killer offer. They have not done any outbound at all. Without saying too much, they wanted to try it as a new channel for the quarter. And to kind of anchor you very high on this amount, to be able to send as many as, you know, cold email DMs, support all of the initiatives and all of the token costs, we work— we quoted them for the whole initiative, including management fees, around $90,000 for the quarter. It's a very high quote. If you are looking to just get started and your target account universe is much smaller. The tool costs, depending on what provider you're going to go to, like ZoomInfo, in my opinion, is very expensive. Clay.com is a one-stop shop that you can go to, go get all of the different API calls all in one place. But you can go as low as $1,000 a month just for data. But again, how old is that data? What is the quality? So if you want to run phone number enrichment in the United States, This is very expensive. If you want to do this in Europe, you're probably going to go to Cognism or even Dealfront to get that data. How do we lower that data cost? How do we, you know, how do we not pay for the SaaS tools? It's quite hard. So I would say, roughly speaking, I would budget anywhere from around $300 a month to around $2,000 a month just for data and orchestration costs, not including management fees if you're going to have somebody out of house to take care of it for you. No.

Okay, well, that's reasonable for just the data, I guess. We do have some questions from the audience. Here is the first one. If you could explain layer 2 again.

Absolutely. So I will pull it back up on the screen as well so we can just have it up on the screen. So layer 2, is all about understanding how does the company actually get enriched. So depending on your situation, Rafa, I would say the first one is understanding the account universe and filtering, but then we need to actually contextualize all of that demographic and all of that firmographic information. We need to understand the context fit, right? So there are tools that you can go out, you can Google, you can go on Reddit and under and type in Company name, Reddit, and reviews, or Glassdoor, Reddit, Glassdoor reviews, company name, right? So this is the type of AI-powered enrichment that you're able to do at scale now. So if you're going to imagine you are sitting at your computer and you're typing in, uh, Company A Reddit reviews, Company A pay gap reviews, Company A Glassdoor hiring module, something of this sort, you can do most of this at scale now. With web scrapers like Apify, Blitz API, and so on and so forth. There are multiple tools that you can do this with. Nowadays, what's— what a lot of people are doing is they're logging into a specific orchestration platform like clay.com. Again, they just changed their prices, so it might be a little bit better for you to adopt. Type in the company name and it pulls up all of the contextual information. It pulls up all of the employee information. It pulls up where the HQ is, where they're registered, how many buildings they have, how many employees. And so forth. The issue with this, and I think where you're trying to understand how it works at scale, is instead of doing this one at a time and looking on LinkedIn and all of these different data sources for all this information, you can do this at scale in parallel, meaning you can research company A, B, C, D, and E all at the same time to kind of speed up your working. And this is how I'd start to think about it contextually. You want to understand What are the relevant data points in a company? Let AI do all of your hard work to do the research and then compile it in a way that makes sense for you. Okay. Google just hired— Google is hiring all of these DevOps people, right? Developers. And they are having all of these job posts in India or in Germany or in the Philippines. Okay. What does that tell us about where they're, you know, where they're trying to penetrate their accounts or their their geos, so on and so forth. So depending on what research you need to have done, you can just do this. You can type into AI now, GPT, or even Claude or Gemini, do deep research on this. So it's just a matter of clicking the deep research node or writing your own one-to-one prompt to be able to do that research. I hope, I hope that this has made it a little bit clearer.

Let us know, Rafael. In the meantime, we have one more question. Which are the sources of signals?

Yeah. So I, because I work mostly in the B2B sector, I would actually, let me correct that. I only work in the B2B sector, sometimes B2G as well. The signals are going to be completely different. What are the sources of the signals? This could be as simple as going onto Google Alerts, or even when somebody posts a job on LinkedIn. Maybe you have something as simple when you go on your LinkedIn page and you are a recruiter or you're open for work and you're saying looking for jobs with this, and then you get notified. That's a signal. When one of your target accounts as a person posts a job, that should be a signal for you to go apply for that job, right? But that signal, that market event could be meaning something completely different for the company when you look at it from the company lens. So let's use this job change or this job posting signal as a source of truth, right? On LinkedIn, a lot of people are going to post the jobs, but a lot of people don't post jobs anymore. They kind of just go and ask their network, right? And at the same time, you need to contextualize that signal. You can do this a lot with ZoomInfo. You can do this with PitchBook. If you're looking for like M&A activity for marketing, uh, mergers and acquisitions, PitchBook has really good data on this. And it tells you when the deal was created, how long the deal was taking place for the merger, so on and so forth. If you're looking for just job changes, you can look at something like Signalbase for, uh, or if you're looking for job basis changes over time, Signalbase is also really good for this, but so is SixSense. SixSense tells you who's in your market when they're in the market and contextualizes it for you. So again, there are multiple sources of those signals. I would say at the end of the day, it comes down to public viable information via OSINT. So open source intelligence. If it makes sense for you to look that up, or if you just type into Google news, colon colon, like about the company, you will come up with a lot of company news. You can also do this originally and set Google alerts for these types, RSS feeds. When somebody posts a blog, we want to contextualize that blog or some, or something of this sort. The sources where you're going to get that information would differ depending on your app. The last question I see is actually, can you dive a bit deeper into automation of the signals capturing? I, yeah, I'll sum this up like very, very quickly. Multiple compile multiple data sources for you. Dan, I'm not sure if you're going to dive into this, but when we start to talk about signals and collating all of that data. The more signals and the more data one of these platforms is going to have for your market, the more you're going to end up paying because they've already built the tool, right? So diving deeper into automating this, if you wanted to do it on your own in clay.com, it's just a matter of running something called a cron job where you refresh the signals and you refresh that table over time to figure out, okay, these 10,000 companies, and we want to look for Google News and Glassdoor reviews and stuff like this every Monday. I want to make a report for my sales team. Boom. That's how you would set it up. You just set it up so it's automated. That's it. It's just as simple as setting a cron job. If you're working at something like Claude Code, you can set something up called a routine that actually just got dropped yesterday. It used to be scheduled tasks, but now you can set it up as a routine and it can run on a routine. That's exactly what it is, right? And it will scan the market. It will do the deep research and it's automated. It's a workflow. It goes A to B, it turns on a certain time and it's done. That's how you would automate it to start. And if you wanted to do it in reverse to capture it and then filter, you could set up listeners with Google Alerts, go onto googlealerts. I think it's like google.com/alerts or something like this. And you can capture keywords that pop up on Google News and then figure out which ones of the companies that are mentioned in those news alerts Series B CFO appointed was one that we did for a client. This news came up on Google Alerts. Series B CFO, they were the keywords we're listening to, but I had to contextualize it before I put it in the CRM. Series B unfortunately brought me around 10,000 results for Series B baseball in the United States. Not ideal for CFOs and listening to the CFO, talking to the CFOs of the baseball committees and stuff. So I had to filter it by the news to contextualize it, or I could go from the company side first, then go find the news. So it's either, are you hunting the news or are you listening for the news? And then I would set up the automation on the backend to follow that logic.

Okay. Uh, let's stop here. Uh, I know we could talk for hours probably, but we need to get moving, but it's really interesting. I would also like to know. So guys, if you have any more questions for Spencer, Leave it in the chat and I guess you can answer later. Also, for the resources, they will be included in the library. Spencer will send it to me and we'll include it in the speaker library that is with the Pro Pass that you guys purchased. So thank you very much, Spencer. This was really great. I have a lot of ideas. I guess the audience has as well. So Thank you very much.

Of course. Happy to be here, Dan. Have a great chat, man.

Awesome. Thanks, Spencer. I'm gonna need to follow up with you on some of the stuff you talked about, cuz it's like some real high-end shit. Really good stuff.

I'm listening for your chat in the background. Don't worry.

All right.

All right.

Thank you very much, Spencer.

See ya.