B (3:57)

Thank you so much for having me. And I appreciate the shot at the bow to the aisdr. I have yet to find anyone that has had good experiences either.

A (4:04)

Oh, man, we gotta start talking about our experiences more. You do, though. You do. You definitely do. So they call you the OG GTM engineer. I call you the Mr. Rogers of Revenue. So I think that needs to pick up. But tell me more about how you got that nickname.

B (4:24)

Well, the Mr. Rogers one I've never heard before. That's a good one. Well, I've been doing this work since 2020, since, you know, Clay was, I don't know, maybe four years into their business at that point. And. But. But we used to have to do, you know, back. Back in my day, we used to have to look for, like, keywords on websites and we had to build our own scrapers from scratch. And. And so we really were building a lot of these workflows in the early days to be able to basically programmatically identify with, you know, observable public data the problems that companies were struggling with so that we could write a message about pain. Not. And so. And really, everything that I have done in this business since 2020 has been to try to work backwards from the customer, from your. From basically my customer's customer. Right what situation are they in? Why are they buying? How do they get here? And then what insights do you have from your other customers where you can deliver independent value to the person receiving your message? Right. And one of the frames that I use to sort of convince people that the current way is broken is that if you think about personalization, imagine if you got a hundred times better at it and you could say, Jordan, I know that you only brush your teeth on average once a day, not twice a day. It's like a super personal thing that might or might not be true about me. Well, it's very personal and it doesn't actually matter that you know that. It has nothing to do with the fact that I'm going to buy your B2B SaaS software. It's just totally transitionally related now. It's a different thing. If you knew that I had got a job three months ago and the purpose of that job was to bring in lowered AWS spend. And as part of that job I had to make sure that it connected with X or Y or Z tools. And also, you know, my organization had increased their spend on cloud computing. Now, independent of how you get that information, knowing if you, if you had that information, that would make a much, much, much better message because you would know who to send it to. And so I think that that's the big problem that we're struggling with today. And it's a quote that I think of very often, which is we shape our tools and thereafter our tools shape us. And so we've gotten so used to thinking in zoom info and Apollo filters, that has really crippled our ability to be creative about why our users buy. And if we could build a list with the information just structured on the public web, you would be surprised at just how much better you could get. Not only at targeting, but by improving targeting, you also improve the quality of your message because you know why you're sending the message to someone and it has nothing to do with the headcount and has nothing to do with the.

A (7:07)

Revenue that is so relevant and hard hitting. And reminds me of another idiom that the list is the message. So you evangelize that a lot as well.

B (7:17)

And I remember, that's my quote.

A (7:18)

Yeah, that's yours. The list is the message.

B (7:21)

Okay, so this, that's a Jordan Crawford original.

A (7:23)

That one sent me out for like a month. I remember like a year or two ago when I first started following you, I was like, hold on, this is changing everything. This changes everything. So awesome. We're going to get into a little more context. Before we go into this live build here, I just want to make sure that we call out that Jordan is the OG GTM engineer. However, this is not something that you have to be an engineer or identify as a technical person in order to be able to accomplish something like this. Actually, it's the opposite. Right. So the reason that we wanted to bring Jordan on the show is because he works directly with the CEOs CROs and like, so that is who Jordan actually works directly with and has a very clear perspective that you should own this. Right. And so don't delegate this out. And so obviously, in order to be able to do that, Jordan, his whole perspective is also around, here's how we can do this practically. And of course, you know, you could hire Jordan to help you with it, or you could, as we're going to show you today, accomplish a lot of this actually on your own, using tools that you probably already have or very low friction. And so this is something that we just want to make sure this isn't going to be like a technical architecture CRM sort of deep dive with us guys. This is very practical. Anything you would add to that, Jordan?

B (8:45)

Well, you provide a beautiful segue about why AI has changed this work. And so in the past, there used to be a huge difference between words and work. And so you would have to send alpha minions of people to go and muck with all the knobs and Salesforce, right? And you'd have to spend a bunch of time to configure six sense or demand base and to launch a campaign. And, you know, and so it doesn't make sense for a go to Market executive to sit in Apollo and like muck around with filters. It's just like not a valuable use of their time. But now your words can be code, which just means that you don't have to know. And by the way, I'm not a coder. I think Python's still a snake. I've just barely heard about the coding language recently. And so it doesn't actually take, you know, a mad scientist to be able to do this work anymore. And this is what I'm here to sort of convince people that just ChatGPT and Claude, you know, maybe Gemini, these three tools are just absolutely plenty to do most of the work to figure out what your go to Market team should do, how it should build list, and by the way, test these things in very short order. And so I think that as a leader now, for the first time, you're going to have to start doing work like and management's not going to cut it anymore. And that doesn't mean that you're going to have to get into all the tools, et cetera, but it does mean that you now can actually go build something yourself. And if you know what is possible and by the way, what is happening on the ground, you kind of know how to reinvent work around AI. But OpenAI did a study recently about how AI is impacting jobs and it's really just taking tasks away from people. And so in order to really understand what's happening at the ground of an organization, you need to be able to, you know, get off the, the well anointed chair and step down and, and go see what your SDRs are prospecting and really get into the weeds of it. And when you start playing around these with these tools, your imagination can become action very quickly. And in the last six months or so, I call vibe coding, like asymptotic coding, which is you get very, very fast and then you can plateau forever and not actually cross the value threshold. But club code I think was one of the first tools to help tip that where it's actually pretty good at helping you get over the value threshold so you can actually ship something that's useful. So what I'm going to do today is I'm going to teach you how to focus with the models, to go find useful data and to go turn that into the structure behind beautiful list and beautiful Messages, just with ChatGPT and your voice. And so it's not like again, I'm not an engineer and so you don't have to be one either to be able to do this.

A (11:32)

Awesome. Thanks for the context. Yep. So today that's we're what, that's what we're going to get into a live AI GTM build. We're going to go from idea to data to message in 45 minutes and we're going to get into it. But a couple more things things before we jump in. Jordan, you mentioned this is something where leaders, Even you know, CMOs, CROs, CEOs, we're going to be doing more work and it doesn't mean coding as you said, like let's, let's define what that is. But what you're recommending is that we stop offloading strategy. And so tell me if this is right. The way I define that is ICP definition. Who's our icp? Stop offloading that onto a rev ops person or you know, like a marketing manager. You're messaging your TAM building Like stop offloading that. And so what we're talking about now in this build is how to go, you know, start that at a high level yourself and then set that direction strategy. But how does this differ from some of the status quo segmentation like firmographics, demographics, icp, like, it sounds like we're talking about something that's a little more creative, a little more nuanced.

B (12:47)

Sure, yeah. And the distance becomes measurably obvious when you look at the message that the SDRs are sending. And so let's envision you're doing it in sort of the old playbook. Right. You define your ICP. It's B2B SaaS, companies between 100 to 1,000 employees with $20 million plus in revenue that are in North America. Okay, great. And they're CMOs, CROs. I'm going after those folks, and that's what I'm doing. And. And they generally have these types of problems. So you do all that work and you, you have accounts that are scored. This is a 10, this is an 8, this person is a 10, this person is a 7.

A (13:25)

Yeah.

B (13:25)

So you do all that work and that gets to the sdr. And the SDR is like, hey, Jordan, I saw that you wear a hoodie. I wear a hoodie too. We're the fucking same person. Jordan, let's keep buying my B2B SaaS. Right?

A (13:35)

Right. Because you're an eight.

B (13:37)

Yeah. Cause I'm eight. I'm a San Francisco eight, I'm a New York seven. This is what people are doing. When it gets to the sdr, none of those scores are useful. None of the fact that someone's in an ICP is really useful. The Persona level messages actually aren't useful because they speak to a fictional thing. And so my sort of process here is to actually work backwards from the customer. So go take. And segmentation is really important. So go figure out what is the grouping of your customers that had the same problem that they were, what is the situation that they were in. And then just drop all the transcripts for one customer into ChatGPT and for a recently closed one customer and everything other context you have about them and your business and say, yo, chatgpt, spend an ungodly amount of time going online and searching for any possible public indication that Jordan was in this situation before we had a conversation and do that for 20 of your best close one deals that are all in that you believe to be in the same situation. And you would be immensely surprised how much amazing public data there is. Out there that will think that you never thought about. And so this is why you kind of have to work backwards from the customer. And if you do that, you can identify, well, what is your unique insight? Well, they all came to me because six months ago they tried this piece of software. And the problem with that piece of software was it didn't actually do this thing. And this thing was really important for GDPR reporting. I don't know, making shit up. Okay, great, now you have a story. And who does that story apply to? Well, that's anyone that installed segment and I'm picking segment. No, no, no shade. This is a made up story segment.

A (15:25)

No shade.

B (15:27)

So you know, six months ago you installed segment and then turns out segment doesn't do some GDPR thing. Not true, not true, not true, not true. But if that were the case, then you would say, okay, go look at their pixel installs, go back in time, use built with to do that, and then go figure out are they in Europe or have they just recently launched in Europe? Right? And now you have an insight about why your, why your tool, your CDP is better than segment in these cases and how now that group of customers is actually subject to some sort of regulation problems that they didn't know about before. And you can bring that insight to the prospect. And so this is where the list is, the message comes in, right? You can identify who has segment installed and you also can identify who has recently like, you know, got into the European. And there's sort of, there's registration for this, etc. Like if you have to register as a business, if you're doing business in the eu. So those two things right now it's a story and it's working backwards from the customer buying experience. And notice I didn't say anything about the, you know, the beautiful gray sweater that you have on or your pink background like, or the plant behind you. Right? That had nothing to do with anything. But I actually took an insight from my existing customers and brought it to you.

A (16:39)

We've laid a lot of acronyms on Jordan, but I'm getting Ironman vibes right now. So before we get into more specifics here, I want to talk about. You said that tools given AI, like tools in general that we use, went from access to power tools overnight. And also vertical SaaS is the future according to you. So can you tell us a little bit more about why vertical SaaS specifically, what are the types of verticals? And like, if you're in, if you don't have a vertical position, right now, like, could you still do this exercise?

B (17:14)

Yes. Yeah, absolutely. So, you know, I did this thing called a go to market cannonball, which we now do every week on, on Fridays at 9am Pacific. And the whole exercise there is to get to demonstrability, which is you really want to dive down almost the feature level, where what is the very specific thing that you can say that none of your competitors can say that if you receive a message that only you can say that. Sometimes it means blending your own internal data, but usually it means getting down to something so specific. And horizontal SaaS companies have a really hard time because their knowledge fractures. It doesn't compound. So imagine if you have five use cases and six Personas and four products and 12 industries, right? Well, the combinatory number of reasons why customers buy is like near infinite. And so you just have a really hard time telling a story. So what ends up happening is you just talk about you. You just say, here's all the things about me, which would be a deranged way to approach anyone in public, right? Like, hi, I'm Jordan. I'm a blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah. And you're like, hello. You're like, oh. And yeah, you know, and then you say like, hey, Jordan, I don't, you know, I'm, I'm, I'm not into plants. I'm like, oh, I'm a, I'm a plant guy. So what are you, you know, it's like, what are you talking about now? And so that's sort of where the, the problem occurs. So vertical SaaS has it easier because if you think about selling to plumbers or banks or whatever, a bank in Tuscaloosa and a bank in San Francisco are not doing radically different things. It's not like they're like, you know what we should take, you know, we should take Italian currency. We should take the eu. No, we should forget about that. We should transact in Bitcoin, right? No, they're all doing kind of the same thing, which means that the more banks you acquire, the more you understand their perspective, the deeper you, you understand, the easier it is for you to gain insights. But if you have one B2B SaaS company, a plumber, you've got one person, oil and gas. And they're all doing different things, right? You can't, you end up speaking about you, not about them, because the, the mental effort to segment your customers is so hard. But ChatGPT will help us here. And so the way in which you do. This is you just need to get super demonstrative very, very fast. And the point about access and power tools is like, this is why we had management, because we had to have a bunch of axes and we had to go teach a bunch of people how to use a bunch of axes. And then we say, you know, all together, we'll chop down this tree. But if you have a, you know, a chainsaw and you can cut down a tree, as an executive, you should at least cut down the first tree. You don't have to cut down all of them. I don't know why this went into a deforestation metaphor, but let's assume that cutting down trees was helpful. The environment, okay.

A (19:58)

We can use the power tools to put it back together.

B (20:00)

Yeah, yeah. Let's say you were. You were building or planting more trees with the chainsaw. Somehow. You should go plant the first tree with that chainsaw. You're digging in the ground with the chainsaw. So this is just why it's a little bit easier.

A (20:13)

Okay, cool. So even if you're not a vertical SaaS company, still listen. So let's. Let's go. Let's go. I think everybody's jumping out of their chair. I'm jumping out of my chair.

B (20:21)

You want to dive this? Dive in.

A (20:22)

Let's dive in.

B (20:24)

Okay, great.

A (20:24)

All right, take it away, Jordan.

B (20:27)

All right, now I do my screen share, right?

A (20:29)

Yes. I see two windows.

B (20:31)

We can. We can get to the moon, but screen sharing is sometimes still a problem for us. Okay, so I just picked a rant. I don't know anything about this business. I. I've never worked with them. I just had ChatGPT pick a random business. So Arcadia is like solutions to power your clean energy future. And the way that, you know if a company has no.

A (20:48)

Oh, we are atoning for our deforestation.

B (20:50)

Yeah. Yeah, that's exactly right. Yeah, yeah. It's like, that worked out really well. You landed the plane. Thanks. So generally, if you look at a horizontal B2B SaaS company, what their H1 will be is like, the future begins today for tomorrow's better achievement. And you're like, what does this mean? And so vertical SaaS is a little bit easier. It's like, at least I know what they're doing, right? They're like, archiving towards clean energy. So all I'm going to do is I'm going to do something on the left window here. This is like ChatGPT. I've. I've selected ChatGPT 5 thinking, and I did heavy Thinking, actually, maybe I'll just do extended so that we can get through this podcast fast enough. But generally I would do heavy thinking. Okay, actually, I can do Stan in this case, and I'm just going to use a tool called Super Whisper and Talk and then I'll explain kind of what I just did after I do that. What I want you to do is I want you to go online and look at Arcadian, understand their ideal customer profile. Now, what I want you to do specifically is you need to create a dossier that is everything a CRO would need, a Chief Revenue Officer would need to be able to take this company to market. And ideally, I want you to focus on, like, what do actual customers say? So heavily biased towards what customers are saying, what types of customers go get their. Their case studies and spend a lot of time here. This single document needs to be everything that I need to understand all of the types of industries they sell to, what are the technologies? And when you come back with a solution, I want you to structure it by the types of companies and Personas that are buying and provide that to me in sort of in a structured way so that I can evaluate if I'm going to run a Go to Market campaign, which should I target and why. And ideally, you're going to order and rank those based on who needs this software the most, where there's like demonstrable public evidence that can be connected to public information to identify people's pain. So I just use a tool called Super Whisperer. There's another one called Whisper Flow, which is. Which is also pretty good. And just so I can talk. But you don't need to do this in ChatGPT. There's this little dictate button here that you can just press. So you don't even need any fancy tools. I just like to use it because I'm a big fan of the founder. But you can just use this dictate tool and ChatGPT. It's very good. And I would say ChatGPT's tool is way better than Claude or Gemini because they built the technology around this translation and it's quite good. So let me just explain what I'm doing here. There is a concept that I have called creative constraint with context, and this is where the models perform the best. And I should know. I've spent more time talking with the models than people, which is a disgusting statistic, but it happens to be true. And so what you want to do in this case is that you want to provide a context about what you're trying to do. And so there's always kind of a question behind the question. And what we're doing is we're just providing grounding for ChatGPT. So who is this company? Who do they serve? And generally while I do this, I'm going to spend time like looking at the website, making sure that I have like ground truth here to say, like, okay, well, who are they? Now, if you actually work at this company, you wouldn't have to do this, but for like, illustrative purposes is very helpful. Right. So use cases, solar and storage, EV charging, property management. So property management and like solar and storage are going to be totally different areas to focus on. And so I wouldn't ever like message a Solar and storage. I don't even know what a der company is, but you can see like Aurora Sunrun. Right. You're not going to say the same thing to those people that you would say to whatever that other case was. Right. You're going to say way different things. And so the first thing that we're going to have to do is figure out exactly who are we going to go after.

A (24:37)

Yeah, this is a really hefty example. I don't know if you picked such a comp. They call themselves a platform, so. Yeah, yeah, this is.

B (24:45)

I really kind of screwed myself here, so I picked a hard one. Yeah, I didn't, I, I didn't spend any time thinking about this, which is sort of my Mimo. So, so what we're going to do is like, we're going to look at their different segments. So Chat GPT did like a nice little benefit for me, which is like, what is the segment? What are their core pains? Who are the primary buyers? Why Arcadia? And then public triggers you can track. So this data, connecting the data to the pain is immensely. Think about this. Just like pause for a minute. We live in a wild time. Think about how long it would take you to do this if you just had to Google, you'd never be able to do this. And so, and remember, we're not having chatgpt make things up. It's actually telling us about like data sources that we can find and we're going to structure that data, not just like, hey, chatgpt, tell me about the new site announcements. No, we're going to go get APIs, we're going to get structured data here. So we have kind of, we basically have six options to go after. And this is a per. This is a, this is a vertical SaaS company that has like horizontal vibes, which you know, kind of screwed me here, but it's probably more relevant for the audience.

A (25:56)

Yeah.

B (25:57)

So now, now we need to face basically figure out like who actually do we go after? So what I'm going to do is I'm going to have ChatGPT help us pick so that we can actually narrow down because we want to get down not to just an industry and use case but like ideally a feature like where we can match this with real good data.

A (26:16)

Yeah. So for folks who are just listening, can we just popcorn a couple of those?

B (26:21)

Oh yeah. So yeah. So let's talk about the segments. I'm just going to read maybe the first one. So I'm going to read the segments. So we've got EV charging networks and OEMs, multi site enterprises. So Fortune a thousand energy and carbon software platforms, solar and storage solution providers, procurement and data heavy center heavy enterprises at proptech right now. Do you think a proptech company and a EV charging network have anything in common? I mean, I mean except that they probably all argue at Thanksgiving. There's really nothing. My guess is that they have.

A (26:51)

Right. The typical argument would be, well, they do because they both need a carbon, you know, track.

B (26:58)

Yeah, Jordan. Yeah, that's exactly. It's like, are you, why are you using Acadia? Oh, it's great software. It's like, yeah, they're only negotiating getting about the B2B SaaS choices at Thanksgiving. That's exactly right. And then ChatGPT is like, what are their core pains? Who are their primary buyers? Why Arcadia and public triggers you can track. So for the EV charging network, it's like thousands of meters across utilities, volatile tariffs, billing disputes, site selection, economies. The primary biters, head head of charging ops, head of energy regulation product for home charging. Why Acadia? So EVgo, it's just a signal, fixed bill accuracy, save tens of thousands in time. So Ford has 95% coverage in North America. It saves 20 to 30% savings for drivers via optimal plans. That's pretty huge. Right. And so notice I asked like customer evidence and what I'm looking for here is, you know, Fox 70, 80% emissions data from bills Arcadia automates with net zero cloud. So Arcadia, my guess is reducing is like helping Fox get to net zero. So I'm just kind of looking at this and then I'm gonna have chatgpt like help me pick. And so let's kind of move forward and have and this is just context for ChatGPT and I'm gonna have it help me sort of dive in deep. All right. What I need to do now is I need to pick one of the six to go after. Now here are some heuristics that are important for for identifying the best segment. I'm looking for both. Demonstrability. So what is something that is so obvious that I can say it where there's public data that allows me to say it so specifically. So you looked at some public triggers and maybe you can spend a little bit more time thinking through where is the biggest amount of obvious evidence that I can sort the market with basically near perfect information from public triggers or public information. So I know if they have a 10x bigger problem that they should be using Arcadia now. And so for example, one of the things that you might consider is like the multi site enterprises Fortune a thousand. I probably don't want to pick them even though there's grain emissions data because my guess is that there's not going to be some interesting thing I can find on the Fortune 1000 that you know, my competitors can't. But maybe for the utility like the energy and carbon software platforms, I don't know how big that is, but like a job post I generally don't like, it's not as good. Structured data tariff engine might be interesting. The compliance stuff is very interesting. That data is usually heavily structured. So really what I want you to do is I want you to tell me which segment I should start with based on the combination of the demonstrability of Arcadia's value prop, the public data to be able to identify that value prop and also how I can connect the two to create a message that provides independent value before you even buy Arcadia. So help me think about the structure of these segments in these lenses. So I know I said a lot and but, but the point is here all we're doing is we're trying to get more and more and more specific. We're trying to dive into a vertical, a use case, a specific feature where we can say something that our competitors can't. And ideally we want to understand how the public data is connected to that information. Does that. Do you want to like while this thing is thinking, do you want to sort of, yeah.

A (30:46)

So when you say we can say something that our competitors can't, is that because we've done this exercise and we're bringing our own brain to the table versus our competitor doing the exercise or is it something specific to also us because you used the customer example. So had this not been a company that had done it sounds like they have like, you know, PR engine sort of stuff going on so we, we can see on the Internet their customer case stories. But if, for example, if you, you don't have that many like ChatGPT can't go find a bu of things that you've published or that your customers have said about you, you could still load that information in elsewhere.

B (31:26)

That's a great question. And the thing is we're not really using any internal data here. So the defensibility is just that you'd be one of the first to deploy this methodology. As far as I know. I'm like one of the very few. Now more and more people are taking my methodology because I make it public and I try to teach people it. But my guess is there's not many people of the competitors that are doing this yet. But ideally what they would do is they would leverage their internal data. My guess is that Arcadia has all this sorts of public data from all of its customers that allow it to have. Especially if they aggregate the data, have secrets, secrets their customers can't copy because it literally exists in the data. Now I'll give you an example for a secret of a company like housecall Pro. They are a CRM for, for the trades. And so one of their secrets that they could deploy is they could say, hey, hey amber, you're a H Vac technician on Healthcall Pro. We looked at your installs from 20 years ago and what we've done is we've actually found that those customers that installed ABSG XY that you installed this H Vac unit for, they need to replace. Now it's 20 years old and we've looked across all of our other customers, that person is still there. So Amber still lives at the house. And by the way, she hasn't had any other technician in our network come and update her H Vac. So go contact her because it's time. And that would be a message. In this case, that would be an upsell message, but that would be a message that Housecall Pro could send to its existing customers that no one else could. Right?

A (32:59)

Yeah. The secret. Yeah, I like calling it a secret.

B (33:02)

Exactly. Okay, so start with PropTech CBRE owners. They have the clearest quantifiable penalties, rich public data sets. And you can deliver building by building, fine forecasts before any sale. Whoa. Huge.

A (33:19)

Let's break this down because I do not know what that just said.

B (33:22)

Yeah, okay, so. So let's like break it down. So we're starting with Proptech, right? Remember we had those like six different segments, right? So we're selling now into people that are, are buying. Right? Or companies that are selling into those that do real estate. And so there's, my guess is that there's some penalty about carbon emissions and you can determine that at the building by building level. So imagine a case where a piece of commercial real estate is up for sale. You could say something like, hey, Jordan, if you buy this thing, you're going to be charged this exact amount of CO2. It's over the cap for New York City, so you should know this. And by the way, here are steps to mitigate that after you buy. So this is an example of a secret that's just compiled with public data where you could forecast their pain ahead of time and provide them independently useful information just by blending public data. Does that make sense?

A (34:22)

Yeah, it does. And it's, it's also making me realize, because I've seen you do a few of these now too, that like how often you're tapping into something that's an analog pain that's currently solved or just fly by the seat of your pants. Right. So like, yeah, let's keep going. I'm not going to interrupt you.

B (34:43)

But yeah, no, no, no, it's a good, I mean, it's a good point. And I think that, you know, we'll read kind of on the screen. So for example, penalties are formulaic. So this is for CO2 emissions. So it's $268 a ton for CO2 over the cap in New York City. And basically the, the New York City government like publishes this data. Cities publish open data sets for covered buildings, historic energy use and compliance status. So these are things that, you know, if you were to look at this in the Google area, you'd be like, this is not worth it. Right. I can't make sense of this data set. And we'll just like kind of click in like greenhouse gas emissions reduction. Right?

A (35:21)

Okay, so now we're looking at what, a nyc.gov website?

B (35:25)

Yeah, exactly. And so like LL97, right? Like, okay, you're probably, I mean, I'm lost. I'm like, what's LLC 97? But the nice thing is large language models can help us understand that. Right. In this case, it's local law in 97 of 2019. And so it sort of talks about like, there's a service notice this June 16, 2025. So it's like relatively recent. What are the covered buildings? Right. So we have some understanding about. It's like if a building exceeds 25,000 gross feet, like or together there are two buildings that exceed 50,000 gross feet. So this is what getting into the weeds looks like. And then there's a what? Of course, everyone has to have an acronym CVL covered building list. So basically I can find the. I'll just like look at the PDF because it's a little bit easier. Like I can go find all of the. Look, these are like websites or sorry, addresses. So like, what's the address? What's the zip code? So I have a really good understanding of like when compliance begins and when they're going to be subject to certain types of fines. And I've got this gigantic address list, right? So imagine if you were a. And this is, you know, buried in some New York City doc. If you're buying real estate in New York, my guess is that you might not know about this. You might not know about their recent. Especially if the law is up and coming, right? So we have a change in the world that is relevant to Acadia and Arcadia and is probably something that they can help mitigate. So this seems like actually like a pretty good place to start. And remember, I didn't know anything about this brand before I came in. So, like, I'm not some like genius energy wonk. Like, I don't know anything about this. And this is also why it's helpful to have an executive that is looking at this because they might say this and they. And especially if they have an. You probably want someone that understands that particular segment doing this too because they might say, oh, Jordan, that's in the disclosures. The disclosures always include the rate. So this might not actually be interesting, but you should focus the processes is still valuable. It just might be that it's possible that this particular insight is not necessarily interesting. So let's see if we can kind of get a little bit further down the. Down the funnel and. And just maybe even challenge the model a bit. Okay, this is great. So what I want you to do now is help me figure out how exactly I connect these. Like, for example, I love that you said that many New York Sittings Pass 2024, but fail 2030. So like, and I'm trying to identify what are things that if I actually put together these data sets that the. That would be both inde they have to meet a couple of criteria. They'd be independently useful to the person I'm sending. Like that they wouldn't know this piece of information, that that information is like pretty timely and that it is at least related to one of Arcadia's value props. So what I want you to do is like give me the exact plays that I should run and structure them with like how I should blend the data, who I should send to, what does that look like? And just as part of that, compile a message that is the combination of that data delivering the value and the message should be no longer than three sentences with line breaks that just describe the insight there. They do not pitch Arcadia but just is about independently delivering useful value to the buyer. So go ahead and structure that information for me now and then at the end sort of critically evaluate it and say like would this actually be useful, interesting, valuable and timely information for the buyer that's related to Arcadia's value prop.

A (39:09)

Okay, so while that's going help orient us again here. So the first thing we did is we gave ChatGPT context about the business and then we said hey, go look at all these things including customer success stories and come up with essentially like six different pain qualified segments or six different segments.

B (39:28)

Right, Stop me there. No, I actually didn't, I didn't qualify segments. I just literally said describe all the people these folks sell to. So I wasn't even segments, I was just like. It's just these are, these are the industries that they sell to. So they sold to six industries. So that's how we begin. And we need to dive further down because remember I'm not going to say the same thing to a solar company that I will say to a real estate developer, for example, ahead.

A (39:51)

Okay, so we started there and now we're layer, now we're getting deeper so that we can create the pain qualified segment.

B (39:59)

Yeah, exactly. So the pqs Pain qualified segment. So remember we had a bunch of different. We had PropTech, EV charging, multi side enterprise energy and carbon, solar and solar. Solar and storage procurement, DC heavy. And it says okay, you should start with proptech and we skipped over this. So I'm glad that you're asking me to go back here. So demonstrability of pain, explicit fines. So there's fines, right? Anytime that there's like money, anytime there's money transacted, this is a good thing, right? There's public data richness, so there's open and structured data, the you know, the exact fine and forecast plus tariff aware savings and can you sort the market this way? So remember we talked about ICP score, right? And there was a 10 versus a 5. Well you know, usually that is like who has a bigger wallet? So Amber, if you have a hundred dollars or ten dollars, I'm gonna be, I'm gonna Want to sell to you more if you have a hundred dollars. But, you know, if you're, if, you know, if you brush your teeth regularly and floss, you don't need my dental services. Right? And so it doesn't actually matter how much money you have. If you have, you know, if you have 10 bucks and your teeth are falling out, my guess is that you would pay two bucks to fix that problem. Right? Whereas you wouldn't pay $2 or any amount even if you had 100, if you had no problems with your teeth. So that was a pretty grim analogy. So this is basically what that's saying, is that start with Proptech, because the way in which I'm going to be able to sort the market is by actual fine amounts. So I know that you're going to have a $10 million fine. And if Arcadia can make that fine, even if they can make it $5 billion, let's say they can't wipe it away. That, that's, that's a demonstrable way to sort your market based on actually how much money your customers are going to lose if they don't buy your software. So now, remember, we talked about the list is the message. If you sort your market in this way, well, what do you say, Amber? You have a $10 million fine up and coming. Here are the exact sources and the laws that are going to. And I hear the properties that you bought that will incur these fines. And here at the distribution of those fines, we can help you reduce. And you don't even have to say anything here. You can just say, like, if there, if you, if you knew that there was a way to reduce these fines from 10 million to 5 million. Is that worth a conversation? Oh, yes. I did the work, I sorted the information, I determined it was valuable to you. And I know I'm going after you because you have the highest amount of upcoming fines. So does that catch us up with where we are?

A (42:33)

Yeah, and I think I'm having light bulb moments too, because I'm starting to see the disconnect between your methodology and, like, even what we see. I was talking to a customer today who uses a scoring methodology, and it's somewhat sophisticated. They did a walk back from their customers. They looked at who buys from us at the highest velocity, you know, best close rate by industry and, you know, role and all these things. And they're like, hey, this is why our score is this way, and that's part of it. But they actually don't know why. So, like, for example, they could have actually developed the score in this example that shows like, hey, a prop tech company high score. They're an eight, just like Jordan. But they wouldn't actually know why. To really be able to engineer that.

B (43:20)

Yeah, the score is like, these scores are our mental masturbation for executives in many ways, because they're selfish scores. And if they're selfish scores, it means that the SDR will look at two eights and not know which eight is more 80. Or look at an eight six and be like, sometimes the six might be useful because they're not scored based on why people buy. They're scored on who you want to sell to. And so what is the value of that? Of course. Who do I want to sell to? I want Tim Cook to buy everything that I've ever done. Why? Because Tim Cook has a gigantic wallet. You know, it's like. But. But that has nothing to do with the like, he doesn't need go to market engineering services. So it doesn't make any sense. Okay, let's sort of talk about how these plays. So start with New York city and the 2030 cliff and exposure by buildings. So this is what we looked at before, right? So there's building energy snapshot. So we know at the building level, the emissions, the limits, and the projected fines of 2024 versus 2030. So we can basically calculate a delta by rank and then forecast your fine, the future of your fines for your entire building portfolio. So send the sustainability lead, Asset Manager and CFO, top 10 buildings by 2030 Delta. So, hey, by the way, your building clears this 2024 rule, but it's going to miss by 2030. And here's how much of CO2. And by the way, don't. ChatGPT's messaging sucks donkey balls. So don't worry about that. Like you're gonna massage this message. But so just like focus on the bones here. Basically implying X amount in years in fines under current rules. So I now have said, I'm not sure if you know this, but I've actually calculated what you're gonna have to pay in five years and how much that is. Like here is the gap is driven mainly by. By like this amount of fuel and like carbon coefficients. And basically, do you want me to provide the rank for all of your buildings? And so owners often know the 2024 status. Right, because it's recent but not asset level 2030 deltas, it's timely and tried directly to penalties. Right. So this is really important is that when you send a message to the customer, it has to be both independently valuable to them and also they have to not know it. And so, you know, we've run plays in the future where it's like, hey, lawyers, did you see the Supreme Court case? Like, yeah, it's my job to read Supreme Court cases. Right. So it's. I'm not actually providing any unique value there. I'm just insulting their intelligence.

A (45:56)

Got it. And that was just one play. So it gave you a few different plays.

B (46:00)

Yeah. Okay. So the New York City late file, cash leak washloads. So there's a grace window, and then there you get fees for every missed report. And so you can basically find the buildings that filed late in previous years. And so you can output the monthly burn. So that means that you have to submit, apparently have to submit things to New York City at a certain amount. And if you don't submit at a building level by certain times, you get charged. This is $0.50 per square foot per month, after every month that you're late. So I could go to you and say, hey, Amber, these six buildings cost you $2 million last year just because of late reports. And so there's a compiling challenge for these buildings. I don't know if you fixed it yet, but here is the path to save you that $2 million. And maybe actually Arcadia, I don't know if Arcadia's software can do this, but you could basically say, hey, we'll give you the report for these buildings now for free on us, just so that you don't have to worry about this upcoming $2 million bill that you're likely to face if you haven't made any changes since last year. Pretty useful stuff. Right? And then we'll just look at, like, maybe one more. We don't have to sort of belabor this point, but. And let's look at one outside of New York City. Fuel switch advantage penalty Delta from electrification. So I hate benchmarking data. Maybe I don't like this one. A cleaner grid. Oh, penalty delta if fuel oil, gas use are electrified. So my understanding of this is still the building level. So building pays these fines on current fuels modeling. Basically, if you switch to the type of heat you're using, and here's how much that will cut to reduce your fines. And so this is like an example of something basically, most owners haven't quantified penalty impacts of electrification using the law's published coefficients. Right. This is just where you have a variety of assets. And my guess is that you're likely, if these laws are in 2030, you haven't done that math yet. And if you're dealing with buildings, my guess is that you definitely like 5 years planning is not an unreasonable amount of time to be able to know this. Right. It's not like you can install a fan overnight to or solar panels overnight across an asset class of thousands of buildings. So that's kind of what this, that's sort of what this.

A (48:28)

Just calling out again, the difference between these plays and what we typically see, even with go to market engineers or folks trying to do this using AI is like number one. It's really just like nuanced. Spray and pray, spray and pray using, you know, ICP or something else. And then when you're or personalization, yeah, you're just personalizing everything with the color of my sweater, but you're not actually giving value. And so like there's just so much to unpack here. So I encourage everyone to, to go follow Jordan Crawford. That's Crawford with A C on LinkedIn to see a lot more of this in practice. But there's a lot more to the message that so different than what we see in our inboxes every day that I mark a spam immediately, but. And so yeah, delivering that independent value like you're, you're simply delivering independent value that's relevant to your business. Right. But not really pitching in your email.

B (49:31)

So. And the hard thing to do, especially as a horizontal SaaS company, is that notice how we went from basically six unique TAMs that they could have scored down to one sort of segment and then down to an individual value prop, which is like the tax implications. Right. And so by doing that we are able to build. Oh, and by the way, we found data sets. So we found data sets so that the model isn't hallucinating, it's just combining the public data so it knows what I want. And the model is acting as a data scientist, not a token generator. It's not inventing words. And that's how most people use these models. Right? It's like take Amber's LinkedIn and invent words to like get Amber on the phone. This is not doing that. It's saying be super demonstrative. And by the way, it's scalable too, right? You saw that PDF. It was like hundreds of pages of all of these addresses and what their 2035 implications are going to be for the entire city of New York. That is a massive play. There's plenty of customers there. And so, and so you see how fast I did that. And what we could have done is Downloaded those data sets and use Claude code and had it structure and pull together that information. We do this weekly on the Cannonball. There's not, you know, we could have launched a campaign in a couple of hours, especially if you sit with an expert from that industry that could say, oh, yes, we wouldn't know that, or they would help us. Help us guide. And that's what this looks like. And so we talked at the beginning of the show about. My goal is to solve three problems. Bad list, bad message, Too slow. So we built a great list. The message. We built an amazing message about the actual amount of money that you'll save and how fast did we do it. And so the thing is that you could say, jordan, they already know this, or they don't care about it. It's in five years. Well, okay, do another segment. And the thing is, it's so fast here. If you could then deploy this into channels, and generally we will. We will work with cold callers to be able to cold call this list really quickly. If I could deploy it this afternoon and call 100 building managers, and I could test it. And you know what if it fails? Well, it failed the first half of Thursday. I spent four hours shipping a campaign. Okay? So I take the next half Thursday, and I build a second campaign. And so suddenly, you have kind of muzzle velocity here to be able to ship these campaigns that are both fast. Good list, good message. And actually anchored against providing your buyers independently valuable insights that they didn't know before that are, by the way, related to your value prop. And I'll say one more thing about that, which is two good critiques here, which is like, well, Jordan, what if you get the data wrong? Actually, I have. And people still reply. They're like, actually, it's not used in that way. But by the way, can you do it for X, Y, or Z? Because they understand the process. And the second thing is that, of course, this doesn't go over absolutely everything Arcadia could possibly do for them, but it doesn't matter. You've opened a door. And that's what a sales rep's job is supposed to do. Right? It's discovery. And so all you're doing is you know who to contact and you're earning a conversation. And the channel doesn't really matter how you send it. Right. You know, we were developing messages, but you could do cold calling. You could show up at their office, you could send gifts. It doesn't really matter. You have the insight. You know who the insight applies to and how much value you can bring that company and it's just to open the door.

A (53:01)

Yeah, that's amazing. So as we're wrapping up here, I'd love to know, like, what, what do you tend to use with the customers that you work with@Cannonball GTM, which we're going to talk more about here in a minute. But do you do recommend a lot of cold calling or emails or.

B (53:17)

Yeah, yeah. In terms of, in terms of channels, people are like, so channel obsessed. Like, what's the best channel? What's the best channel? Yeah, the best channel is where your customers are. I would say that email is getting harder and harder as a general matter, but like, you know, if you sell into the junk industry, is email getting harder? I doubt it. Like, but do those people ever check their email in the first place? Like, probably not, but do they pick up their phone? Right. Is a plumber going to pick up his phone? Yes, because it could be a, you know, it could be a deal. So we generally like cold calling just because of the speed at which you can get feedback from the market, which is immensely valuable. So that's sort of where we, we will tend to start. But you can do LinkedIn automation with tools like Heyreach or you can do, you know, instantly or smart Lead for cold email, you know, Nooks or Aurum for doing cold calling kind of at scale. But don't be obsessed with the channel, be obsessed with the targeting and then you can figure out if, like, if you have an amazingly good insight. And by the way, let's say this work put off a hundred customers that had a hundred x the pain, which by the way, Arcadia knows is going to mean a hundred X the value. Do crazy things to get in front of them. You can afford to put a billboard in front of their office. Right. Because you know, the roi. And so you don't have to be limited to like, well, how do I, you know, do I cold call or do I cold email? It's like, well, this is going to be a $5 million a year account and you know, it demonstrably based on their projected energy fines. So, you know, send a singing telegram. I mean, I don't know. But yeah, don't, don't be as worried about that.

A (54:51)

Yeah, I love it. That's amazing. Thank you. And so, so much value in this. And so definitely, you know, save it, go back, watch the video, hit up Jordan on LinkedIn. You can also just, just grab the prompts that Jordan used and just resay them, you know, in your own words, as you're going through the exercise. I did that one time and totally helpful. I'm excited to use this example and do this and see what we come out with. Epiceto. But yeah, so many amazing things and I think just to wrap this up, like, the reason that your work is so impactful and I see you and I think, you know, a lot of us do see you as like leading this, this transformation in go to market from massively wasteful, you know, as we talked about in the beginning and like, how do we get our MQL to opportunity conversion up, you know, one half of a percent or 1%? It's like we have a customer right now that's, you know, spending a hundred thousand dollars a month on, you know, targeted ads and working with us. Like they can see exactly what's happening or not happening through those. Right. And so you have a good data foundation to be able to say, let's turn that off. But just imagine how much this is going to shift over the next few years as well as more folks start to use these tools to get so targeted that you don't need to go spend a hundred thousand dollars a month on these paid ads that, you know, or whatever it is that's not converting. You can actually spend five hours or with Jordan or you could spend, you know, maybe 10 hours if you're doing this for the first time yourself, to go build something that actually has such a higher propensity to teach, if nothing else, teach you more about your own business and what your customers are actually finding valuable. But you know, at best, like bring in like real qualified, paying qualified people into your pipeline. And the effort, once you get this kind of system going, the effort is just, oh my God, it's amazing to think about like the, the low level of effort and the, and the high effectiveness of it. So that's why I'm obsessed with it personally. So.

B (57:03)

Well, I really appreciated all of your, like, investment in, in this type type of stuff that I do. And, and honestly, when you see it, it's hard to go back. And so, yeah, like, clearly when people consume a lot of the content that I create and they understand what's possible, it starts rewiring, rewiring the way that they think about AI and go to market. And I think that's the thing for me is that it has the, it has the potential to actually provide useful messages to customers and like, there is no better value prop than that.

A (57:33)

Yeah. So if you're evaluating if you've made it to the end of the episode Amazing high five. Hope you got some great value out of this. And yeah, you know, if you're evaluating your spend for next year, you don't probably don't need more tech stack. You probably don't. Maybe you don't even need more budget. Maybe you really just need to get down to the first principles of, of, of who your TAM is. And so that is a super exciting switch. So where to find Jordan? Obviously you can find him on LinkedIn. You can find them at Cannonball GTM. And as he mentioned, he does live builds just like this every Friday. And that's your live show called the CannonBall. It's at 9:00am Is that Pacific Time?

B (58:16)

Yep, Pacific Time, yeah, 9:00am and those.

A (58:18)

Are streamed or you always post about it on LinkedIn like, hey, live. But it's actually a zoom link, right?

B (58:23)

Yeah, they're streamed on LinkedIn via Streamyard. And if you're a subscriber, you can go back and watch any of the historical recordings, but anyone can watch the latest episode for free. And if you want to follow me on LinkedIn, you can just go to jordancrawford.com it'll direct you right to my LinkedIn page. We do 9 to 10 cannonball and then from 11 to 12 we do office hours. So anyone can come in and join and ask questions and then I'll build stuff live just like we did for your business. So yeah, anyone subscriber can come to those office hours and we drop little nuggets like this.

A (58:54)

Yeah, awesome. I gotta make it out to more of those. Well, thanks so much, Jordan. If you enjoyed this episode, let us know and reach out. If you're on YouTube or Spotify, drop a comment, come over to LinkedIn and let us know how you liked it. If you want to have Jordan back on the show, or if you want us to do more very tactical deep dives like this. But thanks, Jordan, for making it happen and I look forward to talking to you soon.

B (59:16)

Yeah, thanks, Amber. Appreciate you. Bye.