Dan McCarthy
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Dan: [00:00:00] Feel, especially for product that most of the problems you're working on have been solved. think the bigger challenge is identifying it ahead of time. So you're not scrambling because you're a startup.

You're always resource constrained. So . It's identifying it so you can be proactive instead of scrambling and saying, oh crap, like we need to shift everything on our roadmap because we didn't think about how this gas vertical is going to mess with our plan. 

Jeff: Welcome to LaunchPod, the show from LogRocket, where we sit down with top product and digital leaders. Today, we're talking with Dan McCarthy, VP of product at Civil, an AI powered mapping tool for transportation infrastructure. In this episode, Dan talks about using lessons from building digital products to improve the physical world around us.

Dan's repeatable playbook for launching into new industries and the pitfalls to avoid. How a drop in data ingestion costs allowed Civil to open up new digitally enhanced ways of looking at the real world around us and why nailing the entire go to market strategy is just as important as how you build the product itself.

So here's our episode with Dan McCarthy.

Dan, how you doing man? I am really excited to have you on 'cause this is kinda a novel topic for us, but [00:01:00] how are you doing today?

Dan: Happy to be here. Doing well, still easing it back in from maternity leave. And it's been a fun last few weeks,

Jeff: Yeah, that's always a big event. Very excited. Nice. I want to dig into this cause like you've had a pretty cool career so far. You started out in consulting, which I think is not, that.

Ridiculously odd for products, given, what we've seen in the past 50 episodes we've done here. You moved into product, went through session M cards, some companies that do the real loyalty kind of stuff. But then, , your latest move is something I really want to talk about because it's wicked intriguing this idea of digital twin architecture, like how do we use the technology and the analytics and we've built through all the SAS, growth and growth of software and stuff that we've used to understand. How people operate and behavior, but you guys are applying it actually to like the real physical world over it civil. 

First of all, what is this digital twin architecture stuff and what are you guys doing with it?

Dan: yeah, definitely. Briefly civil basically works with towns and other government and municipalities to map their, Real [00:02:00] world infrastructure. And more specifically, we have our built and designed in house civil sensor that uses LIDAR imagery and GPS stitches all together and creates a digital twin, basically like a three model of the real world.

And so what we do with all that useful data is identify a pothole in the road or a crack in the road and then we're able to say this is the conditioning of your roadway and the surrounding infrastructure. So it really helps towns make better decisions on how to manage their infrastructure, how to make it better.

It's a problem across the United States. We're not as good about working with our infrastructure as we should be. And it's a really hard problem. There's just not a lack of data around it. And so that goes like into the broad category of digital twin of taking like real world information, put it in the digital world, and it's as you see it civil, but like I've seen a lot of other use cases around it.

It's just having all that data. You can make better decisions. Also, just faster decisions. That isn't typical for physical world use [00:03:00] cases.

Jeff: . interesting to me because there are a lot of analytics companies that hell we, we operate in the analytics space and we help you understand when people are going through your digital experience. , but 

why did it take so long for us to be able to start to build that level of. Data ingestion about the real world, the thing we interact with, every day when we're off these computers.

Dan: Yeah. It's a great question. It's something I think Daniel, our CEO pointed this out and I would tend to agree with it is it's just the right time where technology is becoming scalable enough where we can do this kind of thing. I mentioned LIDAR really briefly. That's shooting off millions of beams of light a second to create what we call 3D point cloud.

But so if I see a street sign. It creates that in the digital world and those were really expensive up until A few years back, and then also specifically for civil, we use machine learning computer vision to make use of that data, right? Light. Our day is interesting, but alone. It's hard to really read.

And so we're able to learn from it and say, okay, this [00:04:00] means there is something wrong with the pavement and we're able to identify that in a way a trained engineer or somebody who works for a town would understand it. And so that machine learning also, I mean, obviously, is everywhere.

Jeff: It's becoming a lot more scalable and maintainable and just cost effective at this point. 

It's really interesting, I'm gonna make you explain this in a sec, Dan, but I was in Austin recently.

Jeff: We had a, get together of product leaders with AGA Dewe did some questions for us and q and a and stuff. But AI was a big topic . And the running joke has been with all product leads is I don't need to ask Oh, what do you do?

Dan: Or what's your company do? It's just what's your AI plan? Cause that's always what the question boils down to. So a big part of kind of understanding the state and like, where does that fit in and how are you pulling out salient stuff and helping cities prioritize here. 

Yeah, especially working in the product space. I feel like we are asked that a lot of what are you doing with AI? Civil has made it really easy, right? It's core to our product specifically, like really down to the nitty gritty, right? We are training our algorithm to look [00:05:00] at a road.

Dan: And just there's a number of different distresses that make up the condition of the roadway. And so we're looking at a road, hundreds of thousands to millions of times and training our algorithm to say, when you see this means that it's a pothole of medium severity. And so that repetition is it's quarter our product and we're constantly growing it.

say. It's an interesting balance where when we're talking to our municipality clients, like sometimes AI isn't the right way to talk about it, right? It's more just like we have a technology that Brings in all this raw data and makes it something useful for you. There are products in the digital twin space that just give you the raw data and it's still very useful.

You can see a lot from it, but what we do and what we found to be really helpful is have the end to end solution . We are taking the raw data, ingesting it, making use of it, and then creating like a valuable reports from it.

Jeff: . especially with like city governments. I can't imagine they all have giant data analytics teams sitting there [00:06:00] ready to deal with huge raw data sets. Maybe I'm wrong. I don't want to insult any city governments here. But my experience has been, that's probably not true.

So it seems like this is a space where you're not looking to deliver data. You're looking to deliver the full package of the insight. What is the thing that needs to get done? . How do you understand the real world?

Is that kind of how you guys see it? Like it's bringing forth. The insight to help communities move that forward 

Dan: exactly. And so I was a civil engineer in college. So it is near and

Jeff: Oh, this came full circle

Dan: Yeah, it really did. I did not think I was gonna be able to use my civil engineering degree once I got into product. But when I was in college, I wrote a thesis on the state of infrastructure in the U S.

And it's not just a United States problem. Like infrastructure is just really hard. But like United States has struggled with it. And it keeps going back to the people making decisions are really good at their job. And they know what they're doing. And the problem is 

it's a really hard problem and there's just so much of it. And so it's more of how can we help the smart people cover more ground and make the decisions better versus there's [00:07:00] inefficiencies in it. I always liken it to in product development there are bugs all the time, right?

I would say let's make a screen that does X, Y, and Z and saves. And sometimes we forgot to actually make the save button, save the work. That's easy to fix. But when you mess up a road, like pulling it back out and ripping it back up and repaving it, that's a lot of money, it takes a lot of time.

And the more we can do around that, the better.

Jeff: Yeah, it's interesting. I think back to , what we do as a company is we do session replay analytics and ideas give people an understanding of like how almost like civil, but for software 

Dan: I don't know if I mentioned this, but we use log rocket in

Jeff: Oh, there you go. Hell yeah. I didn't know that. That's awesome.

But yeah, so you're familiar basically, what we do here to make a metaphor for the software people the same way, like we give the data of like how people are using your product, where they're going, session replay gives you that, but the same way you could have that raw data set for a civil.

And if you deliver that, there's a lot of people aren't gonna be able to do anything like they have to spend hours and hours digging through files and raw data, but, in both cases, both companies have used AI to surface what does this mean? What's, what is the most impactful thing in.

online. It's, why are people [00:08:00] dropping out of your funnel? How can we surface where people are experiencing real world friction? In, in the real world out in, physical hard life that might be a pothole. It might be, like you said a, place where the road is wearing down.

And how do you get ahead of that stuff before you have people complaining about it? How do you get ahead of it and upkeep all your infrastructure before. Yeah. God forbid you have a bridge collapse or something like what is the ways to get ahead of that. So I love this idea.

So like , where does this go in the future? What is the path forward here? Do we live in a world that we can like autonomously dispatch teams to where it's needed? Or what's the vision?

Dan: Yeah. I was, just working with two of the co founders on it yesterday. So it's a very in my head right now, but , what you were saying, like that is the end turn and vision of really shifting from this and this is not just an infrastructure thing, I'm sure in log rocket, you see this, but data collection is key, but a lot of products are reactive, right?

It's just, here's the data and here's something you could do with it. But we want to start to shift on. We're doing really well with that. But we call it condition assessment. We're telling you what's happening. We're telling [00:09:00] you what is happening in your town right now. And we want to move to that more that proactive range of here's a one stop shop.

We've been messing around different things like the civil operating system for your town and figure helping you see all of the different infrastructure in your town. And giving recommendation helping you figure out what to go fix first or what's what could be a risk in the future. We are very focused on roadways in the surrounding infrastructure right now, but you can apply that to waters and sewers the like airports have a lot of this infrastructure as well.

So it's all about getting the data to the smart people in engineering and municipalities. So they can, okay. Get some opinions and then make the right decision basically.

Jeff: Yeah. I have a couple friends I know who work in different aspects of, local governments and city governments. And I gotta say, none of them are not busy. They are all overwhelmingly busy. And so it's never like they don't want to fix these things, it's just, the time to go through and figure out [00:10:00] what needs to be done and how to best spend, whatever limited resources and cash they've allocated to budgeting for these things.

And so This just seems like the future here 

Dan: and it's still a very unsaturated market. I would say, 50 to 60 percent of like United States towns still use a manual process to get this data. So there's still a lot to be grown into and I would say everybody I talked to in the space is excited by it.

They want to adopt it. It's not like. Resisting. It's just finding the right one that really helps them. And that goes back to our end vision of if you can have one place that you're seeing everything getting all this data and then helping you make the decision so you can basically build faster, right?

We want people to be able to build faster so they can cover more ground.

Jeff: On this show, we've come back to again and again, at the crux of it, we are not here to build software, right? Like software is the tool, but what we're here to do is solve people's problems. And that's, macro, not you and me, we, but yeah. The Royal, all of us, we in the end we're here to solve problems.

And if we're not doing that, you [00:11:00] can build all the software we want and you're just like typing. But how do people take this data and move forward? It's like. Engineers, I feel like have in the past year or two really leveled up. They got, what is it?

Cursor AI, or there's, you know, a co pilot. This is like almost the co pilot for these civil engineering groups in the real world, right? Like, how do you make yourselves five, 10 X more efficient in doing this? You can deploy faster. You can in theory, understand it better and make better judgment calls much faster, or, down the road, who's to say we couldn't have an agentic system that is just.

Kind of running a lot of this. And there's just a little bit here in the loop to make sure we're not going off the rails.

Dan: yeah. I love the tagline. I might have to steal the copilot. Yeah, I think that's really exciting because it is building the physical world and if we can enhance it, it solves, it's a very tangible problem, right? You, everybody notices when a street is subpar and it makes everybody's life a lot easier when that is not happening.

Jeff: As someone who lives in downtown Boston, which is known to. Maybe have a couple of [00:12:00] potholes once in a while I am all for this strategy of how do we take, the focus we have put on creating great digital experiences across the web. How do we do the same thing in real life, right?

That said there's one other thing that came up when we had talked previously, which is interesting looking at like the parallel of, applying.

All of this kind of what we've learned in digital to real life is you still face a lot of the same problems like moving vertical to vertical is never easy. And this came up even here at civil, right? Like you run into different states or different use cases of this data.

So where have you guys experienced kind of growing pains? Maybe you didn't expect. 

Dan: Yeah, I guess it wasn't expected. But when I joined 10 months ago now when we first started, it was direct to governance. And we were trying to show this technology and sell to local towns and help them like just gather all this information. And what we found was their engineering partners.

So the ones who do a lot of the planning, designing, It goes from, collecting the data condition [00:13:00] assessment to, okay, what are we going to build it differently? Or what road are we going to repair? And there's a lot of strategy that goes into it. And so we had a lot of success working with the engineering firms directly.

And so when I joined, it was mostly working directly with engineering firms, whose clients were the towns. And so in my head, I did the mistake that I'm sure I'm going to do again, where I simplified the segmentation of light of. Engineering firms and governments. And I knew that wasn't it, but it was like how we're talking about, but obviously if just talking about it, a global engineering firm operates very differently than a regional engineering firm.

And we've had a lot of success with regional engineering firms for a number of reasons. Part of it and the last success in the Northeast because we're here and that's like where we started. And the same thing goes for, a. A local town in like Boston metro area is very different than Boston itself.

Boston, like we've talked to cities like Boston and they do have data analytics teams. They do have some people have been in product [00:14:00] management and are now trying to apply that technology within the city. They're pretty advanced and then you can even go one step higher.

The state DOTs have been collecting LIDAR data for a decade. And so how we talk to each of them and what we build for them is a different story. And it's a learning experience even figuring out , how we help engineering firms, either if we're selling directly to the government and then they're using the data or they're going and bringing it through is, it was a really hard challenge and something like.

At Card and Session M, I also ran across multiple times where I probably oversimplified the segmentation the first time around, and then, after three months was like, yep, I missed this and I have to reassess what I'm building for these specific players.

Jeff: It's always so interesting when you think about verticalization because vertical software has been really popular lately, right? You have a lot of, applications for finance teams, maybe I guess it's horizontal, but a vertical use case, but construction software, but what you don't think about is something like this where you're selling the same [00:15:00] solution, but to go vertical to vertical of, large engineering companies, regional city, state governments.

And how do they need to be spoken to? How do they need to ingest what you're giving them? There's a lot of go to market variance there. Can we maybe dig into like where this is really shown through, like example of where you guys ran into something maybe.

You knew was coming but maybe presented a problem.

Dan: Yeah, I think a great example of this is talking to some of the global engineering firms. They have their own in some national too. They have their own in house what they would call like data collection or infrastructure collection condition assessment branch. And they have for the past 10 to 20 years, use some kind of technology to collect that information.

And so civil is doing slightly different ways of collecting that data. And so when you first started talking to those firms, it's figuring out. Even just who in their whole practice who to talk to, because with the regional firms, you found pretty quickly the key stakeholders, the decision makers, and [00:16:00] they got the solution and it was a lot quicker of like, yeah, we want to move on this in the global ones.

You need to find that like the central person. That we hadn't really always found of like, I own data collection for all of this global firm. And once I give the stamp of, yes, this is really helpful. Then all the project managers for across the state and across the nation and, abroad will then want to use it for projects when they need it.

And so even that, as simple as that, it wasn't even the building the product differently, which there's a bunch of things we could do. It's even just, how do we find that person to go solve the problem. And then I think it's a really good example of especially the larger firms, they really do want an end to end solution.

Right away off the bat. And so as we build our product, how do we help them, but also help our core companies. And also going back to, the government's own the infrastructure. It's always going back to how do we make this better for the government so that they can have better infrastructure, [00:17:00] happier citizens?

It goes, you can keep going. 

Jeff: I remember a little while back we had a guest on, we were talking about go to market matching the product. And one thing you have to do is look at what are the incentive structures and what's actually being delivered and like how does payment work and this case, right? It's not just, yeah, you have to sell the engineering firms, but at the same time they have to see a way that this is going to make them be able to either serve their end customers, which is the cities to governments.

at a better rate where they can pull in, either better margins and more efficiency, or they're going to be able to get bigger contracts as a result. Like they're not going to do this just out of the goodness of their heart. It has to have an end result where it's going to benefit those governments in a way that makes, contracts better for them.

Is that something you ran into kind of like having to address the needs of the buyer's buyer, even though you're not necessarily directly selling there. Yeah.

Dan: especially for civil, we were, lucky in that. We have direct relationships with some governments from like when we were doing that only. And so we have a pretty good idea of that, but it is, that is also just arming [00:18:00] the engineering firms and tweaking the products slightly to help them like make it.

So that when they're working with the governments, they can make the decisions faster. Cause at the end of the day, regardless of who we're selling to, the engineering firms are the ones who are helping with a lot of these decisions because they're the trained professionals. And I think something that always resonates with our clients is that we're not the ones making the engineering decisions, right?

We're not training that we are really good at the technology. So we want to build you a whole technology stack that will make your decisions faster. And we have a pretty good base knowledge . Of how everything works, but you're the professional at the end of the day. So it's, how do we get everything at your fingertips?

And that resonates with both sides, but especially the engineering firm, because either, even if we're selling directly or indirectly, they are going to be the ones taking this data and telling and giving advice to the city of what to do.

Jeff: I love how just across so many different, worlds and verticals, like the same key themes come up, whether it be we're selling, a log record, selling to product people and we're selling to engineering teams and you guys are selling to a very different type of engineering team and seeing state [00:19:00] governments, but it's still in the end, it's like people want just the insight they want the, what is the thing I'm going to act on?

How do I service that? Yeah. As fast and high fidelity as possible, ideally before I have to ask the question. So it's really, I know I'm nerding out a little bit on this, but it's really cool to see the parallels and things that seem like they would not parallel at all between civil and log rocket that I'm just going Hey, this is exactly the conversation I had last night or.

I was in Chicago last night for dinner and talking with a bunch of product leaders. And this came up with five different people about this is their problem. They just want something to show them here's the evidence of these major problems and that's what you need to look at. So that's so cool.

I do want to switch gears real quick because you didn't just come out of your civil engineering degree into civil. You have a whole background, I think, primed you to be really ready to take advantage of this cool, like move in the world towards real life of vacation of how we apply digital analytics.

So you came out of school as we already talked about, started as a consultant, and moved into I guess is the loyalty [00:20:00] space. Is that the right kind of

Dan: yeah. Loyalty, reward, space, a ad, advertising tech, some, somewhere in that range. So I was doing consulting for two different companies for probably six years. I knew it wasn't gonna be like long term, but it was it great learning experience. I would say , I always highlight one thing that was huge is that.

Consulting really gives you a good degree in client management because I think a lot of people like client management to them is saying yes to the client, keeping them happy. I think consulting did teach me to like strategically push back and say no when you had to and that was super helpful.

And then, yeah I joined Session M. Which was right in the seaport when I joined, I was probably 150 people. Looking back, it was pretty perfect first product for a product management or even just like SAS operator role.

It was the right size where it was big enough that it wasn't the chaos of a 20 person company, but it was still chaotic. So I got like this startup feel.

So you hit session M. That is not a [00:21:00] small place. You were dealing with I don't know if companies like McDonald's or that scale. , so I guess you went from there. You went to card. Was there a reason for the move within the space or just different perspective? 

Session m is basically api that powers loyalty programs so if you think of the mcdonald's app the logic to say if you Purchase burger, it's five points. That's really complicated. And so that, that was basically what session of did that, that shifts a card worked out really well in that I really wanted to.

Dan: I enjoyed the growth throughout session M and then it was acquired by MasterCard. And MasterCard was really cool experience. It just was a really big company. I was ready to go small. And. I wanted to be the first product hire if I could be in card was a really good transition in that it was also an API in the advertising tech loyalty space.

So being the first product tire, I had never done that, but I had this base background of building API products and also base understanding of the industry I was going in. I wouldn't [00:22:00] say it was totally calculated that way. My number one goal is be the first product hire somewhere, but it really worked out in that.

I think they. They were like, yeah, we're taking a chance on first product hired less experience, but he has this stuff and it makes sense.

Jeff: I feel like one interesting thing, we started to touch on this a little bit and like one to one to one vertical um, movement and maybe it's one thing in civil, but, we had non you, if you know him on a little while back he's the head of product over at a company called linear now, 

He talked about when he was at Everlane, the big growth factor there was adding categories. Adding different, dress pants or women's, long sleeve shirts or something.

And basically the launch of that kind of category always give them an inflection point up in growth. Is it a wrong kind of parallel to think that maybe that is similar to a card in session M where it was maybe not even launch it, but moving in different vertical spaces was really opened up your TAM and was a big lever for growth.

But I can't imagine that would have been easy. How did you guys approach that? And like, how did that situation where I have to think there was some rough patches going into like, let's move from, a big fast [00:23:00] food vertical into whatever came next.

Dan: It's a great question. And it is something that I thought when you were asking me just like thinking about learning experiences that one does come up a lot. For background at Session M our bread and butter was like quick QSR. So a Chipotle, et cetera, and retail. So like somebody like an urban outfitters and we had a really good opportunity to expand into the travel and hospitality loyalty space, which is very a huge market.

Think about the Marriott app Delta's loyalty app, I think is worth more than Delta's or some stat like that.

Jeff: Yeah. I flew Delta this morning, actually, like literally a couple hours ago and I've been changing my behavior to get, upleveled status with them. It definitely drives my own behavior.

Dan: , I'm a definitely a Delta user from the consulting days and better for worse. And so a huge space and obviously the TAM really expanded. And , it made a lot of sense and it was really interesting for me because I was still, an individual contributor. So my boss was doing a lot of the.

sizing and [00:24:00] figuring out like, does this make sense? And then the team was really good at finding clients that were interested in it. We had a couple of hotels as well as some airlines. So it was really going into it and big market. And then there's always the assumption that we do this.

So the products going to be able to do the functionality that we're talking about. And it, it really wasn't a case. I think the biggest example of the change is that everything traveled to hospitality, , when you make the transaction, you don't actually do anything.

You just make a reservation. You don't do anything for months until you check into the hotel or take the flight. And that's very different than going to Chipotle and ordering a burrito. You're not doing a return. You're not waiting to get the points until you fly. Like all that logic is really different.

And so that was my first foray into it where I was tasked with the execution of a lot of this, which is really cool product to build, but it had its challenges. There's still huge market opportunities. So we're gonna do it. But I remember naively thinking we're pretty close.

So I'm sure we'll be able to [00:25:00] take this over. And then, you think I'd learn. And that would be that. And I never forget. And then I went to card and about six months in, I was seeing the same thing. And just for some summary card is similar session. It's basically a marketplace where they work with large brands like McDonald's and who have advertising budget.

They put that budget into our ecosystem. And then, okay. It is used to create rewards experiences within banks which we are connected with via API. And so if you think of a rewards program, when you log into your banking app and say, Oh, I get , 5 percent cash back for going to Starbucks that, that was the basis of card.

And we did a really good job on when I joined by design, we're really segmented. On the bank side and that we Neo banks for the way to go. So think of digital only banks. Chimes the biggest one. We're really good at that. The other side, though, it was more just get the top 300 brands by spend in the United States.

And once again, in my head, I'm like [00:26:00] it's cash back. It's simpler than what I was just at. And three or four months in, it was very clear that. Oh retail is very different than travel and hospitality. And I had to relearn that whole thing again, which was fun.

And I learned it quicker and was able to action off it. And we were a smaller company. I guess it all always comes full circle in that way.

Jeff: I feel like there has to be some element of just base level human, like we're pattern matchers at heart. And so we go Oh yeah, this is just like this. I can do that. And then it just, probably works really well when you are, outside trying to navigate woods and hills and animals you're like, Oh, a giant animal like that was dangerous before.

This one's probably dangerous as well. Let's not go near it. Works a little bit less when you're like, Oh yeah, travel. Same thing as quick service restaurants. Let's just, same thing. But I guess they're like, we've had people talk about on the podcast before this idea of, steal ideas not, in a negative way, but when I was a dentist, I had a leader who used to say , you don't have to reinvent the wheel, just go look at where it's been solved before and you can use that. And so here, like [00:27:00] what you're describing, the first thing that went off my head when it was the transition from, QSRs or, quick service restaurants over to airlines.

Cause I I literally saw this last night. I'm changing airlines I use. And so I'm trying to like literally plan how I'm flying and where, and when those things are going to hit, because it is. Time delayed everything, but it strikes me.

It's the same thing as talking to accounting teams when we do budget planning and when we do. Budget sync ups at, regularly is it's the same idea of revenue recognition and expense recognition, right? The event happens or that the spend happens, but you don't get the recognition of, the expense of the revenue until you deliver the goods or services or whatever it is.

It seems like that's an area that could have been mined for, how do you solve this problem? , is that kind of ever something that people look at? 

Dan: Yeah, feel, especially for product that most of the problems you're working on have been solved. I think the bigger challenge is identifying it ahead of time. So you're not scrambling because like you're a startup.

You're always resource constrained. So . It's identifying it so you can be proactive instead of scrambling and saying, oh crap, like we need to shift everything on our [00:28:00] roadmap because we didn't think about how this gas vertical is going to mess with our plan. And that was like, honestly, probably the biggest learning experience so far in product was linking your product strategy to the go to market strategy, which I, it sounds so simple, but when you present your roadmap.

People think different things. It's not always exactly the same. So having the go to market team, say this is what we want to achieve by the end of the year. We're going to hit these revenue goals and we want to do it with these clients. Like tying those two together has been so helpful for me.

Cause then , that's where you go into product apps and say, Oh, like you want to expand into gas. And I keep saying gas because it's, it was a real example from card, which that was a very good opportunity. But it was slightly different than what we had and what we were building.

So like the scramble of doing that in a month versus if we had talked about it in January and then did it, that would have been like the big win.

Jeff: Yeah. It's coming back to solving problems, not just creating software. You have to look at the whole world. It's not [00:29:00] just creating the great solution, but it's, like you said, the sales team has to be ready to sell it, which means that you have to plan, like how you're going to train them on if it's a new vertical you have to look at time delays and, recognition versus accrual of benefit.

And then even like, , do different industries do budget planning differently or different timing or have different rollout times? It's one of those problems we could probably spend, two or three episodes just on this. I want to make sure we wrap up on this. And one thing I want to ask you is now that you've had. Multiple times you've gone through this and you've run into the vertical problem and it hits you, do you have a process you use or what's a way you could maybe get ahead and like, how could you assess we have different verticals, what do we need to do, or can we put some structure around how you think through these moves now?

Yeah,

Dan: back to, okay, start with the vision and break it down to what is the product strategy, especially for the upcoming year. And then link that to the go to market strategy. Like that linking is the first thing I always push on.

And then the other thing that I've really pushed on more and more is just, once again people will have different ways of tracking their [00:30:00] pipeline and everything, but really like diving into the pipeline as the product person and stress testing that we actually have market fit in this specific vertical.

Because like in my head, I can split out civils pipeline to six or seven verticals, depending on how you count it. And then like really stress testing, which ones we have market fit in and which ones we don't, and then working with your sales counterpart to make that happen.

And I think the broader thing that I've become better at, and it's always a work in progress is. Just the communication for me personally I've found that like I stole from Amazon, the six pager idea of the six pager memo. And so as soon as you write stuff out, you start thinking about it in more detail and like calling out your assumptions and saying why is that assumption?

Do I actually have a number to back that up or am I just assuming something? And then it also makes the people reading it think about that and go that level deeper and , that has been like. That's a more broad one, but that has been a very helpful thing to me and something that I did bring out the civil it's different now we're smaller.

So I'd have to [00:31:00] be a little more nimble of okay, what is this memo I'm writing? But like that has really changed how I solve those. I don't know, squishy problems. Like it's not very obvious what the solution is when you're trying to figure out like your gaps in the market.

Jeff: I mean, It seems like from a product thinking standpoint, looking at the vertical stuff and kind of what we talked about, you can run into problems where the new vertical recognizes value or accrues value on a different timeline or a different way than previous ones.

It's a little bit different if you're selling to the customer is actually going to use it. If you're selling to, a McDonald's or a Burger King or a quick service restaurant, then if you're selling to. A customer who then has a, another customer that needs to understand the uses probably, it's civil, you're selling to engineering teams, engineering companies who are also then going to need to use for a, B2B sale itself.

What are buying patterns? What are seasonal, differences in some of these verticals, maybe less important. For QSRs if you're trying to sell something to retail and it is going to touch the end user experience, they're probably not going to ship it in October.

Never buy anything new in October, November. You want to [00:32:00] sell to sports teams you gotta look, you know, when's their season start and how do you get further behind that? So all these kind of interesting things. Points to look at to put a framework around of basically how do you build a sustainable way of going?

Like we're moving to new vertical. Here's what we're going to do to make sure we don't make the same mistakes we made 17 times before that everyone always makes. 

I know how busy you can be just in general, a software startup ad in some point you probably like to go home and see that kid.

I'm not going to keep you here all day. Uh, But it's been a blast having you on Dan. Thank you so much for coming. Really appreciate it.

Dan: Yeah. I really appreciate the time. Jeff.

Jeff: If if people want to dig in and find out more about the incredibly interesting and cool world of digital twin architecture, or just want to ask you questions about how you did stuff or maybe how they can look at verticals a little bit better.

What's a good place to find your LinkedIn? 

Dan: I'm awful at social media. LinkedIn is the one good one. So yeah that's going to be the first there's only like 500 Dan McCarthy's in the Boston area.

Jeff: There's no one with the name Dan McCarthy

in the Boston area. What do you talk about? I think I know for like, I could probably name them off who I like know. 

Dan: Yeah. They're running, there's a lot of them running around in the Boston area. But yeah, I really appreciate it.

This [00:33:00] has been awesome.

Jeff: Awesome. Thanks, man. Thanks for coming on. Have a good rest of the day.

Dan: Awesome. Talk soon.