Bill Cilluffo sits down with Sean Harper, CEO and co-founder of Kin Insurance to discuss how he's shaking up the homeowners insurance industry. Sean shares how Kin's cutting-edge direct-to-consumer model and data-driven techniques are setting new standards for pricing accuracy and customer satisfaction in the face of an industry traditionally dominated by agents.
Bill Cilluffo sits down with Sean Harper, CEO and co-founder of Kin Insurance to discuss how he's shaking up the homeowners insurance industry. Sean shares how Kin's cutting-edge direct-to-consumer model and data-driven techniques are setting new standards for pricing accuracy and customer satisfaction in the face of an industry traditionally dominated by agents.
You're listening to the FinTech Thought Leaders podcast from QED Investors. You're deep dive into the world of venture capital and financial services with today's digital disruptors. Qed is a global venture capital firm focused on investing in FinTech companies all the way from pre-seed to IPO. Fintech Thought Leaders brings together the most talented entrepreneurs tackling today's biggest problems. If you're looking to learn more about what motivates our founders and team members to succeed, you're in the right place. Hello and welcome to the FinTech Thought Leaders podcast. I'm Bill Salufo, head of early stage investments at QED Investors. Today on the podcast, I'm excited to be joined by Sean Harper, ceo and co-founder of Kin Insurance. Sean, welcome to the podcast.
Speaker 2:Hey, good morning Bill, Thank you.
Speaker 1:Hey, just as a way to kick things off, so the listeners have some context, I wonder if you can give us maybe a 60 second pitch on what Kin Insurance is and what you guys do.
Speaker 2:Yeah, of course. So Kin is a high tech provider of homeowners insurance. Homeowners insurance is a really big market and when I started Kin I was really excited because it was $100 billion market. It's only been seven years. It's $150 billion market now. Wow, really unusual. You have a legacy market that has a kegger that high. So the two things that are driving it are increased investment in housing and increased weather volatility. I think both of those are pretty enduring trends. So that's the market we're in, and that's a market that's, by and large, occupied by sort of an oligopoly of like 40-ish insurance companies.
Speaker 2:There's a long tail, but mainly it's like 40. So it's not a very competitive market and there hasn't been a lot of innovation. So the innovation that we're bringing is sort of three parts. The first is a business model innovation, if you could call it that. I think it's pretty simple. We go direct to the customer.
Speaker 2:So 95% of homeowners insurance is sold through these local agents. There are actually more than 400,000 retail insurance agencies in the US. That's four times, it's more than four times the number of bank branches and it's more than two times the number of fast food restaurants. Believe it or not. That's frightening, isn't that nuts? And people actually go to their bank. Nobody goes to their insurance agent ever really. Like you can camp outside the office and the only person going there is, like, the owner of the agency.
Speaker 2:So 95% of it is sold through these agents and that creates some issues. The first is they're very expensive. That branch network costs about 20% of the premiums to maintain for the insurance carriers, so it's a huge extra cost. The second is customers actually don't really like it. Like if you survey customers say, hey, would you rather buy from an agent or would you rather buy directly? It's like 70% would prefer to buy directly. And if you say, are you willing to pay extra to have the local agent, it's like almost none of the customers are.
Speaker 2:And then, finally, by going direct to the consumer, we can actually use marketing as a method of risk selection and we'll get more into this later. But there's a huge variance in the types of houses and how. In homeowners insurance, most of the events, most of the bad things that cause a loss, it's weather related. Okay, all the houses are going to get hit by the same weather, but they're all going to respond differently because the homes are built in these idiosyncratic ways, like your roof is like different than my roof and my bathrooms are different than your bathrooms and all of that. So we can actually pick and choose the customers that we know are living in homes that are more resilient to the weather and we can specifically market to those customers. So that's the business model innovation we're direct to consumer. It helps us with risk.
Speaker 1:Whereas the folks that are distributing through agents can't do that because they take whoever the agent goes and finds and the agent's probably not incented to do that right.
Speaker 2:That's right. And they have very crude ways of doing it right. They'll tell the agent like hey, your portfolio looks like X, like we'd like less of this and let more of this. But it's not very nuanced the way they can do it. They don't actually have control, they just sort of have like influence, right. And then the second thing we do differently is tech innovation, which plays very well with the business model innovation.
Speaker 2:The technological innovation is two parts. First, we've built from scratch the best, most cutting edge, efficient insurance core processing system. It's proprietary to us. Most of our competitors don't control their own core processing systems. They're outsourcing it to a company like guidewirecreek which for the most part, are not very good software. It suffers 30 or 40 years old. For the most part it's still on-premises software, like it's really, really not good. So that gives us a big advantage. And the second part of the tech innovation is All the insurance companies, us included, we all these like smart actuaries pricing it stuff like that.
Speaker 2:Right, they're crunching the numbers. They're saying, hey, for homes that look like this, we expect the losses to be like that and that's how we're gonna come up with our pricing. The issue is the way they get all the input data. For that our legacy guys is Asian. Well, that's silly, that doesn't make any sense, like the agent has never been to the house, right? The agent also isn't an architect, he doesn't know about the shingles on your roof, he's an insurance agent. And then, third, they actually aren't compensated for accuracy of data. And so you'll find is, the agents are constantly fudging the data because that's their job. They're like trying to help the customer by getting them a lower price.
Speaker 2:Now, there are lots of bad things that can happen when you flush the data, and one of them is inaccurate pricing. It's important to note that that's a problem at the point like for that risk, right? It's like, oh, if you price this as If the roof was really good and the roof is actually really bad, you're gonna be underpriced for that risk. It's also an even bigger problem because it fundamentally breaks their actual, real models, their pricing models, because, if you think about it, they're feeding all of these models and training them on bogus and potato Sure, so you actually don't see the correlations that it actually exists, and so it sounds like, then you must have a process to collect all this data directly from the source we do so we're Manufacturing our own data and what we're doing is where there's like like all you have to do is Google your address and you can see there's just like tons of unstructured data out there about your home search images, there's MLS records, there's government, there's tax records, building permits, there's all this stuff.
Speaker 2:No, it's all unstructured and that's the problem with it. So what we've been doing for the last seven years is training a set of models To take that unstructured data and package it into structured data and then training our pricing and underwriting models on that input data. So it's not always perfect, right, you don't always have perfect information, but it's way, way, way better than the alternative, which is just like asking some guy who actually has a bunch of reasons where he'll be inaccurate right so that.
Speaker 1:So that allows you to basically use this under undercut price on the best customers and then lose the worst customers because they're you know, they're not as good as the other the industry thinks they are that's right.
Speaker 2:It's more accurate, it's more accurate pricing and that ends up being a bigger deal in some places than others. Basically, what you find is in areas where there's more weather volatility, having accurate pricing makes a much bigger difference. So where I live in northern Illinois, we don't have that much weather volatility, and if you know exactly the type of shingles that's on my roof, it's helpful you know, it doesn't hurt. Yeah, but if I lived in Charleston, south Carolina or Tampa Bay?
Speaker 1:you probably only care whether somebody has a flat roof or a slope roof, so the three feet of snow doesn't doesn't cause a leak.
Speaker 2:That's right, and the roof sheath is an interesting one because there's more nuances to that, like, no roof is truly flat and the roof's all of different angles on the slopes. You actually want to take that into consideration. So there's a lot of nuance in reshape since you bring it up, but it really can make a big difference in some of these places. And and this is where you see the headlines one of the reasons why you see the headlines of Insurance company X is leaving wide geography entirely. Okay. Well, that's because they actually Don't know how to tell the difference between the homes that are going to be profitable for them in the homes that aren't. And we're able To do that, and it allows us to serve these customers who need us the most, the folks who live in higher-weather volatility areas, which, by the way, is more and more of the country every year, unfortunately. So that's, that's what we do.
Speaker 1:Yeah, that's, that's fantastic. Well, look before we, before we go into the next several layers of can, I'd love to spend just a minute or two exploring your background. I mean, I know you got into the starter startup world quite early after you started your professional career. Wonder if you can just talk through kind of how you got into the startup world and what was it about startups that really attracted you.
Speaker 2:So I was a tech guy first. So like I was one of those kids that grew up Programming, you know, and I was just really into making like these stupid little apps and games and Stuff like that. You know, it's like going back to middle school now and Then I was like, well, I'd actually really like people to use the software you know. So that sort of got me more into the business part of it, right? And then I realized I was actually really interested in business. I was really interested in economics. I ended up going to school, you know I'm majoring in computer science and economics, which is a cool combination, and I've really been doing FinTech stuff ever since.
Speaker 2:So my first gig out of college was like, like when I was in college I did the same like investment, banking internships that everyone else does. But my first gig out of college Was working at PCG. I was mostly working with financial services companies like mostly retail banking, credit cards, pnc, insurance. I was like really interesting. But I looked at myself after two years and I was like, well, gosh, sean, I always thought you were gonna be like a tech guy. Like what are you doing making spreadsheets for banks all day?
Speaker 1:This is stupid, that's not what you're doing, so I quit. Hey, that is high-tech at some banks.
Speaker 2:Come on, some of my spreadsheets were we're legendary and a lot of acros in these spreadsheets. That's why I quit and I joined a VC firm and I did that for a couple of years, like really early stage, did two investments there and I was like, oh okay, this kind of feels better, I think this is good. This is more like what I was aiming to do when I was working at VC I. One of the things I learned is like, actually, at least in that case, I had a lot more spare time after being, you know, working as a VC analyst versus Four days a week and travel at a management consulting firm. And so I actually started my first company in parallel with that gig, and it was an online retailer's e-commerce company. There was like a specific product that we were interested in selling. My co-founder and I in that business it sounds silly now we were the largest Seller of replacement parts for satellite radio systems, which at the time, was like this huge consumer product.
Speaker 1:I, I still have one, and in all my cars. Yeah, serious yeah it's.
Speaker 2:It's actually a really good product, you know, and at the time I had done a consulting case for a retailer and I realized how much money was in these sort of spare parts, because people are relatively price insensitive. If you're like, oh, I have this thing and part of it stopped working and now I can't use this, I can't listen to Howard on my Way to to work anymore Because the adapter, the power adapter for my serious satellite radio stopped working, you, you didn't care if you were paying 20 bucks or 15 bucks to get the replacement part. And so you know that was the business that we built. It was really fun. It was a bootstrap business. We were on the Inc 500. It was like cool, and so that was the beginning.
Speaker 2:And then I did another business. It was a payment processing company, similar. I had had some consulting experiences in that space. I had consumed the product, the payment processing product, at my prior company, right, the one I had started, though I see an opportunity here to do it better. The product that we launched was like basically Stripe, but without a $20 million seed round.
Speaker 1:Our seed round was only like $2 million.
Speaker 2:Details, details. So when we were out raising our series A, we got an acquisition offer and we took it and I spent I spent three years at the Acquire, which was Groupon. I built the Square competitor within Groupon. I thought that was really an interesting experience. But ultimately my future was going to do something entrepreneurial again. Right, Take a bigger swing. I was a single. I wanted to go and swing again and then that was sort of when I started incubating Kin and thinking about what ultimately became Kin and I sort of took a year to explore ideas and do some little independent consulting gigs and do some angel investing and stuff like that, but ultimately kick the tires on a bunch of things and this was the opportunity that excited me the most.
Speaker 1:So it seems pretty logical how you could go from e-commerce to a Stripe-like type thing. I mean, I get that you spent a year looking at different ideas, but homeowners insurance is pretty out of left field given what you had done so far. What even got you to start thinking of that idea?
Speaker 2:Well, interestingly, actually, I had a consulting case at one of the big insurance companies where I spent a lot of time in insurance agents' offices. It was like a go-to-market case for this insurance company and I just saw how wasteful that agent experience was and I saw how weird it was that the actual insurance company would be like one of the questions they asked us was like well, why do customers come to us? Like oh, that's kind of a weird question for you to be asking me, 24-year-old management consultant, like don't you know? They're like no, no, actually we don't. Because the customers go to the agents Just while these guys weren't really doing field underwriting, as they would say, like they didn't really know very much about the risks, they were just kind of going to Zillow and copying some stuff off of Zillow and that was actually the data that was getting fed into these underwriting algorithms.
Speaker 2:And then you also noticed that those agents were really high-margin business. They were actually a much higher-margin business than the carriers themselves. Like the carrier was working for they were like happy if they had a 5% net margin year, like that was a good year for them. And then if you crunch the numbers on these agents, which are actually sort of all small, below-scale businesses. You're like whoa, these are like 40% EBITDA margin. Businesses Like this is actually, if you draw the whole value chain out, the agents are extracting the majority of the value. Why is?
Speaker 1:that. So a bit of an aside from Ken, but do you know why PE hasn't tried to roll up all these agencies? Oh they have. Oh, I got you.
Speaker 2:There's a huge amount of roll-up activity in PE and insurance agencies, especially on the commercial side. There are two private companies there's Accra, shure and Hub. These are both like $20 billion market cap private companies. Pe roll-ups Got you.
Speaker 1:But because there's so many, most of them are still these little mom and pop. Right, you got it.
Speaker 2:That's exactly right, especially on the personal line side, where I think there's been less consolidation activity. There's still quite a lot going on in personal lines. It's just so big right there's so many. There's like 400,000 of them. This is a lot. So I had some context for the problem. We actually drew the map of all of financial services. I was like I'm looking for something that meets a few criteria. I want something where it's overly intermediated. Now, some things are really complicated and you need a lot of intermediaries. A lot of products, especially consumer financial products, are not that complicated. You don't need the intermediary there. It actually makes it worse.
Speaker 1:Well, if anyone can relate to the problem, I guess it's the QED, with our Capital One heritage, which that was a big part of the premise, too right, why would you get a credit card in a branch? Why?
Speaker 2:would you get a credit card in a branch? Absolutely yeah, very much so. Capital One was a big inspiration to us. It was a company that we was on our list of. We could be like the Capital One for homeowners insurance. That would be really cool. Then we were also looking for something where there were new data sources that could be used for pricing and underwriting. We looked at everything. There was one other idea that rose to the top. It was also an insurance. It was small business insurance. Ultimately, we decided that this was better because it has a larger, more homogenous market. Small business insurance has many of the same problems that homeowners insurance has. The reason why we didn't want to go after that is because small business is really a bunch of different niches. You actually do need a different product, a different go-to-market to get the yoga studio versus the coffee shop versus the car dealer. We just loved the fact that homeowners insurance was so big. It was so homogenous. It met our criteria, specifically around new data sources being available, around it being overly intermediated.
Speaker 1:That makes a lot of sense. When you decided to start the satellite radio parts company, did you think about this insurance idea then? Or more just, you had done the case. You weren't really connecting the dots until you went back and systematically looked at it later.
Speaker 2:I always have this entrepreneurial. I'm always thinking about things I can do I'd actually thought about when I was at BCG doing some of these cases. I thought about doing a roll-up in the space. Then, after doing my payments company, I realized how important product-led growth could be in some of these things, even in, yeah, you think payments is a commodity it kind of is. But also there really was a real appetite from the customer to have a more streamlined experience, to not have to answer all the dumb questions, to not have a seven-day underwriting period. There's all these sort of nuts and bolts things about the product. That was really inconvenient. That led me to think of more of a product angle for insurance. I was like, yes, I do want to occupy the part of the value chain that's so inefficient, that's so distributed, with 400,000 people doing it, where they have such high margin that they don't really deserve maybe for the work that they're doing. But also I want to do that by having a really unique value proposition to the customer.
Speaker 1:You obviously had a big industry. You were looking for a startup. You had seen the problem firsthand. Were there any major obstacles? That you saw as you started to think about this and like, oh, maybe I shouldn't go do this, that you had to get over to take the leap.
Speaker 2:We knew the two biggest question marks were do customers want this? Can we manufacture it efficiently? We had to answer the first question, which was what do customers want this? What we did is we actually bought a little insurance agency and we wrapped it in a little bit of UX. It wasn't that expensive, it was a few hundred thousand dollars. We wrapped it in a little bit of UX and we started to run marketing experiments and really go, okay, like, after doing this for six months, the unit economics well, they're not great. But like, if I increase conversion rate here and I increase conversion rate here and I reduce the churn there, then they will be great. Right, they'll go from being sort of okay to being great. It's like okay, cool. So we did that.
Speaker 2:Then the next thing was okay, well, how do I get control of the whole stack? Right, because as an agent, we didn't have any differentiation, differentiated value proposition. That was one reason why your conversion rate was low. Okay, so how do I control the whole stacks? Like a manufacturer, a product that is differentiated? Now I come from the payments world and I've done some angel investing and lending and I was like, oh, we got a fight. We have to find a rents a charter for insurance, like and then we realized, oh, that doesn't actually exist. Like there's this, there's this other thing is sort of sort of similar to a rented charter called the front-end carrier. You sort of rent out their insurance licenses. It's not as well established an ecosystem as the rent a charter, but that that was sort of our next step.
Speaker 2:We're likely we know we're not. It's a very difficult industry to approach. It's no accident that it's basically an oligopoly, right Like there is designed to keep people out. So we know we're gonna need to climb up the stack. You know, one wrong at a time. So okay, next step is let's set up something where we're an MGA which is sort of like a virtual insurance company gave us all the control over the product that we wanted. You know there were some downsides to being an MGA. It actually didn't give us all the control over the product we wanted. It created a real existential risk for the business Because you had this one counterparty that was like so important to you. So we knew there were some downsides, but we were like, well, we don't have the money to start insurance companies.
Speaker 1:So are you an insurance company now? Are you still kind of in the process of working up that regulatory stack?
Speaker 2:We are. I'll tell. I'll talk to you about that in a second. So that was the next step. So, basically, the first year we were running these marketing experiments. The second year we were like, okay, cool, now we have this business relationship as an MGA where we can run everything through our software. Okay, but we have to hustle to like write the core processing system, which is not an easy task to write a good core processing system for insurance. Now We've rewritten that thing like five times since, right, because it was very much like hey, here's the MVP. Like we need to be able to issue a policy, like I need to be able to take the custom rain, get the underwriting info manufacturer the data, print a PDF, basically Obviously. Now, six years later, the system does a lot of stuff in addition to that and does it really really well. But that was sort of the next step. Then, two years later, we're like, okay, well, we're like kind of on the verge of being a scale. This is 2019. We're kind of on the verge of being a skilled business. We have unit economics that are working.
Speaker 2:We're really worried about this relationship with this, the sort of single threaded relationship that creates an existential risk for the business. The MGA relationship isn't giving us all the degrees of freedom that we want. Let's go for the big time. Let's start our own insurance company, and we started. We now manage two insurance companies. The first one we started was in 2019, so it took a year to get regulatory approval for that and I had to go raise a bunch more money because we need to capitalize this thing. Now you use reinsurance and stuff To reduce the amount of capital that you need, but we still needed 30 million bucks sitting in a bank account, right. So at the time we'd only raised a Series A right, so we only had 12 million bucks. Then we went spent a lot of that on developers and stuff.
Speaker 2:So we had to raise another round. We had to wait a year for regulatory approval for this. In the meantime, our relationship with the sort of MGA sponsor got worse because they were like wait, you're leaving, like what the heck this? Why should we care about you? Now that you're leaving, it's like well, right, we gave you warrants like you should care about it. It got a little bit tense there, but we eventually did get in. It's. It's interesting.
Speaker 2:We actually have a novel financial structure when kin actually isn't an insurance company. What we did is we set up these things called reciprocal exchanges. Those are insurance companies. They're fully licensed, they have capital, they have credit ratings, they they're regulated just like any other insurance company, but they're actually owned by our policyholders Hmm, okay, and we manage them in exchange for a fee. So our business actually looks sort of like an MGA business where we're just getting this fee, this recurring fee, to manage it.
Speaker 2:Or it looks kind of like a, like an asset manager almost right, like you could think about this like we're the GP and Our customers are the LP in these insurance exchanges. No, it's a good structure for us Because it gives us a stable recurring revenue stream. Our profit center is what our legacy competitors cost centers are right, the agents and the outside software right, so we're basically keeping that margin. They think about what our legacy competitors. Their profit center is Underwriting income and investment income, and and we're actually leaving that in these customer owned exchanges, right, so the customers are literally getting a better deal and I think it's all ultimately makes it very difficult for our competitors to compete with us, right, because, like what's historically been their profit center, I'm just giving back to the customer.
Speaker 1:Yeah, it's funny, my wife's whole family is ex-military and so I know she's always tickled every year when we get our USA rebate check. Yes, it's similar, similar kind of concept, right, very similar so.
Speaker 2:USA is the second largest reciprocal exchange in the US. The largest is actually farmers. There's another one called Erie. That's pretty big. They're sort of in the Mid Atlantic. The management company of Erie is publicly traded, so it's sort of an interesting company for us to go and look at. But yeah, it's very similar to what you just described with USA. We don't give the dividend check, we just leave it in there and give the customer a lower price. But you can do either.
Speaker 1:So let me, let me dive into this. I know you know, back when you were giving the the intro to kin, you talked about you know, look, you're innovating across a number of vectors. I know innovation and insurance has been notoriously slow and I'm sure there's tons of factors behind that, one of which being Correct me from wrong state regulators have to approve all of your pricing schemes and you've got to show you know a bunch of data behind it. I'm guessing state insurance regulators aren't really at the cutting edge of large language models and all of the tools that you use to convert your unstructured data to structured data, etc. How have you thought about that and how have you overcome that obstacle? And and how big of an obstacle is it from? You know, say, a perfect world where you could kind of go do whatever testing you wanted to do and do whatever you wanted. We've found it to be manageable.
Speaker 2:It is true that In the majority of situations, in the majority of lines of insurance, you do have to have your rates and forums Upproof by the regulator. An example of that would be do I charge X factor if your roof is shaped like a hip shaped roof, right where all the sides are angled, and I charge Y factor if your roof is only angled on two sides? The roof that's angled on all sides is better. Right, because the wind just sort of flows over it. Better, that's what they regulate. What they don't really regulate is how do I know that the roof is hip or gable right? And so that's where a lot of our big data innovation has come is on like, hey, let's make sure we have really accurate input data. Now, dirty secret. If the regulators did regulate that input data, they would find that the majority of the input data being used by the legacy industry is just wrong. Right, it's like literally default values in many cases, because the agent is lazy and they just sort of click through and they just make assumptions. Right, because there's not always a good way for them to know the exact angles of every part of the roof, just to use an example. So we found that to be a really good nexus for innovation is sort of on the input data.
Speaker 2:The other thing that is nice is, as a direct to consumer business, the state regulators don't pay as much attention to who are you marketing to. So I can have an interesting situation where I am really good at knowing if a customer has this is an example. I'm really good at knowing if a customer has a metal roof. Metal roofs are great, by the way. If you can afford a metal roof, you should get a metal roof. They're great. Then I can offer that customer an actuarially justified, regulatory approved lower rate because they have a metal roof that's really resilient to hail and storms and everything like that. Then I can create a marketing campaign that specifically markets to the customers with the metal roofs, right. So we have this sort of closed loop system.
Speaker 2:We also have customers where we're like oh, I can tell from aerial imagery, this customer doesn't have a very good roof, right, the shingles are curling at the edges, there's striping on them, like. We have image recognition algorithms that can tell us stuff like that. All right, well, we're not going to market to that customer. So all of our competitors are in the bad situation of like they obviously also don't want those roofs. They don't have a good way of knowing right, because a lot of times they don't have the same image recognition capabilities that we have. They don't really know which roofs are in one camp versus the other. But, even worse, they're like a hockey goalie, because the agents are just flinging all the houses at them and they have to be defensive. And if they let one through by mistake, or because they didn't validate the data, or because the agent was good at finagling their system, well now they're in trouble. Right now they have something in their portfolio that doesn't really blog in their portfolio, so the marketing lever has actually turned out to be really important to us.
Speaker 1:In terms of this data advantage and analytics advantage. I mean, it's super innovative. What you guys have done Are all of the advances and LLMs and open AI and all of that type of thing. Is that helpful for you because it allows you to advance your capabilities, or is it hurt because it lowers the bar dramatically for other people to kind of come in in ways that you really sort of pioneered? Or is it just a red herring and doesn't really matter a whole lot to what you're doing?
Speaker 2:I think it's more of like LLMs specifically, are a little bit more of a red herring. Now you do get a surprisingly decent answer. I'll just use an example. If you upload a picture of roof to to chat gpt and you ask it like, hey, describe the roof, for me it's like a 50% answer, which is surprising, right, because you know that it wasn't trained on images or roofs. Or you can upload like a building permit.
Speaker 2:Now, building permits are really messy data because they're different literally in every town. You can upload that. Similarly, it'll give you like a 50% answer, but we really need more like a 95% answer, and so what we've been doing is training specific models on specific input data, and LLMs are also really expensive to run right now. So the specific models, because they're trained on that, have a much higher degree of accuracy and for us that's important, right like we can't have 50% accuracy and they're a lot cheaper to run. So, generally speaking, the fact that the tools have all gotten a lot better is great for us and, I think, for anyone who's sort of trying to apply it in this way. The like APIs that are available right now I think are too general for this and we've really been training our own models.
Speaker 1:Yeah, well, hey, let me switch gears to another angle. I mean, I know that big part of your motivation is using technology to really help consumers and you're in a pretty unique spot to do that, and so I'd love your take on some of the places you're doing business. You know Florida, texas, hurricane zones, etc. You know customers obviously have the risk every day of a pretty severe incident, but they also have issues hard to get insurance, how to get insured at a reasonable price. You know get dropped all the time, etc. And you've kind of found an interesting way. So I wonder if you can talk about the customer angle of kind of what you guys are doing and how that's really pretty unique in this industry.
Speaker 2:Yeah.
Speaker 2:So it was in the early days where now I sort of knew in the back of my head like it's really hard to win in financial services if you go up the gut of the customers that, like everybody knows, are good. It's just hard to dislodge them right. If the legacy guys know that this is a really good customer, it's one they all want, you have a hard time attracting that customer, especially for sticky products. So we knew we needed to find like an underserved niche, right where it was maybe not as well understood by the legacy competitors or where we had a unique advantage. So we were sort of looking for that. And then the early days of marketing. We really just realized like you talked to somebody about this in Wisconsin, where I grew up, and they'd be like oh yeah, that's kind of neat. You talk to somebody about this in Florida or Texas or California. The lead for the oh, that's really interesting. Like actually my homeowner's insurance it just doubled in price or I only have three choices of insurance companies, or it's really hard I have to work with these sort of weird providers. They have all this like all this paperwork. It's really annoying right. And so there was like a real engagement there and we're like, oh, that's kind of interesting. Like you know, maybe there is a regional element to this. And then we looked at it more and we realized that the things that we were trying to do so you do have a difference right there. There is less availability of insurance and it is more expensive in areas where there's more extreme weather and More and more places every day are being exposed to extreme weather, and that's driven by global warming. Okay, that sounds like an interesting trend to be part of. You know, helping these customers who are underserved there's more and more of them every day. That market is growing.
Speaker 2:And then we thought about it tomorrow, like, oh, actually, this really maps very well to the things that we're good at. Why, okay, well, in the places where the insurance is more expensive, the agents, the distributors, still get paid the same percentage fee, so they're making a ton of money. Right, it's like your homeowners insurance companies in Florida might not be doing that well. Homeowners agents in Florida are doing incredibly well Because they do the same amount of work is an agent in Chicago and they get paid four times as much. Okay, well, that's interesting because we occupy that part of the value chain. Like I want to go there where the the economics are best for that part of there.
Speaker 2:And then we looked at the stuff we were doing on the data side. We're like, well, if I know Exactly the, the type of shingle that's on this roof, that's gonna make a hundred dollar price difference in Chicago and it's gonna make a thousand dollar price difference in New Orleans. Okay, well, obviously this skill that we have of understanding the data and the physical traits is way more useful in New Orleans that is in Chicago. So that was really the triangulation of data that led us into this. So we're we have a pretty coastal footprint. We're in South Carolina, mississippi, alabama, louisiana, florida, texas and Arizona and Arizona is the outlier there and the the next few states that we're launching. We're gonna be launching Another batch of states this year, in 2024. They're all sort of coastal states.
Speaker 1:When you guys hit Massachusetts, I'll be very excited to try to be a customer with our vacation place on the Cape.
Speaker 2:Yeah, no, getting getting insurance on the Cape is not easy, right, totally. There's a lot of places like that, you know Coastal Massachusetts, coastal Rhode Island, coastal New Jersey. They're all pretty hard places to get insurance.
Speaker 1:It's pretty expensive, yeah we, we, we definitely work with the agent and she has a new company for us every year, and so I'm very familiar with the problem you're you're talking about. Well, look, we only have about 10 minutes left. You know, we've kind of skipped half our script here just because the the business model of what you're doing is so, so interesting. But I'd love to ask just a couple questions, kind of about the inside of the company and kind of management as we close. I mean, you've now grown to roughly 500 companies. It's your third startup. I mean, how do you think about corporate culture? You know, as you had a couple startups that wound up selling pretty early, you've now probably learned a ton from that building your first startup when you were small. I'm sure you've learned a ton more as this one has grown and gotten to be large. How do you think about company culture and what are some of the, the lessons that a listener could take away?
Speaker 2:culturally. What's always been really important to us again is To make sure the people who are closest to the data are making the decisions. But I feel like a lot of the times you have a lot of waste where it's like, especially in bigger companies, people are like oh, let me ask my boss about that. Okay, well, you know your boss is probably not as knowledgeable about this problem as you are, so that's always been a really key value for us. Another one is We've always had this idea of running through walls and this is certainly true. Like to do this is really hard. We do accomplish the impossible every day, and then we had to amend that this was an interesting one. As we got bigger, we had to change that stated core value to be run through walls together, because you'd have these guys who would just like be like I'm gonna solve this problem and they go and they do it and have all of these unintended consequences. You're like well, you did solve that problem. But Second, with these other two problems you cost Just because the problems are solving are more complicated now that we're bigger. So you know we've had to evolve over time.
Speaker 2:We try to be a company that's like really low ego and really focused on the numbers. And it's that can be hard as an entrepreneur, especially somebody who is pursuing an idea that is at least somewhat visionary, because on the one hand, you have to have the vision but then also like, well, don't focus on that too much. Like here's what we have to do tomorrow, like do this one small thing tomorrow and then we'll do another small thing tomorrow and then we'll do another small thing the day after, and that's what adds up here. And I feel like it's certainly in our space. You know, and sure, tech was this space with a lot of big ideas but a lot of companies that did stupid things, like trying to scale with unit economics that weren't positive. Okay, like let's not do that. You know, let's try to be somewhat practical in our approach here. So that's that's always been a big part of our DNA is just being practical.
Speaker 1:Yeah, it makes sense. It's a great example of the lesson of the run through walls right, where one thing works when you're 10 or 20 people and the core kernel still is important when you get bigger. But needing to modify it, I mean you're kind of be close to the data really resonates really to Capital One, I mean I think we were also had a very similar kind of approach. I mean I think I was managing a hundred million dollar marketing budget at age 23 or something which I look back and I'm like, wow, they were stupid to let me do that. But similar concept right, like the people doing the analytics, making decisions. And I think there's also a good example when the company scales creates a challenge, because bottoms-up companies can just kind of turn into a bit of a you know gummed up mess To sort of make decisions as you get bigger, and so how to how to figure that out will be be an important one, I think so.
Speaker 1:So here's another, another angle. I know when, when COVID hit you and everybody else kind of went remote Big debate now of some companies have gone back to almost all in person a bunch of hybrids, a bunch of people love remote. I mean you're more on the remote side, if I, if I understand correctly. How have you thought about that? And is it, you know, just kind of a no-brainer good for you guys? Or are there kind of trade-offs that you wish you had more in person? I mean, how have you thought about that?
Speaker 2:that trade-off there are definitely trade-offs and we were very much an office company before this. You know, we had an office in In Tampa area and we had office in Chicago and everyone was in one of the two offices every day and we loved it. Like we had so much fun together and we play ping-pong and all that. I would cook people breakfast, like it was a huge part of our part, of our culture. We didn't have a choice right like we had to go home and we, just because of how we grew, like our company, tripled in size over that time period and and so by the time we were able to gather again we had people all over the country and we're like well, this is really great. On the one hand because I can, especially because we're not in Silicon Valley and like neither Chicago nor Tampa are like big talent centers, for us it was a net positive trade to be able to recruit everywhere. I think maybe if you're in Silicon Valley that's not as true you know, because they have such a such a concentration of talent there, but for us it was really great to be able to open a recruiting aperture.
Speaker 2:The Coventine was hard on the cultural side because we could never see each other and now we just we just get together a lot, you know, and we get together on the individual level, we get together on the group level, we get together on the company level. We've offices where it's more like it's a small office but there's space we can flex into and that's been really nice because we can get a larger group of people together. And so you know we're committed to it. I think there might like there's another version of the world. We're like we never went Remote because there are costs to it. Right, it is harder to coordinate, it's harder to build culture, but there's no putting the genie back in the bottle for us. You know that we just have too many people in too many different places to say, hey, go back to the office. It's, half of the company doesn't live anywhere near our offices.
Speaker 1:Yeah. So I spent a fair amount of time internationally and it's always a bit of a debate of now that you can hire remote people, should you go with a global talent base or what are the benefits of having everyone together, and you know, everyone kind of comes up with different conclusions. It's fascinating how how broad it is and it sort of makes sense. Over time things would shuffle into multiple options and employees Over time can self-select into the environment. That works and you know, it seems pretty clear you can be successful. So look as we close and I really appreciate the time there's probably could could easily go another another hour here. You know one thing I see saleboats right behind you and the piece of art. I know you've got a huge hobby for fixing old sailboats. How did that come to be?
Speaker 2:I don't know, that's a good question. That was. I grew up sailing and then I didn't do it for a long time and then In COVID I sort of picked it back up again because it was one of the things you could do. So I got one sailboat and I sailed that it. First I was just like I want to sail, I'm gonna like go down to the beach and rent a sailboat, but of course that wasn't open because COVID was shot. So, okay, now I have to buy my own sailboat. And then I just got into it and I bought another one and I don't know, it's a fun thing to do for me. I like being out in nature, that's awesome.
Speaker 1:Do you ever do any of the the long haul like Mackinac races? Had a good buddy of mine in high school that does all these Port Huron to Mac, Chicago to Mac. But sailing on Lake Michigan must be pretty fantastic. Yeah, like Michigan's pretty nice sailing.
Speaker 2:It's very close, you know, in Chicago You're right here.
Speaker 1:Well, look, I really appreciate the time today we try to close with with the same question for everyone. Hopefully we have a number of aspiring entrepreneurs listening to. These love to know. You know, if you were to give one tip to an aspiring entrepreneur given your you know on your on your third radio right now, what would that be figure?
Speaker 2:out the unit economics first. It's the thing like just so much time is wasted, so much money is wasted Scaling businesses that don't have positive unit economics and never will like don't Really have so many years on this planet. You know, let's find something it's sustainable and then figure out how to scale it. No, that's awesome.
Speaker 1:Well, that was a lesson that the world kind of forgotten 2020 and 2021 but I think it's quickly trying to remember again and very much agree with that. So so, sean, thanks so much for joining today. It's been a great conversation. We're huge fans of kin and love the innovation that that you're doing here, so really appreciate you spending the time. Thanks for having me and all you listeners. Till next time, take care, and thanks for listening. This has been the FinTech thought leaders podcast your window into the world of venture capital and financial services with today's digital disruptors. Qed is proud to provide the best FinTech advice you can get. To learn more or to read the full show notes from today's episode, check out QED investors comm and be sure to also follow QED on Twitter and LinkedIn at QED investors. Thanks for listening you.