April 8, 2024 • 44 mins
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.
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Speaker 1 (00:08):
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(00:30):
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place.
Hello and welcome to theFinTech Thought Leaders podcast.
I'm Bill Salufo, head of earlystage investments at QED
Investors.
Today on the podcast, I'mexcited to be joined by Sean
Harper, ceo and co-founder ofKin Insurance.
Sean, welcome to the podcast.
Speaker 2 (00:50):
Hey, good morning Bill, Thank you.
Speaker 1 (00:53):
Hey, just as a way to kick things off, so the
listeners have some context, Iwonder if you can give us maybe
a 60 second pitch on what KinInsurance is and what you guys
do.
Speaker 2 (01:01):
Yeah, of course.
So Kin is a high tech providerof homeowners insurance.
Homeowners insurance is areally big market and when I
started Kin I was really excitedbecause 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 thathas a kegger that high.
(01:22):
So the two things that aredriving it are increased
investment in housing andincreased weather volatility.
I think both of those arepretty enduring trends.
So that's the market we're in,and that's a market that's, by
and large, occupied by sort ofan oligopoly of like 40-ish
insurance companies.
(01:43):
There's a long tail, but mainlyit's like 40.
So it's not a very competitivemarket and there hasn't been a
lot of innovation.
So the innovation that we'rebringing is sort of three parts.
The first is a business modelinnovation, if you could call it
that.
I think it's pretty simple.
We go direct to the customer.
So 95% of homeowners insuranceis sold through these local
(02:05):
agents.
There are actually more than400,000 retail insurance
agencies in the US.
That's four times, it's morethan four times the number of
bank branches and it's more thantwo times the number of fast
food restaurants.
Believe it or not.
That's frightening, isn't thatnuts?
(02:25):
And people actually go to theirbank.
Nobody goes to their insuranceagent ever really.
Like you can camp outside theoffice and the only person going
there is, like, the owner ofthe agency.
So 95% of it is sold throughthese agents and that creates
some issues.
The first is they're veryexpensive.
That branch network costs about20% of the premiums to maintain
(02:47):
for the insurance carriers, soit's a huge extra cost.
The second is customersactually don't really like it.
Like if you survey customerssay, hey, would you rather buy
from an agent or would yourather buy directly?
It's like 70% would prefer tobuy directly.
And if you say, are you willingto pay extra to have the local
agent, it's like almost none ofthe customers are.
(03:11):
And then, finally, by goingdirect to the consumer, we can
actually use marketing as amethod of risk selection and
we'll get more into this later.
But there's a huge variance inthe types of houses and how.
In homeowners insurance, mostof the events, most of the bad
things that cause a loss, it'sweather related.
Okay, all the houses are goingto get hit by the same weather,
(03:34):
but they're all going to responddifferently because the homes
are built in these idiosyncraticways, like your roof is like
different than my roof and mybathrooms are different than
your bathrooms and all of that.
So we can actually pick andchoose the customers that we
know are living in homes thatare more resilient to the
weather and we can specificallymarket to those customers.
(03:56):
So that's the business modelinnovation we're direct to
consumer.
It helps us with risk.
Speaker 1 (04:00):
Whereas the folks that are distributing through
agents can't do that becausethey take whoever the agent goes
and finds and the agent'sprobably not incented to do that
right.
Speaker 2 (04:07):
That's right.
And they have very crude waysof doing it right.
They'll tell the agent like hey, your portfolio looks like X,
like we'd like less of this andlet more of this.
But it's not very nuanced theway they can do it.
They don't actually havecontrol, they just sort of have
like influence, right.
And then the second thing we dodifferently is tech innovation,
which plays very well with thebusiness model innovation.
The technological innovation istwo parts.
(04:28):
First, we've built from scratchthe best, most cutting edge,
efficient insurance coreprocessing system.
It's proprietary to us.
Most of our competitors don'tcontrol their own core
processing systems.
They're outsourcing it to acompany like guidewirecreek
which for the most part, are notvery good software.
It suffers 30 or 40 years old.
(04:50):
For the most part it's stillon-premises software, like it's
really, really not good.
So that gives us a bigadvantage.
And the second part of the techinnovation is All the insurance
companies, us included, we allthese like smart actuaries
pricing it stuff like that.
Right, they're crunching thenumbers.
They're saying, hey, for homesthat look like this, we expect
(05:10):
the losses to be like that andthat's how we're gonna come up
with our pricing.
The issue is the way they getall the input data.
For that our legacy guys isAsian.
Well, that's silly, thatdoesn't make any sense, like the
agent has never been to thehouse, right?
The agent also isn't anarchitect, he doesn't know about
(05:32):
the shingles on your roof, he'san insurance agent.
And then, third, they actuallyaren't compensated for accuracy
of data.
And so you'll find is, theagents are constantly fudging
the data because that's theirjob.
They're like trying to help thecustomer by getting them a
lower price.
Now, there are lots of badthings that can happen when you
flush the data, and one of themis inaccurate pricing.
It's important to note thatthat's a problem at the point
(05:55):
like for that risk, right?
It's like, oh, if you pricethis as If the roof was really
good and the roof is actuallyreally bad, you're gonna be
underpriced for that risk.
It's also an even biggerproblem because it fundamentally
breaks their actual, realmodels, their pricing models,
because, if you think about it,they're feeding all of these
models and training them onbogus and potato Sure, so you
actually don't see thecorrelations that it actually
(06:18):
exists, and so it sounds like,then you must have a process to
collect all this data directlyfrom the source we do so we're
Manufacturing our own data andwhat we're doing is where
there's like like all you haveto do is Google your address and
you can see there's just liketons of unstructured data out
there about your home searchimages, there's MLS records,
there's government, there's taxrecords, building permits,
(06:40):
there's all this stuff.
No, it's all unstructured andthat's the problem with it.
So what we've been doing forthe last seven years is training
a set of models To take thatunstructured data and package it
into structured data and thentraining our pricing and
underwriting models on thatinput data.
So it's not always perfect,right, you don't always have
perfect information, but it'sway, way, way better than the
(07:03):
alternative, which is just likeasking some guy who actually has
a bunch of reasons where he'llbe inaccurate right so that.
Speaker 1 (07:10):
So that allows you to basically use this under
undercut price on the bestcustomers and then lose the
worst customers because they'reyou know, they're not as good as
the other the industry thinksthey are that's right.
Speaker 2 (07:21):
It's more accurate, it's more accurate pricing and
that ends up being a bigger dealin some places than others.
Basically, what you find is inareas where there's more weather
volatility, having accuratepricing makes a much bigger
difference.
So where I live in northernIllinois, we don't have that
much weather volatility, and ifyou know exactly the type of
(07:42):
shingles that's on my roof, it'shelpful you know, it doesn't
hurt.
Yeah, but if I lived inCharleston, south Carolina or
Tampa Bay?
Speaker 1 (07:52):
you probably only care whether somebody has a flat
roof or a slope roof, so thethree feet of snow doesn't
doesn't cause a leak.
Speaker 2 (07:58):
That's right, and the roof sheath is an interesting
one because there's more nuancesto that, like, no roof is truly
flat and the roof's all ofdifferent angles on the slopes.
You actually want to take thatinto consideration.
So there's a lot of nuance inreshape since you bring it up,
but it really can make a bigdifference in some of these
places.
And and this is where you seethe headlines one of the reasons
(08:19):
why you see the headlines ofInsurance company X is leaving
wide geography entirely.
Okay.
Well, that's because theyactually Don't know how to tell
the difference between the homesthat are going to be profitable
for them in the homes thataren't.
And we're able To do that, andit allows us to serve these
customers who need us the most,the folks who live in
(08:39):
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 (08:47):
Yeah, that's, that's fantastic.
Well, look before we, before wego into the next several layers
of can, I'd love to spend justa minute or two exploring your
background.
I mean, I know you got into thestarter startup world quite
early after you started yourprofessional career.
Wonder if you can just talkthrough kind of how you got into
the startup world and what wasit about startups that really
attracted you.
Speaker 2 (09:07):
So I was a tech guy first.
So like I was one of those kidsthat grew up Programming, you
know, and I was just really intomaking like these stupid little
apps and games and Stuff likethat.
You know, it's like going backto middle school now and Then I
was like, well, I'd actuallyreally like people to use the
(09:28):
software you know.
So that sort of got me moreinto the business part of it,
right?
And then I realized I wasactually really interested in
business.
I was really interested ineconomics.
I ended up going to school, youknow I'm majoring in computer
science and economics, which isa cool combination, and I've
really been doing FinTech stuffever since.
So my first gig out of collegewas like, like when I was in
(09:48):
college I did the same likeinvestment, banking internships
that everyone else does.
But my first gig out of collegeWas working at PCG.
I was mostly working withfinancial services companies
like mostly retail banking,credit cards, pnc, insurance.
I was like really interesting.
But I looked at myself aftertwo years and I was like, well,
gosh, sean, I always thought youwere gonna be like a tech guy.
(10:10):
Like what are you doing makingspreadsheets for banks all day?
Speaker 1 (10:13):
This is stupid, that's not what you're doing, so
I quit.
Hey, that is high-tech at somebanks.
Speaker 2 (10:18):
Come on, some of my spreadsheets were we're
legendary and a lot of acros inthese spreadsheets.
That's why I quit and I joineda VC firm and I did that for a
couple of years, like reallyearly 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 wasaiming to do when I was working
(10:38):
at VC I.
One of the things I learned islike, actually, at least in that
case, I had a lot more sparetime after being, you know,
working as a VC analyst versusFour days a week and travel at a
management consulting firm.
And so I actually started myfirst company in parallel with
that gig, and it was an onlineretailer's e-commerce company.
(11:01):
There was like a specificproduct that we were interested
in selling.
My co-founder and I in thatbusiness it sounds silly now we
were the largest Seller ofreplacement parts for satellite
radio systems, which at the time, was like this huge consumer
product.
Speaker 1 (11:20):
I, I still have one, and in all my cars.
Yeah, serious yeah it's.
Speaker 2 (11:25):
It's actually a really good product, you know,
and at the time I had done aconsulting case for a retailer
and I realized how much moneywas in these sort of spare parts
, because people are relativelyprice insensitive.
If you're like, oh, I have thisthing 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 Becausethe adapter, the power adapter
(11:47):
for my serious satellite radiostopped working, you, you didn't
care if you were paying 20bucks or 15 bucks to get the
replacement part.
And so you know that was thebusiness that we built.
It was really fun.
It was a bootstrap business.
We were on the Inc 500.
It was like cool, and so thatwas the beginning.
And then I did another business.
It was a payment processingcompany, similar.
I had had some consultingexperiences in that space.
(12:08):
I had consumed the product, thepayment processing product, at
my prior company, right, the oneI had started, though I see an
opportunity here to do it better.
The product that we launchedwas like basically Stripe, but
without a $20 million seed round.
Speaker 1 (12:23):
Our seed round was only like $2 million.
Speaker 2 (12:25):
Details, details.
So when we were out raising ourseries A, we got an acquisition
offer and we took it and Ispent I spent three years at the
Acquire, which was Groupon.
I built the Square competitorwithin Groupon.
I thought that was really aninteresting experience.
But ultimately my future wasgoing to do something
entrepreneurial again.
(12:45):
Right, Take a bigger swing.
I was a single.
I wanted to go and swing againand then that was sort of when I
started incubating Kin andthinking about what ultimately
became Kin and I sort of took ayear to explore ideas and do
some little independentconsulting gigs and do some
angel investing and stuff likethat, but ultimately kick the
(13:07):
tires on a bunch of things andthis was the opportunity that
excited me the most.
Speaker 1 (13:14):
So it seems pretty logical how you could go from
e-commerce to a Stripe-like typething.
I mean, I get that you spent ayear looking at different ideas,
but homeowners insurance ispretty out of left field given
what you had done so far.
What even got you to startthinking of that idea?
Speaker 2 (13:28):
Well, interestingly, actually, I had a consulting
case at one of the big insurancecompanies where I spent a lot
of time in insurance agents'offices.
It was like a go-to-market casefor this insurance company and
I just saw how wasteful thatagent experience was and I saw
how weird it was that the actualinsurance company would be like
(13:52):
one of the questions they askedus was like well, why do
customers come to us?
Like oh, that's kind of a weirdquestion for you to be asking
me, 24-year-old managementconsultant, like don't you know?
They're like no, no, actuallywe don't.
Because the customers go to theagents Just while these guys
weren't really doing fieldunderwriting, as they would say,
(14:14):
like they didn't really knowvery much about the risks, they
were just kind of going toZillow and copying some stuff
off of Zillow and that wasactually the data that was
getting fed into theseunderwriting algorithms.
And then you also noticed thatthose agents were really
high-margin business.
They were actually a muchhigher-margin business than the
(14:34):
carriers themselves.
Like the carrier was workingfor they were like happy if they
had a 5% net margin year, likethat was a good year for them.
And then if you crunch thenumbers on these agents, which
are actually sort of all small,below-scale businesses.
You're like whoa, these arelike 40% EBITDA margin.
Businesses Like this isactually, if you draw the whole
(14:56):
value chain out, the agents areextracting the majority of the
value.
Why is?
Speaker 1 (15:00):
that.
So a bit of an aside from Ken,but do you know why PE hasn't
tried to roll up all theseagencies?
Oh they have.
Oh, I got you.
Speaker 2 (15:07):
There's a huge amount of roll-up activity in PE and
insurance agencies, especiallyon the commercial side.
There are two private companiesthere's Accra, shure and Hub.
These are both like $20 billionmarket cap private companies.
Pe roll-ups Got you.
Speaker 1 (15:20):
But because there's so many, most of them are still
these little mom and pop.
Right, you got it.
Speaker 2 (15:24):
That's exactly right, especially on the personal line
side, where I think there'sbeen less consolidation activity
.
There's still quite a lot goingon in personal lines.
It's just so big right there'sso many.
There's like 400,000 of them.
This is a lot.
So I had some context for theproblem.
We actually drew the map of allof financial services.
(15:47):
I was like I'm looking forsomething that meets a few
criteria.
I want something where it'soverly intermediated.
Now, some things are reallycomplicated and you need a lot
of intermediaries.
A lot of products, especiallyconsumer financial products, are
not that complicated.
You don't need the intermediarythere.
It actually makes it worse.
Speaker 1 (16:06):
Well, if anyone can relate to the problem, I guess
it's the QED, with our CapitalOne heritage, which that was a
big part of the premise, tooright, why would you get a
credit card in a branch?
Why?
Speaker 2 (16:14):
would you get a credit card in a branch?
Absolutely yeah, very much so.
Capital One was a biginspiration to us.
It was a company that we was onour list of.
We could be like the CapitalOne for homeowners insurance.
That would be really cool.
Then we were also looking forsomething where there were new
data sources that could be usedfor pricing and underwriting.
(16:35):
We looked at everything.
There was one other idea thatrose to the top.
It was also an insurance.
It was small business insurance.
Ultimately, we decided thatthis was better because it has a
larger, more homogenous market.
Small business insurance hasmany of the same problems that
(16:57):
homeowners insurance has.
The reason why we didn't wantto go after that is because
small business is really a bunchof different niches.
You actually do need adifferent product, a different
go-to-market to get the yogastudio versus the coffee shop
versus the car dealer.
We just loved the fact thathomeowners insurance was so big.
It was so homogenous.
It met our criteria,specifically around new data
(17:21):
sources being available, aroundit being overly intermediated.
Speaker 1 (17:26):
That makes a lot of sense.
When you decided to start thesatellite radio parts company,
did you think about thisinsurance idea then?
Or more just, you had done thecase.
You weren't really connectingthe dots until you went back and
systematically looked at itlater.
Speaker 2 (17:42):
I always have this entrepreneurial.
I'm always thinking aboutthings I can do I'd actually
thought about when I was at BCGdoing some of these cases.
I thought about doing a roll-upin the space.
Then, after doing my paymentscompany, I realized how
important product-led growthcould be in some of these things
(18:03):
, even in, yeah, you thinkpayments is a commodity it kind
of is.
But also there really was areal appetite from the customer
to have a more streamlinedexperience, to not have to
answer all the dumb questions,to not have a seven-day
underwriting period.
There's all these sort of nutsand bolts things about the
(18:23):
product.
That was really inconvenient.
That led me to think of more ofa product angle for insurance.
I was like, yes, I do want tooccupy the part of the value
chain that's so inefficient,that's so distributed, with
400,000 people doing it, wherethey have such high margin that
they don't really deserve maybefor the work that they're doing.
(18:44):
But also I want to do that byhaving a really unique value
proposition to the customer.
Speaker 1 (18:50):
You obviously had a big industry.
You were looking for a startup.
You had seen the problemfirsthand.
Were there any major obstacles?
That you saw as you started tothink about this and like, oh,
maybe I shouldn't go do this,that you had to get over to take
the leap.
Speaker 2 (19:05):
We knew the two biggest question marks were do
customers want this?
Can we manufacture itefficiently?
We had to answer the firstquestion, which was what do
customers want this?
What we did is we actuallybought a little insurance agency
and we wrapped it in a littlebit of UX.
It wasn't that expensive, itwas a few hundred thousand
(19:28):
dollars.
We wrapped it in a little bitof UX and we started to run
marketing experiments and reallygo, okay, like, after doing
this for six months, the uniteconomics well, they're not
great.
But like, if I increaseconversion rate here and I
increase conversion rate hereand I reduce the churn there,
(19:49):
then they will be great.
Right, they'll go from beingsort of okay to being great.
It's like okay, cool.
So we did that.
Then the next thing was okay,well, how do I get control of
the whole stack?
Right, because as an agent, wedidn't have any differentiation,
differentiated valueproposition.
That was one reason why yourconversion rate was low.
Okay, so how do I control thewhole stacks?
Like a manufacturer, a productthat is differentiated?
(20:14):
Now I come from the paymentsworld and I've done some angel
investing and lending and I waslike, oh, we got a fight.
We have to find a rents acharter for insurance, like and
then we realized, oh, thatdoesn't actually exist.
Like there's this, there's thisother thing is sort of sort of
similar to a rented chartercalled the front-end carrier.
You sort of rent out theirinsurance licenses.
It's not as well established anecosystem as the rent a charter
(20:38):
, but that that was sort of ournext step.
We're likely we know we're not.
It's a very difficult industryto approach.
It's no accident that it'sbasically an oligopoly, right
Like there is designed to keeppeople out.
So we know we're gonna need toclimb up the stack.
You know, one wrong at a time.
So okay, next step is let's setup something where we're an MGA
(20:59):
which is sort of like a virtualinsurance company gave us all
the control over the productthat we wanted.
You know there were somedownsides to being an MGA.
It actually didn't give us allthe control over the product we
wanted.
It created a real existentialrisk for the business Because
you had this one counterpartythat was like so important to
you.
So we knew there were somedownsides, but we were like,
(21:20):
well, we don't have the money tostart insurance companies.
Speaker 1 (21:23):
So are you an insurance company now?
Are you still kind of in theprocess of working up that
regulatory stack?
Speaker 2 (21:28):
We are.
I'll tell.
I'll talk to you about that ina second.
So that was the next step.
So, basically, the first yearwe were running these marketing
experiments.
The second year we were like,okay, cool, now we have this
business relationship as an MGAwhere we can run everything
through our software.
Okay, but we have to hustle tolike write the core processing
system, which is not an easytask to write a good core
processing system for insurance.
(21:49):
Now We've rewritten that thinglike five times since, right,
because it was very much likehey, here's the MVP.
Like we need to be able toissue a policy, like I need to
be able to take the custom rain,get the underwriting info
manufacturer the data, print aPDF, basically Obviously.
Now, six years later, thesystem does a lot of stuff in
addition to that and does itreally really well.
(22:10):
But that was sort of the nextstep.
Then, two years later, we'relike, okay, well, we're like
kind of on the verge of being ascale.
This is 2019.
We're kind of on the verge ofbeing a skilled business.
We have unit economics that areworking.
We're really worried about thisrelationship with this, the sort
of single threaded relationshipthat creates an existential
(22:32):
risk for the business.
The MGA relationship isn'tgiving us all the degrees of
freedom that we want.
Let's go for the big time.
Let's start our own insurancecompany, and we started.
We now manage two insurancecompanies.
The first one we started was in2019, so it took a year to get
regulatory approval for that andI had to go raise a bunch more
(22:53):
money because we need tocapitalize this thing.
Now you use reinsurance andstuff To reduce the amount of
capital that you need, but westill needed 30 million bucks
sitting in a bank account, right.
So at the time we'd only raiseda Series A right, so we only
had 12 million bucks.
Then we went spent a lot ofthat on developers and stuff.
(23:13):
So we had to raise another round.
We had to wait a year forregulatory approval for this.
In the meantime, ourrelationship with the sort of
MGA sponsor got worse becausethey were like wait, you're
leaving, like what the heck this?
Why should we care about you?
Now that you're leaving, it'slike well, right, we gave you
warrants like you should careabout it.
It got a little bit tense there, but we eventually did get in.
(23:37):
It's.
It's interesting.
We actually have a novelfinancial structure when kin
actually isn't an insurancecompany.
What we did is we set up thesethings called reciprocal
exchanges.
Those are insurance companies.
They're fully licensed, theyhave capital, they have credit
ratings, they they're regulatedjust like any other insurance
(23:59):
company, but they're actuallyowned by our policyholders Hmm,
okay, and we manage them inexchange for a fee.
So our business actually lookssort of like an MGA business
where we're just getting thisfee, this recurring fee, to
manage it.
Or it looks kind of like a, likean asset manager almost right,
like you could think about thislike we're the GP and Our
(24:22):
customers are the LP in theseinsurance exchanges.
No, it's a good structure forus Because it gives us a stable
recurring revenue stream.
Our profit center is what ourlegacy competitors cost centers
are right, the agents and theoutside software right, so we're
basically keeping that margin.
(24:43):
They think about what ourlegacy competitors.
Their profit center isUnderwriting income and
investment income, and and we'reactually leaving that in these
customer owned exchanges, right,so the customers are literally
getting a better deal and Ithink it's all ultimately makes
it very difficult for ourcompetitors to compete with us,
right, because, like what'shistorically been their profit
(25:05):
center, I'm just giving back tothe customer.
Speaker 1 (25:07):
Yeah, it's funny, my wife's whole family is
ex-military and so I know she'salways tickled every year when
we get our USA rebate check.
Yes, it's similar, similar kindof concept, right, very similar
so.
Speaker 2 (25:17):
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 MidAtlantic.
The management company of Erieis publicly traded, so it's sort
of an interesting company forus to go and look at.
But yeah, it's very similar towhat you just described with USA
.
We don't give the dividendcheck, we just leave it in there
(25:39):
and give the customer a lowerprice.
But you can do either.
Speaker 1 (25:43):
So let me, let me dive into this.
I know you know, back when youwere giving the the intro to kin
, you talked about you know,look, you're innovating across a
number of vectors.
I know innovation and insurancehas been notoriously slow and
I'm sure there's tons of factorsbehind that, one of which being
Correct me from wrong stateregulators have to approve all
of your pricing schemes andyou've got to show you know a
(26:04):
bunch of data behind it.
I'm guessing state insuranceregulators aren't really at the
cutting edge of large languagemodels and all of the tools that
you use to convert yourunstructured data to structured
data, etc.
How have you thought about thatand how have you overcome that
obstacle?
And and how big of an obstacleis it from?
You know, say, a perfect worldwhere you could kind of go do
whatever testing you wanted todo and do whatever you wanted.
(26:25):
We've found it to be manageable.
Speaker 2 (26:28):
It is true that In the majority of situations, in
the majority of lines ofinsurance, you do have to have
your rates and forums Upproof bythe regulator.
An example of that would be doI charge X factor if your roof
(26:48):
is shaped like a hip shaped roof, right where all the sides are
angled, and I charge Y factor ifyour roof is only angled on two
sides?
The roof that's angled on allsides is better.
Right, because the wind justsort of flows over it.
Better, that's what theyregulate.
What they don't really regulateis how do I know that the roof
(27:09):
is hip or gable right?
And so that's where a lot ofour big data innovation has come
is on like, hey, let's makesure we have really accurate
input data.
Now, dirty secret.
If the regulators did regulatethat input data, they would find
that the majority of the inputdata being used by the legacy
industry is just wrong.
Right, it's like literallydefault values in many cases,
(27:32):
because the agent is lazy andthey just sort of click through
and they just make assumptions.
Right, because there's notalways a good way for them to
know the exact angles of everypart of the roof, just to use an
example.
So we found that to be a reallygood nexus for innovation is
sort of on the input data.
The other thing that is nice is,as a direct to consumer
(27:53):
business, the state regulatorsdon't pay as much attention to
who are you marketing to.
So I can have an interestingsituation where I am really good
at knowing if a customer hasthis is an example.
I'm really good at knowing if acustomer has a metal roof.
Metal roofs are great, by theway.
If you can afford a metal roof,you should get a metal roof.
(28:14):
They're great.
Then I can offer that customeran actuarially justified,
regulatory approved lower ratebecause they have a metal roof
that's really resilient to hailand storms and everything like
that.
Then I can create a marketingcampaign that specifically
markets to the customers withthe metal roofs, right.
So we have this sort of closedloop system.
(28:36):
We also have customers wherewe're like oh, I can tell from
aerial imagery, this customerdoesn't have a very good roof,
right, the shingles are curlingat the edges, there's striping
on them, like.
We have image recognitionalgorithms that can tell us
stuff like that.
All right, well, we're notgoing to market to that customer
.
So all of our competitors arein the bad situation of like
(28:59):
they obviously also don't wantthose roofs.
They don't have a good way ofknowing right, because a lot of
times they don't have the sameimage recognition capabilities
that we have.
They don't really know whichroofs are in one camp versus the
other.
But, even worse, they're like ahockey goalie, because the
agents are just flinging all thehouses at them and they have to
be defensive.
(29:20):
And if they let one through bymistake, or because they didn't
validate the data, or becausethe agent was good at finagling
their system, well now they'rein trouble.
Right now they have somethingin their portfolio that doesn't
really blog in their portfolio,so the marketing lever has
actually turned out to be reallyimportant to us.
Speaker 1 (29:37):
In terms of this data advantage and analytics
advantage.
I mean, it's super innovative.
What you guys have done Are allof the advances and LLMs and
open AI and all of that type ofthing.
Is that helpful for you becauseit allows you to advance your
capabilities, or is it hurtbecause it lowers the bar
dramatically for other people tokind of come in in ways that
you really sort of pioneered?
(29:58):
Or is it just a red herring anddoesn't really matter a whole
lot to what you're doing?
Speaker 2 (30:03):
I think it's more of like LLMs specifically, are a
little bit more of a red herring.
Now you do get a surprisinglydecent answer.
I'll just use an example.
If you upload a picture of roofto to chat gpt and you ask it
like, hey, describe the roof,for me it's like a 50% answer,
(30:24):
which is surprising, right,because you know that it wasn't
trained on images or roofs.
Or you can upload like abuilding permit.
Now, building permits are reallymessy data because they're
different literally in everytown.
You can upload that.
Similarly, it'll give you likea 50% answer, but we really need
more like a 95% answer, and sowhat we've been doing is
(30:48):
training specific models onspecific input data, and LLMs
are also really expensive to runright now.
So the specific models, becausethey're trained on that, have a
much higher degree of accuracyand for us that's important,
right like we can't have 50%accuracy and they're a lot
(31:08):
cheaper to run.
So, generally speaking, thefact that the tools have all
gotten a lot better is great forus and, I think, for anyone
who's sort of trying to apply itin this way.
The like APIs that areavailable right now I think are
too general for this and we'vereally been training our own
models.
Speaker 1 (31:28):
Yeah, well, hey, let me switch gears to another angle
.
I mean, I know that big part ofyour motivation is using
technology to really helpconsumers and you're in a pretty
unique spot to do that, and soI'd love your take on some of
the places you're doing business.
You know Florida, texas,hurricane zones, etc.
You know customers obviouslyhave the risk every day of a
(31:49):
pretty severe incident, but theyalso have issues hard to get
insurance, how to get insured ata reasonable price.
You know get dropped all thetime, etc.
And you've kind of found aninteresting way.
So I wonder if you can talkabout the customer angle of kind
of what you guys are doing andhow that's really pretty unique
in this industry.
Speaker 2 (32:07):
Yeah.
So it was in the early dayswhere now I sort of knew in the
back of my head like it's reallyhard to win in financial
services if you go up the gut ofthe customers that, like
everybody knows, are good.
It's just hard to dislodge themright.
If the legacy guys know thatthis is a really good customer,
it's one they all want, you havea hard time attracting that
(32:29):
customer, especially for stickyproducts.
So we knew we needed to findlike an underserved niche, right
where it was maybe not as wellunderstood by the legacy
competitors or where we had aunique advantage.
So we were sort of looking forthat.
And then the early days ofmarketing.
We really just realized likeyou talked to somebody about
this in Wisconsin, where I grewup, and they'd be like oh yeah,
(32:51):
that's kind of neat.
You talk to somebody about thisin Florida or Texas or
California.
The lead for the oh, that'sreally interesting.
Like actually my homeowner'sinsurance it just doubled in
price or I only have threechoices of insurance companies,
or it's really hard I have towork with these sort of weird
(33:12):
providers.
They have all this like allthis paperwork.
It's really annoying right.
And so there was like a realengagement there and we're like,
oh, that's kind of interesting.
Like you know, maybe there is aregional element to this.
And then we looked at it moreand we realized that the things
that we were trying to do so youdo have a difference right
there.
There is less availability ofinsurance and it is more
(33:33):
expensive in areas where there'smore extreme weather and More
and more places every day arebeing exposed to extreme weather
, and that's driven by globalwarming.
Okay, that sounds like aninteresting trend to be part of.
You know, helping thesecustomers who are underserved
there's more and more of themevery day.
That market is growing.
And then we thought about ittomorrow, like, oh, actually,
(33:56):
this really maps very well tothe things that we're good at.
Why, okay, well, in the placeswhere the insurance is more
expensive, the agents, thedistributors, still get paid the
same percentage fee, so they'remaking a ton of money.
Right, it's like yourhomeowners insurance companies
in Florida might not be doingthat well.
Homeowners agents in Floridaare doing incredibly well
(34:19):
Because they do the same amountof work is an agent in Chicago
and they get paid four times asmuch.
Okay, well, that's interestingbecause we occupy that part of
the value chain.
Like I want to go there wherethe the economics are best for
that part of there.
And then we looked at the stuffwe were doing on the data side.
We're like, well, if I knowExactly the, the type of shingle
(34:40):
that's on this roof, that'sgonna make a hundred dollar
price difference in Chicago andit's gonna make a thousand
dollar price difference in NewOrleans.
Okay, well, obviously thisskill that we have of
understanding the data and thephysical traits is way more
useful in New Orleans that is inChicago.
So that was really thetriangulation of data that led
(35:03):
us into this.
So we're we have a prettycoastal footprint.
We're in South Carolina,mississippi, alabama, louisiana,
florida, texas and Arizona andArizona is the outlier there and
the the next few states thatwe're launching.
We're gonna be launchingAnother batch of states this
(35:23):
year, in 2024.
They're all sort of coastalstates.
Speaker 1 (35:27):
When you guys hit Massachusetts, I'll be very
excited to try to be a customerwith our vacation place on the
Cape.
Speaker 2 (35:34):
Yeah, no, getting getting insurance on the Cape is
not easy, right, totally.
There's a lot of places likethat, you know Coastal
Massachusetts, coastal RhodeIsland, coastal New Jersey.
They're all pretty hard placesto get insurance.
Speaker 1 (35:46):
It's pretty expensive , yeah we, we, we definitely
work with the agent and she hasa new company for us every year,
and so I'm very familiar withthe problem you're you're
talking about.
Well, look, we only have about10 minutes left.
You know, we've kind of skippedhalf our script here just
because the the business modelof what you're doing is so, so
interesting.
But I'd love to ask just acouple questions, kind of about
(36:07):
the inside of the company andkind of management as we close.
I mean, you've now grown toroughly 500 companies.
It's your third startup.
I mean, how do you think aboutcorporate culture?
You know, as you had a couplestartups that wound up selling
pretty early, you've nowprobably learned a ton from that
building your first startupwhen you were small.
I'm sure you've learned a tonmore as this one has grown and
(36:27):
gotten to be large.
How do you think about companyculture and what are some of the
, the lessons that a listenercould take away?
Speaker 2 (36:34):
culturally.
What's always been reallyimportant to us again is To make
sure the people who are closestto the data are making the
decisions.
But I feel like a lot of thetimes you have a lot of waste
where it's like, especially inbigger companies, people are
like oh, let me ask my bossabout that.
Okay, well, you know your bossis probably not as knowledgeable
(36:54):
about this problem as you are,so that's always been a really
key value for us.
Another one is We've always hadthis idea of running through
walls and this is certainly true.
Like to do this is really hard.
We do accomplish the impossibleevery day, and then we had to
amend that this was aninteresting one.
As we got bigger, we had tochange that stated core value to
(37:16):
be run through walls together,because you'd have these guys
who would just like be like I'mgonna solve this problem and
they go and they do it and haveall of these unintended
consequences.
You're like well, you did solvethat problem.
But Second, with these othertwo problems you cost Just
because the problems are solvingare more complicated now that
we're bigger.
So you know we've had to evolveover time.
(37:39):
We try to be a company that'slike really low ego and really
focused on the numbers.
And it's that can be hard as anentrepreneur, especially
somebody who is pursuing an ideathat is at least somewhat
visionary, because on the onehand, 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 dotomorrow, like do this one small
(38:02):
thing tomorrow and then we'lldo another small thing tomorrow
and then we'll do another smallthing the day after, and that's
what adds up here.
And I feel like it's certainlyin our space.
You know, and sure, tech wasthis space with a lot of big
ideas but a lot of companiesthat did stupid things, like
trying to scale with uniteconomics that weren't positive.
Okay, like let's not do that.
You know, let's try to besomewhat practical in our
(38:25):
approach here.
So that's that's always been abig part of our DNA is just
being practical.
Speaker 1 (38:28):
Yeah, it makes sense.
It's a great example of thelesson of the run through walls
right, where one thing workswhen you're 10 or 20 people and
the core kernel still isimportant when you get bigger.
But needing to modify it, Imean you're kind of be close to
the data really resonates reallyto Capital One, I mean I think
we were also had a very similarkind of approach.
I mean I think I was managing ahundred million dollar
(38:48):
marketing budget at age 23 orsomething which I look back and
I'm like, wow, they were stupidto let me do that.
But similar concept right, likethe people doing the analytics,
making decisions.
And I think there's also a goodexample when the company scales
creates a challenge, becausebottoms-up companies can just
kind of turn into a bit of a youknow gummed up mess To sort of
(39:09):
make decisions as you get bigger, and so how to how to figure
that out will be be an importantone, I think so.
So here's another, another angle.
I know when, when COVID hit youand everybody else kind of went
remote Big debate now of somecompanies have gone back to
almost all in person a bunch ofhybrids, a bunch of people love
remote.
I mean you're more on theremote side, if I, if I
(39:29):
understand correctly.
How have you thought about that?
And is it, you know, just kindof a no-brainer good for you
guys?
Or are there kind of trade-offsthat you wish you had more in
person?
I mean, how have you thoughtabout that?
Speaker 2 (39:39):
that trade-off there are definitely trade-offs and we
were very much an officecompany before this.
You know, we had an office inIn Tampa area and we had office
in Chicago and everyone was inone of the two offices every day
and we loved it.
Like we had so much funtogether 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 rightlike we had to go home and we,
(40:05):
just because of how we grew,like our company, tripled in
size over that time period andand so by the time we were able
to gather again we had peopleall 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 neitherChicago nor Tampa are like big
(40:25):
talent centers, for us it was anet positive trade to be able to
recruit everywhere.
I think maybe if you're inSilicon Valley that's not as
true you know, because they havesuch a such a concentration of
talent there, but for us it wasreally great to be able to open
a recruiting aperture.
The Coventine was hard on thecultural side because we could
(40:46):
never see each other and now wejust we just get together a lot,
you know, and we get togetheron the individual level, we get
together on the group level, weget together on the company
level.
We've offices where it's morelike it's a small office but
there's space we can flex intoand that's been really nice
because we can get a largergroup of people together.
And so you know we're committedto it.
(41:08):
I think there might likethere's another version of the
world.
We're like we never went Remotebecause there are costs to it.
Right, it is harder tocoordinate, it's harder to build
culture, but there's no puttingthe genie back in the bottle
for us.
You know that we just have toomany people in too many
different places to say, hey, goback to the office.
It's, half of the companydoesn't live anywhere near our
(41:29):
offices.
Speaker 1 (41:30):
Yeah.
So I spent a fair amount oftime internationally and it's
always a bit of a debate of nowthat you can hire remote people,
should you go with a globaltalent base or what are the
benefits of having everyonetogether, and you know, everyone
kind of comes up with differentconclusions.
It's fascinating how how broadit is and it sort of makes sense
.
Over time things would shuffleinto multiple options and
employees Over time canself-select into the environment
(41:50):
.
That works and you know, itseems pretty clear you can be
successful.
So look as we close and Ireally appreciate the time
there's probably could couldeasily go another another hour
here.
You know one thing I seesaleboats right behind you and
the piece of art.
I know you've got a huge hobbyfor fixing old sailboats.
How did that come to be?
Speaker 2 (42:08):
I don't know, that's a good question.
That was.
I grew up sailing and then Ididn't do it for a long time and
then In COVID I sort of pickedit back up again because it was
one of the things you could do.
So I got one sailboat and Isailed that it.
First I was just like I want tosail, I'm gonna like go down to
the beach and rent a sailboat,but of course that wasn't open
(42:29):
because COVID was shot.
So, okay, now I have to buy myown sailboat.
And then I just got into it andI bought another one and I
don't know, it's a fun thing todo for me.
I like being out in nature,that's awesome.
Speaker 1 (42:41):
Do you ever do any of the the long haul like Mackinac
races?
Had a good buddy of mine inhigh school that does all these
Port Huron to Mac, Chicago toMac.
But sailing on Lake Michiganmust be pretty fantastic.
Yeah, like Michigan's prettynice sailing.
Speaker 2 (42:54):
It's very close, you know, in Chicago You're right
here.
Speaker 1 (42:56):
Well, look, I really appreciate the time today we try
to close with with the samequestion for everyone.
Hopefully we have a number ofaspiring entrepreneurs listening
to.
These love to know.
You know, if you were to giveone tip to an aspiring
entrepreneur given your you knowon your on your third radio
right now, what would that befigure?
Speaker 2 (43:13):
out the unit economics first.
It's the thing like just somuch time is wasted, so much
money is wasted Scalingbusinesses that don't have
positive unit economics andnever will like don't Really
have so many years on thisplanet.
You know, let's find somethingit's sustainable and then figure
out how to scale it.
No, that's awesome.
Speaker 1 (43:33):
Well, that was a lesson that the world kind of
forgotten 2020 and 2021 but Ithink it's quickly trying to
remember again and very muchagree with that.
So so, sean, thanks so much forjoining today.
It's been a great conversation.
We're huge fans of kin and lovethe innovation that that you're
doing here, so reallyappreciate you spending the time
.
Thanks for having me and allyou listeners.
(43:53):
Till next time, take care, andthanks for listening.
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(44:16):
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You're listening to the FinTech Thought Leaders
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(00:30):
If you're looking to learn moreabout what motivates our
founders and team members tosucceed, you're in the right
place.
Hello and welcome to theFinTech Thought Leaders podcast.
I'm Bill Salufo, head of earlystage investments at QED
Investors.
Today on the podcast, I'mexcited to be joined by Sean
Harper, ceo and co-founder ofKin Insurance.
Sean, welcome to the podcast.
Speaker 2 (00:50):
Hey, good morning Bill, Thank you.
Speaker 1 (00:53):
Hey, just as a way to kick things off, so the
listeners have some context, Iwonder if you can give us maybe
a 60 second pitch on what KinInsurance is and what you guys
do.
Speaker 2 (01:01):
Yeah, of course.
So Kin is a high tech providerof homeowners insurance.
Homeowners insurance is areally big market and when I
started Kin I was really excitedbecause 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 thathas a kegger that high.
(01:22):
So the two things that aredriving it are increased
investment in housing andincreased weather volatility.
I think both of those arepretty enduring trends.
So that's the market we're in,and that's a market that's, by
and large, occupied by sort ofan oligopoly of like 40-ish
insurance companies.
(01:43):
There's a long tail, but mainlyit's like 40.
So it's not a very competitivemarket and there hasn't been a
lot of innovation.
So the innovation that we'rebringing is sort of three parts.
The first is a business modelinnovation, if you could call it
that.
I think it's pretty simple.
We go direct to the customer.
So 95% of homeowners insuranceis sold through these local
(02:05):
agents.
There are actually more than400,000 retail insurance
agencies in the US.
That's four times, it's morethan four times the number of
bank branches and it's more thantwo times the number of fast
food restaurants.
Believe it or not.
That's frightening, isn't thatnuts?
(02:25):
And people actually go to theirbank.
Nobody goes to their insuranceagent ever really.
Like you can camp outside theoffice and the only person going
there is, like, the owner ofthe agency.
So 95% of it is sold throughthese agents and that creates
some issues.
The first is they're veryexpensive.
That branch network costs about20% of the premiums to maintain
(02:47):
for the insurance carriers, soit's a huge extra cost.
The second is customersactually don't really like it.
Like if you survey customerssay, hey, would you rather buy
from an agent or would yourather buy directly?
It's like 70% would prefer tobuy directly.
And if you say, are you willingto pay extra to have the local
agent, it's like almost none ofthe customers are.
(03:11):
And then, finally, by goingdirect to the consumer, we can
actually use marketing as amethod of risk selection and
we'll get more into this later.
But there's a huge variance inthe types of houses and how.
In homeowners insurance, mostof the events, most of the bad
things that cause a loss, it'sweather related.
Okay, all the houses are goingto get hit by the same weather,
(03:34):
but they're all going to responddifferently because the homes
are built in these idiosyncraticways, like your roof is like
different than my roof and mybathrooms are different than
your bathrooms and all of that.
So we can actually pick andchoose the customers that we
know are living in homes thatare more resilient to the
weather and we can specificallymarket to those customers.
(03:56):
So that's the business modelinnovation we're direct to
consumer.
It helps us with risk.
Speaker 1 (04:00):
Whereas the folks that are distributing through
agents can't do that becausethey take whoever the agent goes
and finds and the agent'sprobably not incented to do that
right.
Speaker 2 (04:07):
That's right.
And they have very crude waysof doing it right.
They'll tell the agent like hey, your portfolio looks like X,
like we'd like less of this andlet more of this.
But it's not very nuanced theway they can do it.
They don't actually havecontrol, they just sort of have
like influence, right.
And then the second thing we dodifferently is tech innovation,
which plays very well with thebusiness model innovation.
The technological innovation istwo parts.
(04:28):
First, we've built from scratchthe best, most cutting edge,
efficient insurance coreprocessing system.
It's proprietary to us.
Most of our competitors don'tcontrol their own core
processing systems.
They're outsourcing it to acompany like guidewirecreek
which for the most part, are notvery good software.
It suffers 30 or 40 years old.
(04:50):
For the most part it's stillon-premises software, like it's
really, really not good.
So that gives us a bigadvantage.
And the second part of the techinnovation is All the insurance
companies, us included, we allthese like smart actuaries
pricing it stuff like that.
Right, they're crunching thenumbers.
They're saying, hey, for homesthat look like this, we expect
(05:10):
the losses to be like that andthat's how we're gonna come up
with our pricing.
The issue is the way they getall the input data.
For that our legacy guys isAsian.
Well, that's silly, thatdoesn't make any sense, like the
agent has never been to thehouse, right?
The agent also isn't anarchitect, he doesn't know about
(05:32):
the shingles on your roof, he'san insurance agent.
And then, third, they actuallyaren't compensated for accuracy
of data.
And so you'll find is, theagents are constantly fudging
the data because that's theirjob.
They're like trying to help thecustomer by getting them a
lower price.
Now, there are lots of badthings that can happen when you
flush the data, and one of themis inaccurate pricing.
It's important to note thatthat's a problem at the point
(05:55):
like for that risk, right?
It's like, oh, if you pricethis as If the roof was really
good and the roof is actuallyreally bad, you're gonna be
underpriced for that risk.
It's also an even biggerproblem because it fundamentally
breaks their actual, realmodels, their pricing models,
because, if you think about it,they're feeding all of these
models and training them onbogus and potato Sure, so you
actually don't see thecorrelations that it actually
(06:18):
exists, and so it sounds like,then you must have a process to
collect all this data directlyfrom the source we do so we're
Manufacturing our own data andwhat we're doing is where
there's like like all you haveto do is Google your address and
you can see there's just liketons of unstructured data out
there about your home searchimages, there's MLS records,
there's government, there's taxrecords, building permits,
(06:40):
there's all this stuff.
No, it's all unstructured andthat's the problem with it.
So what we've been doing forthe last seven years is training
a set of models To take thatunstructured data and package it
into structured data and thentraining our pricing and
underwriting models on thatinput data.
So it's not always perfect,right, you don't always have
perfect information, but it'sway, way, way better than the
(07:03):
alternative, which is just likeasking some guy who actually has
a bunch of reasons where he'llbe inaccurate right so that.
Speaker 1 (07:10):
So that allows you to basically use this under
undercut price on the bestcustomers and then lose the
worst customers because they'reyou know, they're not as good as
the other the industry thinksthey are that's right.
Speaker 2 (07:21):
It's more accurate, it's more accurate pricing and
that ends up being a bigger dealin some places than others.
Basically, what you find is inareas where there's more weather
volatility, having accuratepricing makes a much bigger
difference.
So where I live in northernIllinois, we don't have that
much weather volatility, and ifyou know exactly the type of
(07:42):
shingles that's on my roof, it'shelpful you know, it doesn't
hurt.
Yeah, but if I lived inCharleston, south Carolina or
Tampa Bay?
Speaker 1 (07:52):
you probably only care whether somebody has a flat
roof or a slope roof, so thethree feet of snow doesn't
doesn't cause a leak.
Speaker 2 (07:58):
That's right, and the roof sheath is an interesting
one because there's more nuancesto that, like, no roof is truly
flat and the roof's all ofdifferent angles on the slopes.
You actually want to take thatinto consideration.
So there's a lot of nuance inreshape since you bring it up,
but it really can make a bigdifference in some of these
places.
And and this is where you seethe headlines one of the reasons
(08:19):
why you see the headlines ofInsurance company X is leaving
wide geography entirely.
Okay.
Well, that's because theyactually Don't know how to tell
the difference between the homesthat are going to be profitable
for them in the homes thataren't.
And we're able To do that, andit allows us to serve these
customers who need us the most,the folks who live in
(08:39):
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 (08:47):
Yeah, that's, that's fantastic.
Well, look before we, before wego into the next several layers
of can, I'd love to spend justa minute or two exploring your
background.
I mean, I know you got into thestarter startup world quite
early after you started yourprofessional career.
Wonder if you can just talkthrough kind of how you got into
the startup world and what wasit about startups that really
attracted you.
Speaker 2 (09:07):
So I was a tech guy first.
So like I was one of those kidsthat grew up Programming, you
know, and I was just really intomaking like these stupid little
apps and games and Stuff likethat.
You know, it's like going backto middle school now and Then I
was like, well, I'd actuallyreally like people to use the
(09:28):
software you know.
So that sort of got me moreinto the business part of it,
right?
And then I realized I wasactually really interested in
business.
I was really interested ineconomics.
I ended up going to school, youknow I'm majoring in computer
science and economics, which isa cool combination, and I've
really been doing FinTech stuffever since.
So my first gig out of collegewas like, like when I was in
(09:48):
college I did the same likeinvestment, banking internships
that everyone else does.
But my first gig out of collegeWas working at PCG.
I was mostly working withfinancial services companies
like mostly retail banking,credit cards, pnc, insurance.
I was like really interesting.
But I looked at myself aftertwo years and I was like, well,
gosh, sean, I always thought youwere gonna be like a tech guy.
(10:10):
Like what are you doing makingspreadsheets for banks all day?
Speaker 1 (10:13):
This is stupid, that's not what you're doing, so
I quit.
Hey, that is high-tech at somebanks.
Speaker 2 (10:18):
Come on, some of my spreadsheets were we're
legendary and a lot of acros inthese spreadsheets.
That's why I quit and I joineda VC firm and I did that for a
couple of years, like reallyearly 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 wasaiming to do when I was working
(10:38):
at VC I.
One of the things I learned islike, actually, at least in that
case, I had a lot more sparetime after being, you know,
working as a VC analyst versusFour days a week and travel at a
management consulting firm.
And so I actually started myfirst company in parallel with
that gig, and it was an onlineretailer's e-commerce company.
(11:01):
There was like a specificproduct that we were interested
in selling.
My co-founder and I in thatbusiness it sounds silly now we
were the largest Seller ofreplacement parts for satellite
radio systems, which at the time, was like this huge consumer
product.
Speaker 1 (11:20):
I, I still have one, and in all my cars.
Yeah, serious yeah it's.
Speaker 2 (11:25):
It's actually a really good product, you know,
and at the time I had done aconsulting case for a retailer
and I realized how much moneywas in these sort of spare parts
, because people are relativelyprice insensitive.
If you're like, oh, I have thisthing 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 Becausethe adapter, the power adapter
(11:47):
for my serious satellite radiostopped working, you, you didn't
care if you were paying 20bucks or 15 bucks to get the
replacement part.
And so you know that was thebusiness that we built.
It was really fun.
It was a bootstrap business.
We were on the Inc 500.
It was like cool, and so thatwas the beginning.
And then I did another business.
It was a payment processingcompany, similar.
I had had some consultingexperiences in that space.
(12:08):
I had consumed the product, thepayment processing product, at
my prior company, right, the oneI had started, though I see an
opportunity here to do it better.
The product that we launchedwas like basically Stripe, but
without a $20 million seed round.
Speaker 1 (12:23):
Our seed round was only like $2 million.
Speaker 2 (12:25):
Details, details.
So when we were out raising ourseries A, we got an acquisition
offer and we took it and Ispent I spent three years at the
Acquire, which was Groupon.
I built the Square competitorwithin Groupon.
I thought that was really aninteresting experience.
But ultimately my future wasgoing to do something
entrepreneurial again.
(12:45):
Right, Take a bigger swing.
I was a single.
I wanted to go and swing againand then that was sort of when I
started incubating Kin andthinking about what ultimately
became Kin and I sort of took ayear to explore ideas and do
some little independentconsulting gigs and do some
angel investing and stuff likethat, but ultimately kick the
(13:07):
tires on a bunch of things andthis was the opportunity that
excited me the most.
Speaker 1 (13:14):
So it seems pretty logical how you could go from
e-commerce to a Stripe-like typething.
I mean, I get that you spent ayear looking at different ideas,
but homeowners insurance ispretty out of left field given
what you had done so far.
What even got you to startthinking of that idea?
Speaker 2 (13:28):
Well, interestingly, actually, I had a consulting
case at one of the big insurancecompanies where I spent a lot
of time in insurance agents'offices.
It was like a go-to-market casefor this insurance company and
I just saw how wasteful thatagent experience was and I saw
how weird it was that the actualinsurance company would be like
(13:52):
one of the questions they askedus was like well, why do
customers come to us?
Like oh, that's kind of a weirdquestion for you to be asking
me, 24-year-old managementconsultant, like don't you know?
They're like no, no, actuallywe don't.
Because the customers go to theagents Just while these guys
weren't really doing fieldunderwriting, as they would say,
(14:14):
like they didn't really knowvery much about the risks, they
were just kind of going toZillow and copying some stuff
off of Zillow and that wasactually the data that was
getting fed into theseunderwriting algorithms.
And then you also noticed thatthose agents were really
high-margin business.
They were actually a muchhigher-margin business than the
(14:34):
carriers themselves.
Like the carrier was workingfor they were like happy if they
had a 5% net margin year, likethat was a good year for them.
And then if you crunch thenumbers on these agents, which
are actually sort of all small,below-scale businesses.
You're like whoa, these arelike 40% EBITDA margin.
Businesses Like this isactually, if you draw the whole
(14:56):
value chain out, the agents areextracting the majority of the
value.
Why is?
Speaker 1 (15:00):
that.
So a bit of an aside from Ken,but do you know why PE hasn't
tried to roll up all theseagencies?
Oh they have.
Oh, I got you.
Speaker 2 (15:07):
There's a huge amount of roll-up activity in PE and
insurance agencies, especiallyon the commercial side.
There are two private companiesthere's Accra, shure and Hub.
These are both like $20 billionmarket cap private companies.
Pe roll-ups Got you.
Speaker 1 (15:20):
But because there's so many, most of them are still
these little mom and pop.
Right, you got it.
Speaker 2 (15:24):
That's exactly right, especially on the personal line
side, where I think there'sbeen less consolidation activity
.
There's still quite a lot goingon in personal lines.
It's just so big right there'sso many.
There's like 400,000 of them.
This is a lot.
So I had some context for theproblem.
We actually drew the map of allof financial services.
(15:47):
I was like I'm looking forsomething that meets a few
criteria.
I want something where it'soverly intermediated.
Now, some things are reallycomplicated and you need a lot
of intermediaries.
A lot of products, especiallyconsumer financial products, are
not that complicated.
You don't need the intermediarythere.
It actually makes it worse.
Speaker 1 (16:06):
Well, if anyone can relate to the problem, I guess
it's the QED, with our CapitalOne heritage, which that was a
big part of the premise, tooright, why would you get a
credit card in a branch?
Why?
Speaker 2 (16:14):
would you get a credit card in a branch?
Absolutely yeah, very much so.
Capital One was a biginspiration to us.
It was a company that we was onour list of.
We could be like the CapitalOne for homeowners insurance.
That would be really cool.
Then we were also looking forsomething where there were new
data sources that could be usedfor pricing and underwriting.
(16:35):
We looked at everything.
There was one other idea thatrose to the top.
It was also an insurance.
It was small business insurance.
Ultimately, we decided thatthis was better because it has a
larger, more homogenous market.
Small business insurance hasmany of the same problems that
(16:57):
homeowners insurance has.
The reason why we didn't wantto go after that is because
small business is really a bunchof different niches.
You actually do need adifferent product, a different
go-to-market to get the yogastudio versus the coffee shop
versus the car dealer.
We just loved the fact thathomeowners insurance was so big.
It was so homogenous.
It met our criteria,specifically around new data
(17:21):
sources being available, aroundit being overly intermediated.
Speaker 1 (17:26):
That makes a lot of sense.
When you decided to start thesatellite radio parts company,
did you think about thisinsurance idea then?
Or more just, you had done thecase.
You weren't really connectingthe dots until you went back and
systematically looked at itlater.
Speaker 2 (17:42):
I always have this entrepreneurial.
I'm always thinking aboutthings I can do I'd actually
thought about when I was at BCGdoing some of these cases.
I thought about doing a roll-upin the space.
Then, after doing my paymentscompany, I realized how
important product-led growthcould be in some of these things
(18:03):
, even in, yeah, you thinkpayments is a commodity it kind
of is.
But also there really was areal appetite from the customer
to have a more streamlinedexperience, to not have to
answer all the dumb questions,to not have a seven-day
underwriting period.
There's all these sort of nutsand bolts things about the
(18:23):
product.
That was really inconvenient.
That led me to think of more ofa product angle for insurance.
I was like, yes, I do want tooccupy the part of the value
chain that's so inefficient,that's so distributed, with
400,000 people doing it, wherethey have such high margin that
they don't really deserve maybefor the work that they're doing.
(18:44):
But also I want to do that byhaving a really unique value
proposition to the customer.
Speaker 1 (18:50):
You obviously had a big industry.
You were looking for a startup.
You had seen the problemfirsthand.
Were there any major obstacles?
That you saw as you started tothink about this and like, oh,
maybe I shouldn't go do this,that you had to get over to take
the leap.
Speaker 2 (19:05):
We knew the two biggest question marks were do
customers want this?
Can we manufacture itefficiently?
We had to answer the firstquestion, which was what do
customers want this?
What we did is we actuallybought a little insurance agency
and we wrapped it in a littlebit of UX.
It wasn't that expensive, itwas a few hundred thousand
(19:28):
dollars.
We wrapped it in a little bitof UX and we started to run
marketing experiments and reallygo, okay, like, after doing
this for six months, the uniteconomics well, they're not
great.
But like, if I increaseconversion rate here and I
increase conversion rate hereand I reduce the churn there,
(19:49):
then they will be great.
Right, they'll go from beingsort of okay to being great.
It's like okay, cool.
So we did that.
Then the next thing was okay,well, how do I get control of
the whole stack?
Right, because as an agent, wedidn't have any differentiation,
differentiated valueproposition.
That was one reason why yourconversion rate was low.
Okay, so how do I control thewhole stacks?
Like a manufacturer, a productthat is differentiated?
(20:14):
Now I come from the paymentsworld and I've done some angel
investing and lending and I waslike, oh, we got a fight.
We have to find a rents acharter for insurance, like and
then we realized, oh, thatdoesn't actually exist.
Like there's this, there's thisother thing is sort of sort of
similar to a rented chartercalled the front-end carrier.
You sort of rent out theirinsurance licenses.
It's not as well established anecosystem as the rent a charter
(20:38):
, but that that was sort of ournext step.
We're likely we know we're not.
It's a very difficult industryto approach.
It's no accident that it'sbasically an oligopoly, right
Like there is designed to keeppeople out.
So we know we're gonna need toclimb up the stack.
You know, one wrong at a time.
So okay, next step is let's setup something where we're an MGA
(20:59):
which is sort of like a virtualinsurance company gave us all
the control over the productthat we wanted.
You know there were somedownsides to being an MGA.
It actually didn't give us allthe control over the product we
wanted.
It created a real existentialrisk for the business Because
you had this one counterpartythat was like so important to
you.
So we knew there were somedownsides, but we were like,
(21:20):
well, we don't have the money tostart insurance companies.
Speaker 1 (21:23):
So are you an insurance company now?
Are you still kind of in theprocess of working up that
regulatory stack?
Speaker 2 (21:28):
We are.
I'll tell.
I'll talk to you about that ina second.
So that was the next step.
So, basically, the first yearwe were running these marketing
experiments.
The second year we were like,okay, cool, now we have this
business relationship as an MGAwhere we can run everything
through our software.
Okay, but we have to hustle tolike write the core processing
system, which is not an easytask to write a good core
processing system for insurance.
(21:49):
Now We've rewritten that thinglike five times since, right,
because it was very much likehey, here's the MVP.
Like we need to be able toissue a policy, like I need to
be able to take the custom rain,get the underwriting info
manufacturer the data, print aPDF, basically Obviously.
Now, six years later, thesystem does a lot of stuff in
addition to that and does itreally really well.
(22:10):
But that was sort of the nextstep.
Then, two years later, we'relike, okay, well, we're like
kind of on the verge of being ascale.
This is 2019.
We're kind of on the verge ofbeing a skilled business.
We have unit economics that areworking.
We're really worried about thisrelationship with this, the sort
of single threaded relationshipthat creates an existential
(22:32):
risk for the business.
The MGA relationship isn'tgiving us all the degrees of
freedom that we want.
Let's go for the big time.
Let's start our own insurancecompany, and we started.
We now manage two insurancecompanies.
The first one we started was in2019, so it took a year to get
regulatory approval for that andI had to go raise a bunch more
(22:53):
money because we need tocapitalize this thing.
Now you use reinsurance andstuff To reduce the amount of
capital that you need, but westill needed 30 million bucks
sitting in a bank account, right.
So at the time we'd only raiseda Series A right, so we only
had 12 million bucks.
Then we went spent a lot ofthat on developers and stuff.
(23:13):
So we had to raise another round.
We had to wait a year forregulatory approval for this.
In the meantime, ourrelationship with the sort of
MGA sponsor got worse becausethey were like wait, you're
leaving, like what the heck this?
Why should we care about you?
Now that you're leaving, it'slike well, right, we gave you
warrants like you should careabout it.
It got a little bit tense there, but we eventually did get in.
(23:37):
It's.
It's interesting.
We actually have a novelfinancial structure when kin
actually isn't an insurancecompany.
What we did is we set up thesethings called reciprocal
exchanges.
Those are insurance companies.
They're fully licensed, theyhave capital, they have credit
ratings, they they're regulatedjust like any other insurance
(23:59):
company, but they're actuallyowned by our policyholders Hmm,
okay, and we manage them inexchange for a fee.
So our business actually lookssort of like an MGA business
where we're just getting thisfee, this recurring fee, to
manage it.
Or it looks kind of like a, likean asset manager almost right,
like you could think about thislike we're the GP and Our
(24:22):
customers are the LP in theseinsurance exchanges.
No, it's a good structure forus Because it gives us a stable
recurring revenue stream.
Our profit center is what ourlegacy competitors cost centers
are right, the agents and theoutside software right, so we're
basically keeping that margin.
(24:43):
They think about what ourlegacy competitors.
Their profit center isUnderwriting income and
investment income, and and we'reactually leaving that in these
customer owned exchanges, right,so the customers are literally
getting a better deal and Ithink it's all ultimately makes
it very difficult for ourcompetitors to compete with us,
right, because, like what'shistorically been their profit
(25:05):
center, I'm just giving back tothe customer.
Speaker 1 (25:07):
Yeah, it's funny, my wife's whole family is
ex-military and so I know she'salways tickled every year when
we get our USA rebate check.
Yes, it's similar, similar kindof concept, right, very similar
so.
Speaker 2 (25:17):
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 MidAtlantic.
The management company of Erieis publicly traded, so it's sort
of an interesting company forus to go and look at.
But yeah, it's very similar towhat you just described with USA
.
We don't give the dividendcheck, we just leave it in there
(25:39):
and give the customer a lowerprice.
But you can do either.
Speaker 1 (25:43):
So let me, let me dive into this.
I know you know, back when youwere giving the the intro to kin
, you talked about you know,look, you're innovating across a
number of vectors.
I know innovation and insurancehas been notoriously slow and
I'm sure there's tons of factorsbehind that, one of which being
Correct me from wrong stateregulators have to approve all
of your pricing schemes andyou've got to show you know a
(26:04):
bunch of data behind it.
I'm guessing state insuranceregulators aren't really at the
cutting edge of large languagemodels and all of the tools that
you use to convert yourunstructured data to structured
data, etc.
How have you thought about thatand how have you overcome that
obstacle?
And and how big of an obstacleis it from?
You know, say, a perfect worldwhere you could kind of go do
whatever testing you wanted todo and do whatever you wanted.
(26:25):
We've found it to be manageable.
Speaker 2 (26:28):
It is true that In the majority of situations, in
the majority of lines ofinsurance, you do have to have
your rates and forums Upproof bythe regulator.
An example of that would be doI charge X factor if your roof
(26:48):
is shaped like a hip shaped roof, right where all the sides are
angled, and I charge Y factor ifyour roof is only angled on two
sides?
The roof that's angled on allsides is better.
Right, because the wind justsort of flows over it.
Better, that's what theyregulate.
What they don't really regulateis how do I know that the roof
(27:09):
is hip or gable right?
And so that's where a lot ofour big data innovation has come
is on like, hey, let's makesure we have really accurate
input data.
Now, dirty secret.
If the regulators did regulatethat input data, they would find
that the majority of the inputdata being used by the legacy
industry is just wrong.
Right, it's like literallydefault values in many cases,
(27:32):
because the agent is lazy andthey just sort of click through
and they just make assumptions.
Right, because there's notalways a good way for them to
know the exact angles of everypart of the roof, just to use an
example.
So we found that to be a reallygood nexus for innovation is
sort of on the input data.
The other thing that is nice is,as a direct to consumer
(27:53):
business, the state regulatorsdon't pay as much attention to
who are you marketing to.
So I can have an interestingsituation where I am really good
at knowing if a customer hasthis is an example.
I'm really good at knowing if acustomer has a metal roof.
Metal roofs are great, by theway.
If you can afford a metal roof,you should get a metal roof.
(28:14):
They're great.
Then I can offer that customeran actuarially justified,
regulatory approved lower ratebecause they have a metal roof
that's really resilient to hailand storms and everything like
that.
Then I can create a marketingcampaign that specifically
markets to the customers withthe metal roofs, right.
So we have this sort of closedloop system.
(28:36):
We also have customers wherewe're like oh, I can tell from
aerial imagery, this customerdoesn't have a very good roof,
right, the shingles are curlingat the edges, there's striping
on them, like.
We have image recognitionalgorithms that can tell us
stuff like that.
All right, well, we're notgoing to market to that customer
.
So all of our competitors arein the bad situation of like
(28:59):
they obviously also don't wantthose roofs.
They don't have a good way ofknowing right, because a lot of
times they don't have the sameimage recognition capabilities
that we have.
They don't really know whichroofs are in one camp versus the
other.
But, even worse, they're like ahockey goalie, because the
agents are just flinging all thehouses at them and they have to
be defensive.
(29:20):
And if they let one through bymistake, or because they didn't
validate the data, or becausethe agent was good at finagling
their system, well now they'rein trouble.
Right now they have somethingin their portfolio that doesn't
really blog in their portfolio,so the marketing lever has
actually turned out to be reallyimportant to us.
Speaker 1 (29:37):
In terms of this data advantage and analytics
advantage.
I mean, it's super innovative.
What you guys have done Are allof the advances and LLMs and
open AI and all of that type ofthing.
Is that helpful for you becauseit allows you to advance your
capabilities, or is it hurtbecause it lowers the bar
dramatically for other people tokind of come in in ways that
you really sort of pioneered?
(29:58):
Or is it just a red herring anddoesn't really matter a whole
lot to what you're doing?
Speaker 2 (30:03):
I think it's more of like LLMs specifically, are a
little bit more of a red herring.
Now you do get a surprisinglydecent answer.
I'll just use an example.
If you upload a picture of roofto to chat gpt and you ask it
like, hey, describe the roof,for me it's like a 50% answer,
(30:24):
which is surprising, right,because you know that it wasn't
trained on images or roofs.
Or you can upload like abuilding permit.
Now, building permits are reallymessy data because they're
different literally in everytown.
You can upload that.
Similarly, it'll give you likea 50% answer, but we really need
more like a 95% answer, and sowhat we've been doing is
(30:48):
training specific models onspecific input data, and LLMs
are also really expensive to runright now.
So the specific models, becausethey're trained on that, have a
much higher degree of accuracyand for us that's important,
right like we can't have 50%accuracy and they're a lot
(31:08):
cheaper to run.
So, generally speaking, thefact that the tools have all
gotten a lot better is great forus and, I think, for anyone
who's sort of trying to apply itin this way.
The like APIs that areavailable right now I think are
too general for this and we'vereally been training our own
models.
Speaker 1 (31:28):
Yeah, well, hey, let me switch gears to another angle
.
I mean, I know that big part ofyour motivation is using
technology to really helpconsumers and you're in a pretty
unique spot to do that, and soI'd love your take on some of
the places you're doing business.
You know Florida, texas,hurricane zones, etc.
You know customers obviouslyhave the risk every day of a
(31:49):
pretty severe incident, but theyalso have issues hard to get
insurance, how to get insured ata reasonable price.
You know get dropped all thetime, etc.
And you've kind of found aninteresting way.
So I wonder if you can talkabout the customer angle of kind
of what you guys are doing andhow that's really pretty unique
in this industry.
Speaker 2 (32:07):
Yeah.
So it was in the early dayswhere now I sort of knew in the
back of my head like it's reallyhard to win in financial
services if you go up the gut ofthe customers that, like
everybody knows, are good.
It's just hard to dislodge themright.
If the legacy guys know thatthis is a really good customer,
it's one they all want, you havea hard time attracting that
(32:29):
customer, especially for stickyproducts.
So we knew we needed to findlike an underserved niche, right
where it was maybe not as wellunderstood by the legacy
competitors or where we had aunique advantage.
So we were sort of looking forthat.
And then the early days ofmarketing.
We really just realized likeyou talked to somebody about
this in Wisconsin, where I grewup, and they'd be like oh yeah,
(32:51):
that's kind of neat.
You talk to somebody about thisin Florida or Texas or
California.
The lead for the oh, that'sreally interesting.
Like actually my homeowner'sinsurance it just doubled in
price or I only have threechoices of insurance companies,
or it's really hard I have towork with these sort of weird
(33:12):
providers.
They have all this like allthis paperwork.
It's really annoying right.
And so there was like a realengagement there and we're like,
oh, that's kind of interesting.
Like you know, maybe there is aregional element to this.
And then we looked at it moreand we realized that the things
that we were trying to do so youdo have a difference right
there.
There is less availability ofinsurance and it is more
(33:33):
expensive in areas where there'smore extreme weather and More
and more places every day arebeing exposed to extreme weather
, and that's driven by globalwarming.
Okay, that sounds like aninteresting trend to be part of.
You know, helping thesecustomers who are underserved
there's more and more of themevery day.
That market is growing.
And then we thought about ittomorrow, like, oh, actually,
(33:56):
this really maps very well tothe things that we're good at.
Why, okay, well, in the placeswhere the insurance is more
expensive, the agents, thedistributors, still get paid the
same percentage fee, so they'remaking a ton of money.
Right, it's like yourhomeowners insurance companies
in Florida might not be doingthat well.
Homeowners agents in Floridaare doing incredibly well
(34:19):
Because they do the same amountof work is an agent in Chicago
and they get paid four times asmuch.
Okay, well, that's interestingbecause we occupy that part of
the value chain.
Like I want to go there wherethe the economics are best for
that part of there.
And then we looked at the stuffwe were doing on the data side.
We're like, well, if I knowExactly the, the type of shingle
(34:40):
that's on this roof, that'sgonna make a hundred dollar
price difference in Chicago andit's gonna make a thousand
dollar price difference in NewOrleans.
Okay, well, obviously thisskill that we have of
understanding the data and thephysical traits is way more
useful in New Orleans that is inChicago.
So that was really thetriangulation of data that led
(35:03):
us into this.
So we're we have a prettycoastal footprint.
We're in South Carolina,mississippi, alabama, louisiana,
florida, texas and Arizona andArizona is the outlier there and
the the next few states thatwe're launching.
We're gonna be launchingAnother batch of states this
(35:23):
year, in 2024.
They're all sort of coastalstates.
Speaker 1 (35:27):
When you guys hit Massachusetts, I'll be very
excited to try to be a customerwith our vacation place on the
Cape.
Speaker 2 (35:34):
Yeah, no, getting getting insurance on the Cape is
not easy, right, totally.
There's a lot of places likethat, you know Coastal
Massachusetts, coastal RhodeIsland, coastal New Jersey.
They're all pretty hard placesto get insurance.
Speaker 1 (35:46):
It's pretty expensive , yeah we, we, we definitely
work with the agent and she hasa new company for us every year,
and so I'm very familiar withthe problem you're you're
talking about.
Well, look, we only have about10 minutes left.
You know, we've kind of skippedhalf our script here just
because the the business modelof what you're doing is so, so
interesting.
But I'd love to ask just acouple questions, kind of about
(36:07):
the inside of the company andkind of management as we close.
I mean, you've now grown toroughly 500 companies.
It's your third startup.
I mean, how do you think aboutcorporate culture?
You know, as you had a couplestartups that wound up selling
pretty early, you've nowprobably learned a ton from that
building your first startupwhen you were small.
I'm sure you've learned a tonmore as this one has grown and
(36:27):
gotten to be large.
How do you think about companyculture and what are some of the
, the lessons that a listenercould take away?
Speaker 2 (36:34):
culturally.
What's always been reallyimportant to us again is To make
sure the people who are closestto the data are making the
decisions.
But I feel like a lot of thetimes you have a lot of waste
where it's like, especially inbigger companies, people are
like oh, let me ask my bossabout that.
Okay, well, you know your bossis probably not as knowledgeable
(36:54):
about this problem as you are,so that's always been a really
key value for us.
Another one is We've always hadthis idea of running through
walls and this is certainly true.
Like to do this is really hard.
We do accomplish the impossibleevery day, and then we had to
amend that this was aninteresting one.
As we got bigger, we had tochange that stated core value to
(37:16):
be run through walls together,because you'd have these guys
who would just like be like I'mgonna solve this problem and
they go and they do it and haveall of these unintended
consequences.
You're like well, you did solvethat problem.
But Second, with these othertwo problems you cost Just
because the problems are solvingare more complicated now that
we're bigger.
So you know we've had to evolveover time.
(37:39):
We try to be a company that'slike really low ego and really
focused on the numbers.
And it's that can be hard as anentrepreneur, especially
somebody who is pursuing an ideathat is at least somewhat
visionary, because on the onehand, 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 dotomorrow, like do this one small
(38:02):
thing tomorrow and then we'lldo another small thing tomorrow
and then we'll do another smallthing the day after, and that's
what adds up here.
And I feel like it's certainlyin our space.
You know, and sure, tech wasthis space with a lot of big
ideas but a lot of companiesthat did stupid things, like
trying to scale with uniteconomics that weren't positive.
Okay, like let's not do that.
You know, let's try to besomewhat practical in our
(38:25):
approach here.
So that's that's always been abig part of our DNA is just
being practical.
Speaker 1 (38:28):
Yeah, it makes sense.
It's a great example of thelesson of the run through walls
right, where one thing workswhen you're 10 or 20 people and
the core kernel still isimportant when you get bigger.
But needing to modify it, Imean you're kind of be close to
the data really resonates reallyto Capital One, I mean I think
we were also had a very similarkind of approach.
I mean I think I was managing ahundred million dollar
(38:48):
marketing budget at age 23 orsomething which I look back and
I'm like, wow, they were stupidto let me do that.
But similar concept right, likethe people doing the analytics,
making decisions.
And I think there's also a goodexample when the company scales
creates a challenge, becausebottoms-up companies can just
kind of turn into a bit of a youknow gummed up mess To sort of
(39:09):
make decisions as you get bigger, and so how to how to figure
that out will be be an importantone, I think so.
So here's another, another angle.
I know when, when COVID hit youand everybody else kind of went
remote Big debate now of somecompanies have gone back to
almost all in person a bunch ofhybrids, a bunch of people love
remote.
I mean you're more on theremote side, if I, if I
(39:29):
understand correctly.
How have you thought about that?
And is it, you know, just kindof a no-brainer good for you
guys?
Or are there kind of trade-offsthat you wish you had more in
person?
I mean, how have you thoughtabout that?
Speaker 2 (39:39):
that trade-off there are definitely trade-offs and we
were very much an officecompany before this.
You know, we had an office inIn Tampa area and we had office
in Chicago and everyone was inone of the two offices every day
and we loved it.
Like we had so much funtogether 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 rightlike we had to go home and we,
(40:05):
just because of how we grew,like our company, tripled in
size over that time period andand so by the time we were able
to gather again we had peopleall 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 neitherChicago nor Tampa are like big
(40:25):
talent centers, for us it was anet positive trade to be able to
recruit everywhere.
I think maybe if you're inSilicon Valley that's not as
true you know, because they havesuch a such a concentration of
talent there, but for us it wasreally great to be able to open
a recruiting aperture.
The Coventine was hard on thecultural side because we could
(40:46):
never see each other and now wejust we just get together a lot,
you know, and we get togetheron the individual level, we get
together on the group level, weget together on the company
level.
We've offices where it's morelike it's a small office but
there's space we can flex intoand that's been really nice
because we can get a largergroup of people together.
And so you know we're committedto it.
(41:08):
I think there might likethere's another version of the
world.
We're like we never went Remotebecause there are costs to it.
Right, it is harder tocoordinate, it's harder to build
culture, but there's no puttingthe genie back in the bottle
for us.
You know that we just have toomany people in too many
different places to say, hey, goback to the office.
It's, half of the companydoesn't live anywhere near our
(41:29):
offices.
Speaker 1 (41:30):
Yeah.
So I spent a fair amount oftime internationally and it's
always a bit of a debate of nowthat you can hire remote people,
should you go with a globaltalent base or what are the
benefits of having everyonetogether, and you know, everyone
kind of comes up with differentconclusions.
It's fascinating how how broadit is and it sort of makes sense
.
Over time things would shuffleinto multiple options and
employees Over time canself-select into the environment
(41:50):
.
That works and you know, itseems pretty clear you can be
successful.
So look as we close and Ireally appreciate the time
there's probably could couldeasily go another another hour
here.
You know one thing I seesaleboats right behind you and
the piece of art.
I know you've got a huge hobbyfor fixing old sailboats.
How did that come to be?
Speaker 2 (42:08):
I don't know, that's a good question.
That was.
I grew up sailing and then Ididn't do it for a long time and
then In COVID I sort of pickedit back up again because it was
one of the things you could do.
So I got one sailboat and Isailed that it.
First I was just like I want tosail, I'm gonna like go down to
the beach and rent a sailboat,but of course that wasn't open
(42:29):
because COVID was shot.
So, okay, now I have to buy myown sailboat.
And then I just got into it andI bought another one and I
don't know, it's a fun thing todo for me.
I like being out in nature,that's awesome.
Speaker 1 (42:41):
Do you ever do any of the the long haul like Mackinac
races?
Had a good buddy of mine inhigh school that does all these
Port Huron to Mac, Chicago toMac.
But sailing on Lake Michiganmust be pretty fantastic.
Yeah, like Michigan's prettynice sailing.
Speaker 2 (42:54):
It's very close, you know, in Chicago You're right
here.
Speaker 1 (42:56):
Well, look, I really appreciate the time today we try
to close with with the samequestion for everyone.
Hopefully we have a number ofaspiring entrepreneurs listening
to.
These love to know.
You know, if you were to giveone tip to an aspiring
entrepreneur given your you knowon your on your third radio
right now, what would that befigure?
Speaker 2 (43:13):
out the unit economics first.
It's the thing like just somuch time is wasted, so much
money is wasted Scalingbusinesses that don't have
positive unit economics andnever will like don't Really
have so many years on thisplanet.
You know, let's find somethingit's sustainable and then figure
out how to scale it.
No, that's awesome.
Speaker 1 (43:33):
Well, that was a lesson that the world kind of
forgotten 2020 and 2021 but Ithink it's quickly trying to
remember again and very muchagree with that.
So so, sean, thanks so much forjoining today.
It's been a great conversation.
We're huge fans of kin and lovethe innovation that that you're
doing here, so reallyappreciate you spending the time
.
Thanks for having me and allyou listeners.
(43:53):
Till next time, take care, andthanks for listening.
This has been the FinTechthought leaders podcast your
window into the world of venturecapital and financial services
with today's digital disruptors.
Qed is proud to provide thebest FinTech advice you can get.
To learn more or to read thefull show notes from today's
(44:16):
episode, check out QED investorscomm and be sure to also follow
QED on Twitter and LinkedIn atQED investors.
Thanks for listening you.
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