# How to Start a Startup: Finding Product-Market Fit 

**Author:** Peter Reinhardt
**Type:** Video
**URL:** https://www.ycombinator.com/library/86-how-to-start-a-startup-finding-product-market-fit
**YouTube ID:** _6pl5GG8RQ4

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Peter Reinhardt, Co-founder and CEO of Segment (YC S11), draws from his experience to shed some light on finding
product-market fit and how much it is tied to solving real problems.

## Transcript

Today we have Peter Reinhardt, he's
the CEO of Segment, went through YC when? >> 2011. >> 2011, and
now Segment is doing extremely well. Peter is going to talk about product
market fit, sort of this magic concept or word people use. I don't think they really understand it,
certainly, the first job to get right in any startup.

Peter, thank you very much for
coming to talk to us, and I look forward to hearing this. >> My pleasure, all right, so I'm going to talk about finding
product market fit today. Segment is a B2B company, so that's probably why none
of you have ever heard of us. But just to give you a little sense,
we're about 150 people, and grew from about 4 people,
3 and a half years ago. So growing quickly, and hopefully I can shed some light on
finding product market fit for you. I thought I'd just start by sort of
looking back to Alan K's lecture last week. I think he had some pretty
incredible advice around how to do

really amazing research by sort
of looking into the future and imagining what that future might be like. And in particular, he had this diagram
of sort of exploring the pink plane, and sort of testing out
different ideas there. And then at some point, having a
breakthrough and realizing that, actually, there's a blue plane, right, and you can
sort of go explore an entirely new space. And that's a key breakthrough for sort of understanding how you
can really invent that future. And in particular, he had this idea
of sort of going into the future and imaging yourself there. So he had this Wayne Gretzky idea of
skating to where the puck should be or

will be, and sort of going there,
grabbing that future, and then pulling it back to the present. And the way that he talks about doing
this is, I think, amazing for research. He talks about basically going and
sort of imagining that future world. But I'd actually wager that not a single successful company has
actually been founded by doing that. I think that's, again, awesome for
research, but when it comes to founding companies, it actually has nothing to do
with sort of imagining your vision for the future and what that future could be. It's not about sort of how
you wish the world was, but

it's actually about what customers want. And I think a lot of companies
get marketed post facto, of hey, we have this great idea, and
here's how we deductively logic out what customers were going to want,
and then we built the thing. But in reality, that's not how it
works at all, right, in reality, it's a very inductive process. You're going back and
forth with customers, and you eventually find something that works. And so I wanted to talk a little
bit about that process today, and just how critical it is, and
actually, how difficult it is, too. So one of the most common mistakes that
startups make is to build something that

no one wants, right, or
solve problems that no one has. And so that Wayne Gretzky method for
sort of going into the future and imagining what that future might look
like, again, is a good idea for research. It might even be a good idea for venture capitalists to sort of imagine
what the future is like and sort of put startups into different buckets of what
building that future might look like. But actually, I think it's a really
bad strategy for founders. And the reason is that most founders can
actually pretty easily build something that looks like it's from the future,
or is from the future, to some extent.

It's easy to solve the technology problem
there, but it's actually really difficult. That's not really what you need to do,
right, you don't need to build something
that is your vision of the future. You actually need to solve a specific
problem that customers have today, here, now, and
most startups actually fail to do that. Most startups actually build something
that looks vaguely futuristic, but is not, in fact,
a real problem that people have today. And it will kill the company every time,
right, the market always wins, so we're going to
dig into that a little bit more.

And this sort of problem of finding
product market fit is that 80% of all founders fail to
find product market fit. So four out of five attempts to found a
company just fail at the sort of earliest stage of even finding a problem
that you can solve in a unique way. And the last one out of five are going to
sort of struggle through the remainder of actually building that company out
from there, but that's just the stats. I'll share our own story of
finding product market fit. But the short version is that when
you're not finding product market fit,

it feels like sort of
a bottomless emotional free fall. Not to get too dark, but
when you're failing to find it, it sort of becomes a strange obsession, and
it could really make you sick, actually. To get, maybe, too personal for
a second, in 2012, in the midst of our search for
product market fit, I lost ten pounds in three weeks, and then went to
the hospital twice for panic attacks. I haven't had a panic attack before or
since then, and I think it's not just me, actually.

I think Sam, I don't know if I
should show this or not, but when he was founding Loot, I think he
basically forgot to eat properly and ended up with scurvy,
if I remember correctly. Sam is back there somewhere. So basically, be warned, finding product
market fit is, very emotionally, a grind. And the reason it is is that you really,
really desperately want to find that fit, right, you really want to figure
out what is going to work. What is something that customers want? We're all wired to want to succeed and
help people like that.

And so you're so intent on finding this
thing that you sort of convince yourself into seeing mirages of it. And basically, you convince yourself,
this is the thing, we're almost there, it's like this is something
that people really want. And so this is a picture of myself with
my three co-founders, Elliot, Calvin and Ian, in the summer of 2012, I think. And this is in the midst of
sort of our deepest false hopes that we had found something. So I'll dive into that story, and obviously, I'm here today
because it worked out. I wouldn't be speaking if we had
just disappeared into the darkness. But yeah, today we're about 150 people,
and growing rapidly, yeah?

>> How can you make sure you're
finding a big market, and not just a small niche [INAUDIBLE]? >> Yeah, that's a good question, so
[COUGH] I think PD's advice on this is good and valid, which is that it's
actually harder to find the first problem, and solve any problem well,
than it is to find a route out. So finding a route out from some initial
foothold into a broad market is actually not that hard. It sounds hard, but once you find any
foothold, you can pretty much find something, and I can cover that
a little bit in a bit, but yeah.

>> Is that because you have more
leverage with the customer? >> Yeah, it turns out that once you
solve a problem for a customer, they'll keep bringing you more problems. All right, the most efficient thing for
them to do is just say, well, I have this other problem that's also
adjacent to it, can you solve that? And you're like, sure, and you solve that, and you just keep solving
adjacent problems. So once you find one thing, it's actually
almost trivial to find the next thing, and the next thing, and the next thing,
especially in selling to other businesses, where you have someone who
can just tell you more stuff. Much harder in consumer, I'm the wrong guy to talk to you
about consumer stuff, I don't get it.

Cool, but back then,
we were just four dudes in an apartment, we were failing to find
product market fit. We were convincing ourselves that maybe we
had it, or we were on the cusp of it, and we were writing a lot of code. I saw someone back there who
was writing code earlier. We were writing a lot of code,
and we had no customers. That's the wrong order of operations, and
so we were tricking ourselves into this. In reality,
the good thing that was going on here, the reality was that we were
a bunch of cockroaches. And the reason is that
it's very important,

pre-product market fit, to basically
save as much cash as possible, spend as little as possible and
extend your runway as long as possible. So a lot of founders make a mistake of
spending a bunch on sales, or marketing, or other things as soon
as they have an idea. But in reality, until you find product
market fit, until you find something that people want, you shouldn't
be spending any time on that. You should just be spending time
talking with customers and iterating. And you really want to constrain your
runway, or constrain your burn so that you can have a really long runway.

So that's what we did,
we kept our burn really low. I think we paid ourselves like, the minimum allowable amount by law,
which was 20 or 30k a year. We lived in our apartment/office and
like the company paid for part of it as an office,
as it was the minimal thing. And we stretched out our runway
through as many ideas as we could. And so to sort of make this process a
little bit more explicit, step one is that you build, launch, and sort of
iterate on several different ideas. And this is where it's really
important to be a cockroach, where you're sort of conserving
everything you have.

Then suddenly something magical happens,
we'll talk about that. And this is product/market fit and
it really does feel magical. Hopefully, I can illustrate to you and
help you feel that. And then,
three product/market fit, sort of, suddenly turns you into
a unicorn/cockroach. And rather than just surviving, after product/market fit,
everything gets suddenly easier, right? You can, not easy but easier. Customer show up and buy your thing,
people want to join your team.

You're still pushing
the boulder uphill if you will, but you're not totally
constrained on progress, right? You can actually feel forward momentum for
the first time, it's not like one step forward at the product idea, full
step back when a customer doesn't care, you actually can keep iterating on
a product that's starting to work. So, let's take that middle portion and
deconstruct it a little bit. First we are going to deconstruct
what we call category leaders. These are really large companies
that have been very successful. Then we'll get to the heart of
why product market fit is so

important at the very beginning. And we'll talk about
what bad fit feels like. Bad product market fit. And then we'll talk about what good
product market fit feels like. And we'll try to do that through
stories because the goal here is to for you to walk away and be able to more easily identify
which is which, with your own ideas. So category leaders. The reason we care about category
leaders is that they're much, much larger than basically the rest
of the companies out there. So I'll give you an example. Amazon and Facebook are sort of consumer
companies that you might be familiar with.

Salesforce is a company that sells
to other businesses that provide sales software basically. And the reason that we're interested
in them is that they're huge, right. They're often ten or a thousand times larger than
the next competitor in the space. So, Salesforce for example you've
probably heard of SugarCRM and Zoho, are probably companies
you've never heard of. They all do roughly the same thing. And so, we want to dig into how
can you build a category leader, what's the layer underneath that.

And basically what it comes down to is
building a platform, where it's not just a product that you're selling, but
where the data inside of your product is actually useful for other businesses
to build their business on top of yours. And that's what Salesforce has done,
that's what Amazon has done, that's what Facebook has done. If you look at Salesforce, they have
something called the appexchange, and the appexchange sort of reveals all
of the data inside of Salesforce and allows other companies to build marketing
and sales products on top of that. Similarly, Amazon has
the reseller program, right?

Where now there's ton of other businesses,
built on top of Amazon. And so, the key here is having some sort
of platform, eventually, when you get the scale having some sort of platform
that other companies are building on. Selling, and by nature that pulls
people into your ecosystem. But, how do you build
a platform like that? I know Peter Thiel may not be
the most popular right now, but his opinion is to build a sustainable and
compelling platform. You really need to get to
100 million in revenue. And the reason is just that you need to
be at a scale, where someone actually can

capture a couple percentage
points of your customer base and build a real business themselves. So if they can capture a couple
percentage points, say, 2 or 3 million in revenue,
that's a real business. You can build something on top of that. So the sequence I weigh here,
you want to build a category leader, to do that you need a platform, to do
that you need a 100 million in revenue. So let's keep digging. 100 million in revenue is tough, right? So, let's keep digging down. I think you met Jason Lumpkin
a couple of weeks ago, am I right? Maybe he is coming soon. He's written some amazing stuff about
building software as a service businesses,

would highly recommend reading
all of his answers on Quora. And he basically breaks down the path
to 100 million in revenue in sort of three different steps. He says zero to one million is
impossible $1-10M is improbable and $10-100M is inevitable. And he says the $10-100M is inevitable, because at that point there is so
much momentum. You have customers out there who
are singing your praises, they're buying, their companies are going to buy more. And it might take you a while, but eventually you're going to
get from $10-100M.

One to ten million I think is
basically always a brutal grind for the founders, and the reason is that
you're running a real business. You have real customers. You have some scale. Your customers have reasonable
demands about sort of how, the quality of service that
they expect from the product. But the problem is that
pre-ten million in revenue, you have a really tough time attracting
a world class management team. because you can't hire
a great exec team yet because the large enough
market isn't there. And so, what you have is basically all the
early crew sort of like holding the ship together and trying to make
sure that it's going to work.

So, that's a very tough period, I think,
for most founders to go through. And then, zero to one million
isn't possible because this is finding market product fit and 80% or
more of people fail at that first step. And so the question is, how can you become
not and how can you actually make it through that impossible section and
become one of the ones that succeeds? And one of the things that people
talk about as investors or founders is how important it
is to learn from failure. And I recently read a pretty good study
talking about the stats of, sort of,

the actual analysis of failure and
success in startups and finding product market fit. And basically the research shows that
you're actually no more likely to succeed the second time around if
you failed the first time. So if you fail in finding product
market fit the first time, your odds of success are still 20 or
22% the second time around. But, if you succeed in finding product
market fit, the chances of your success the second time around go from 22%
to 34% which is still miserable. But, it's at least 50% better.

And so what I think that means is
there's actually not that much information encoded in failing
to find product market fit. Like you don't actually worry that much. But it's actually quite a bit encoded
in feeling the success of actually understanding what did work. And so when we were struggling to find
products market fit in 2011 and 2012, I felt this really acutely. We had failed multiple
times at this point, but I still didn't feel like I really
knew what I was looking for. What is this mystical
product market fit thing? So frustrating.

We kept seeing glimmers of hope. We kept convincing ourselves that this,
that or the other thing, like a visitor chatting with us on
our site whatever was a big deal, or a vaguely interested sales
prospect was product market fit. And so without any positive
product market fit examples, we didn't really know what it looked like. And so we could sort of convince
ourselves of everything. And the way I think about it now is I
really desperately if you imagine like a machine learning model, I had a bunch
of negative training set examples but I had no positive training set examples. And so of course my machine learning
algorithm was like I don't know.

And so I'm not going to
walk through three stories. But I'm going to show you
two examples of failure, actually five stories, two examples
of failure, and three examples on the positive side of times that we
actually did feel product market fit. And again, my goal is for you to
actually feel what this feels like so that you can identify
it in your own product. So today's segment is a customer data
platform, but, we actually started as an education tool, and it was actually
designed exactly for lectures like this. So, this is us coding in our Mountain View
apartment, on a summer of 2011.

And the idea was that, as a professor
standing up talking up to a class, you have no idea if anyone in the audience
actually understands what you're saying. And so we are students at that time
at MITA renounce school design. And we said we really want to do this it
gives students a button to push where they can say, I'm confuse, right? Or I get it, either one. And the professor would see this graph
overtime of how confuse the students were, might be helpful to me right now. And so, we built this.

We were at hundreds of thousands of
lines of code it had like commenting and notes and all sorts of crazy stuff, and we
actually came to Stanford's campus, we've convinced them, it might have even been
in this hall Convinced some professors. We would like run up to them after class. This is a picture from Berkley, we pounced
on this professor right after class, and we were testing for
product market fit, right. We were trying to convince. Hey professor like did you get
any feedback from your class during this class? No. Okay well we have a solution for you. So, we were hustling to try to get
people to actually use this tool.

But we we're mostly sort of ignoring any
test of real product market fit there. And so professors would agree to test it
out for a few lectures sort of out of pity, maybe, for some students
from MIT who were trying to help. And so basically we thought this was
product market fit but it really wasn't. And I'll show you why. Because if you stand in
the back of the classroom and look at what people actually
had on their screens, none of them were using the product. Right, like people were using
all these different things. This is that same class at
Berkley the next week by the way.

It was horrifying, and
basically the students, as soon as students opened their laptops
they all went and did other things. And so basically putting
a laptop into the classroom was the most distracting thing
you could conceivably do. So as you can imagine,
this was pretty horrifying. One of the more embarrassing
things that could have happened. We had just raised 600k coming out of why
Commonator Demo Day, and we had sold this vision of like, this is how the future
of classrooms is going to work, right? Like, it's going to be digital, it's going
to be online, much as this is a moot, etc.

And it was a great vision, but
again the market wins every time. It doesn't matter where vision is,
it matters what the market actually wants. And in this case,
the students didn't care. All right, the students didn't actually
get that much value out of using the tool. And actually if you go back,
we should have had an even earlier warning sign which was that the professors didn't
really want to use the tool either. Right? I mean you go and talk to the professors, they would sort of out of pity agree
to test it for a few lectures. But that is not the same thing as
product market fit where they're like holy crap that solves
this problem that I have.

Bullying customers into using your
product is not anything close to product market fit,
even if they reticently agree to do it. I think that being dismissive of users and
having your clear vision of the future that isn't
necessarily solving a problem for your customers is a pretty stunning
failure on our part, and is a key thing that founders do again and again and again
in their search for product market fit. So then we had to the awkward thing. Which by the way is the right thing of
calling back all the investors and saying.

This was like four weeks after
they sent the checks right. By the way, turns out this is a terrible
idea we are going to do something else. Do you want your money back? And in most of the cases
the investors did take. Or sorry, did not take the money back. Instead, we invested for the team,
like go find another idea. We believe in you guys. Go find something else. So he said, okay, let's do it. And we were all very committed to working
together as team or four founders. So we shut down the lecture tool.

We went and sort of shut down all
the classrooms, and then we went back to the whiteboard and we said, what is
something that is sort of interesting here and we had always felt like we should
have been able to determine that we didn't have product market fit, that the product
usage wasn't there from our actual data. The way that we actually figured this out,
right? Was we went and we stood in the classroom,
back where Sam is. And looked at what was on
all the laptop screens. And that was how we figured out whether
we had product market fit or not. But we should have been able
to do that with the data. We should have been able to
just look at the analytics and

figure out not only are people using it or not, but are anthropology classes using it
different than computer science classes? And so we decided to build
basically an analytics tool, which it turns out as bad idea,
in case anyone was considering that. And so the way that we approach this is, how many of you have read
the book Lean Start Up? Wow, that's actually awesome. You all are way far ahead. So as talks about you want to
get out of the building and

actually talk to customers, right. So we read that right around this
moment and we're like, okay, we're going to go out and
talk to customers. So we did that, and
we tried to validate our ideas. So we'd take people out for coffee and
we'd pull people from companies and try to figure out if they were
interested in analytics products. And I'd say again and again,
they were vaguely interested, right? And they had willingness to meet and
chat with us. And they said they wanted product
updates as things came out. And so we thought this must be it, right? This must be product-market fit. People are interested in what we're doing.

Again, this is not product-market fit. This is idle interest. Very, very big difference and so based
on this sort of we got very excited and we spend about the next six
months just writing codes and now this is in a new office, that's my
co-founder as you can see there's lots of code on our screen again,
wrong order of operations. I had gone on one sales trip
to visit potential customers. They were all pretty happy with mixed
panel and Google Analytics, but that had these edge case features that
they were hoping that we could solve.

So, I sort of tricked myself into
believing that these little edge cases that we might be able to solve were actually a really wide gap
we could fill as a product. And so I came back, and
I would sort of pitch my cofounders, and we would keep sort of believing
that we were almost there. If we just ship one more feature,
one more thing, we're going to get to product market fit. Again, bad idea. Give you another one, we used lots of
little positive interactions like this. You can also read this not as a positive
interaction, but every once in a while, some stray person would visit our website,
and they'd open a chat.

This is idle interest. Hey, what segment? And there's a complete
transcript of that website chat. You can see it's 3:00AM,
so we're up late coding. And, yeah, this person is interested. So maybe this is product market fit. Again this is not what it looks
like this is ideal interest. So it's now December 2012,
we've been at this for a year and a half and
we decided that something was wrong.

Right, clearly this was not working. And so we went back to YC, we emailed. A Paul Graham. And we said. Hey, we think we should catch up and sort of explain what's happened
over the last year and a half. And so, okay, great. So we go. This is us in front of
the old YC building, and we're walking around the little
cul de sac by YC on Pioneer Way. We bring him up to speed. Okay, we've spent half a million dollars. Here's everything we've been through
over the last couple of years. And as we walk around, he comes to a stop. Aand looks at us, and he says, "so,
just to be clear, we spent half a million

dollars and you have nothing to show for
it." totally accurate. And super fair and sort of like shocked us
into, ****, yea, we gotta do something. And that was the moment where we
realized we had hit rock bottom. But we still have a 100k left so
we still got one more shot, right? Alright, so that was all
the product market failure cases, now you're going to see some successes. But let's rewind. Let's go all way back to
the first week of YC. And in that first week, we have been
like well, we have this class and

lecture tool and
we should have analytics on it, right? So, we looked at the different
analytics tools, guest metrics, Google analytics, mixed panel. And we were looking at
what's similar here and we saw basically they have different
graphs and different APIs. But it's actually the same data going
into all these tools it's just that they give you different
stuff out the other end. And we were like, well, we don't really
want to make a business decision here about which tool we want to use. So we'll just solve the engineering
problem because we're engineers and we'll just build some code that
sends data to all three and

does this automatic translation. So we put one data point in, gets translated into three API calls
that go out to all three services. Cool, this was like 100 lines of code
in the hundreds of thousands that we wrote, right? Set that aside. And then now four months later, it gets
improved a little bit, four months later, it gets improved a little bit. At that point we are trying to sell
our own analytics tool, right? Akin to Mixpanel and Google Analytics. And we keep encountering the sales
objection when we're trying to sell it, which is I already have
Mixpanel installed and I don't really want to install your
tools it seems like a lot of work.

So my co-founder Ilya has this great idea,
he says, what if we take that little library we wrote a year ago that
we've totally forgotten about and we add ourselves as the fourth
service that it can send data to. And then every time someone hits us with
that objection, we hit them back with the open source library and we say, okay,
great now you can try both in parallel. And we use this like a growth hack, basically, to get customers
to start adopting our tool. Okay great, we start doing that, start
setting it up, people start replying. This is awesome. I love the library,
I'm definitely going to use it.

A few weeks later we follow up. Hey, we saw you're not still sending any
data to segment.io, what's going on? And they're like well,
the library is fantastic. I just don't really want to
use your analytics service. Should have taken note right then. But a few months go by,
people start storing this on GitHub. Maybe it gets up to, it was a big deal for
us at the time, like 30 stars. And I think there was maybe
one pull request issued. And then, fast forward some more,
people keep sort of paying attention. It's the first time we'd ever felt pull.

People were just finding this thing and
doing something with it. And so fast forward,
we have this conversation with PG, and the next day we sit down and we're like
all right, we need a new idea, right? And so my co-founder Ian,
he's like, you know what? I have an idea, you remember that analytics JS library
that has been sort of idling on GitHub. I think that could be a big business. And I was like, you've got to be kidding
me, that's the worst idea I've ever heard. First of all, it's open source, and
second of all, it's 580 lines of code so

who the heck's going to pay for that? Right, how do you build a business
around that, it makes no sense. And so we were like fighting and
fighting and fighting and I went home and I was wracking my brains so
like, how can I kill this idea? It's really bad. And it's going to sink us,
we only have one more shot. And then, so I came in the next day and
I was like, all right, guys, here's what we're going to do. We're going to build a landing page. It's going to be awesome landing page. It's going to be beautiful. We're going to put it up on Hacker News. It's going to push the product and we'll
have an Email sign up form at the bottom. And we'll use this to just test
whether it's a good idea or not. They agree.

Okay, great. Some I'm like, all right, totally done. We get ready to launch on Hacker News. I'm starting to think about other
ideas and it goes straight to the top. So it gets about 300 up votes on Hacker
News, gets a few thousand stars on GitHub. We have people reaching out to us on
LinkedIn demanding access to the beta. Like this guys says, what does a brother have to do to
get bumped up on your beta list? And there were others like this, right? Like holy crap, so full stop, right? Compare this to everything previously,
everything changed. This is what product market fail looks
like, where it's not just a single metric,

slowly starts moving,
it's not just a few random conversations where people express vague interest,
right? Literally every single
metric went totally haywire. And with our lecture tool and
our analytics tool, we've been sort of searching in the dark
for what features to build next. We did not have that problem any more,
right? There were thousands of
people who had signed up. And they're like, your seven integrations
are good, but I need these ten more. I'm like, I"m like deploying it tomorrow,
I'm like blah, blah, blah, we're like, holy crap. Like, okay, slow down.

And that's one of the key things. One of the key things is it flips from
being something that you're like pushing against the customer to all of a sudden
the customer's running away with it. And you're like, but like hold on,
wait it's not quite ready yet. And so another example,
with our analytics tool, we had this sort of sad
unanswered questions and chats. No one really seemed to care
about what we had built. But now all of a sudden we
had thousands of stars, people were issuing pull requests. We got like ten pull requests in the first
48 hours or something like that.

And I guess the other key thins is with
the lecture tool and our analytics tool, we had had this huge vision, right? We have a vision of like here's how
the classroom should operate early. Here's company should do analytics. And then we went about trying to
build a product that fit that vision. But this was the total opposite, right? This is like a little tiny
library that we built for ourselves that solved the real problem. We had no vision associated with
it whatsoever at the beginning. Now it does because we have something
that we really want to go accomplish. But at the beginning, it literally solves
the tiniest of tiny problems and so,

to your question earlier, right? Like, this is that tiny little foothold. And again, it's an open source
library with 580 lines of code. That's a foothold, right? And since then now we've expanded greatly
into doing all sorts of things and solving adjacent problems for customers. But the key is that, again, the market doesn't care at all
what your vision of the world is. The market wants what it wants,
and it will win every time. So if you walk away with sort of one thing
today, I think it's, let's be incredibly clear that basically product market fit
doesn't feel like vague idle interest.

It doesn't feel like sort of a glimmer
of hope from some early conversation. It doesn't feel like a trickle
of people signing up. It really feels like sort of everything in
your business has gone totally haywire. There's just this big rush of adrenaline
from customers starting to adopt it, and sort of ripping it out of your hands. And it really feels like the market
is dragging you forward. I think that Dropbox founders
said this best actually, that product market fit feels
like stepping on a landmine. And you really, you can't mistake the two.

So if you are at all questioning
whether you have product market fit or not, you don't. So, obviously that didn't stop
us from making this mistake. So just to make sure that this
is [INAUDIBLE], no, all right. So, I thought ClassMetric for
sure had product market fit, right? Big vision. Market said no. Like, market doesn't care what you think. I thought Segment.io for
sure had great product market fit. Again market said no,
market doesn't care, market wins.

And even on our third attempt when we did
find a product market fit, I thought for sure they were like,
this is too tiny to matter, right? But actually it solved the real problem,
and the market demanded it and
sort of ripped it out of our hands. Either that goes to show
sort of how obtuse I am or how hard it is to actually
find product market fit. >> So how come people for
something that was open source and only a couple lines of code? >> That's a good question. So it turns out that
the open source library by- >> [INAUDIBLE] Repeat the question. >> Sorry, the question is why
would someone pay anything for

an open source library
that's 580 lines of code? It's a good question. It turns out that the open
source library by itself doesn't totally solve the problem. So the actual problem that we found out
that we were solving after we launched it was that marketing teams keep coming
to engineering departments and saying I just signed a contract with
a exact target in marketing tool or I just a contract with W Analytics and
here's the docs. And engineering team says,
what the heck, I have a road map. Like, I need to be
executing this whole thing.

I can't do this analytics implementation. And so what really needed to happen and
sort of what analytics suggest could solve the problem, that we could
solve the hosted version it was allowing engineers to do a single implementation
of collecting that data once. And then letting marketers just
push buttons in our interface and send the data wherever they need it. But the open source library
doesn't quite solve that problem. Engineers saw that it was
the right abstraction, but if a marketer needed a new tool and
there is an open source version, the engineer still has to go compile it. So, in some ways we got lucky, that the open source version
doesn't fully solve the problem.

Cool, so I'm going to steal gratuitously
from Allen K's slides again. And I think finding product market fit
feels like finding the sort of blue plane, when you're in the pink plane. And I want to give some other examples
of finding product market fit, after finding that initial
product market fit. And so the key difference from that
initial breakthrough from what Alan Kay was talking about, is that rather
than removing yourself from the world and trying to imagine what the future is like,
you actually need to go out into the world and
research what problems people really have.

But then once you find that product,
once you find that sort of blue plain, then things actually
get enormously easier. That blue plane is basically a foothold
into a totally new perspective. And I think Allen K talked about
the value of perspective and context. sort of get this foothold into a whole
new way of thinking about the world, and new way of solving this problem for
someone. And so, not only do you know
what good fit feels, like but you are now operating in
sort of green pastures. And you are approaching problems in
ways that all the encumbrance and the pink plan don't think
about in the world.

So I wanted to give you two examples
of finding product market fit, since that initial a win for us. A short intro to what Segment does, we basically help you collect data
from mobile apps and websites, we pull it up to Segment and we fan it out
to all the different tools that you need. And, that's all you really need to know. One of the places we could
send data was Amazon S3, so this is basically just a place
to put all of your log files. And we started to notice that all
of our business to customers,

were using this one integration. They were all sending their data to S3, like you have to do
something with the data. You don't just like collect log files,
do something with the log file and you're like what the heck
is going on here. And so we went and visited five of our
largest customers in New York about three years ago, and we said okay,
you're using this integration, but what the heck are using it for? And for five out of five customers
in a row they said "well, we have a data engineering team"
that's taking data from the S3 bucket, converting it into CSV files and
managing all the schema translation, and

then uploading it into a data warehouse. Red shift. And the first time I heard that
from a customer I was like, okay, that's interesting and I took a note. Went to the second meeting,
customer said exactly the same thing. I was like, that's weird,
okay, take a note. Third conversation I was like,
all right, this is getting ridiculous. Did you guys talk ahead of time or
what happened here? And by the fifth time it's like, okay,
well obviously I know what we need to build, we need to build a connection
from this straight to red shift. So then the question is,
we went and built that. And just to show you now,

now we have a company that axis here
in millions of dollars in revenue. And so we have a real product
that real people are using. And so what does product market
fit look like at that point? So we're introducing
a second product basically. Well, it looks like this. So you can tell when we
introduced red shift. And basically again, almost every
metric in the business goes nuts. So, it's very, very clear whether or
not you have something, that is really transformative for
your customers or not.

One more story and
then I will open it up for Q and A. So, this is maybe about five months ago,
we had five ideas for products that we thought
might be exciting to our customers. So I went to visit the customer up a large
company, up in the Pacific Northwest. And, I sat down with a data architect
there and I said okay, here are the five ideas that I want to run through and
see if they're interesting to you. So I went through the first idea and
he was like, yeah, you know what?

I totally get it. That sounds super valuable. And I went through the second one and
he's like, that's really cool. That's cool that you can do that. And like, that makes a lot of sense. And then third idea,
fourth idea, same kind of thing. And so this is how I would summarize that,
that's great, I totally understand what the value is. It means, yeah, that would be great. Doesn't care is what that is code for,
that's someone being nice. And then on the fifth idea I said,
hey, here's what we can do, blah, blah, blah, blah. And he said "wait sorry, you can do what"?

>> [LAUGH]
>> And I re-explained it and he said, " interesting". He turns to his friend and says can you
set up a follow up meeting with this team, this team and this team. We also need to tell Joe about this,
because it could affect this other thing. That is the feeling of product market fit,
which is like, you're like wait, wait, wait, no, no, no it doesn't exist yet,
these are ideas we might like all of a sudden the customer
is just going to rip it out of you. And so now you're on a tight timeline
because the customer expects that it already exist. So again, that's what product
market fit actually feels like.

And I think if you want to
find product market fit and build one of these category leading
companies, become one of the one out of five founders that actually
does succeed in finding this. I think you just need to be
really honest with yourself. That the sort of glimmers of false
hope that you have are not the same, as customers actually ripping
something out of your hands. And so yeah, you just need to be honest
with yourself, that's the message. So, questions? Yeah, Sam.

>> I certainly agree, and it's been my experience that that
is what product market fit is like. But when you're trying to find it,
[COUGH] how do you even like have for what kind of ideas to try? When you're just sort of casting
around looking for ideas. >> I do think that the bigger problem
is not necessarily having the ideas. I think that everyone has
lots of interesting ideas. I think the bigger problem is not
killing the bad ideas fast enough. I think,
actually I have the most respect for the Codecademy founders in this respect.

I think they tried 12 ideas in 7 weeks or
something like that in the summer of YC, something totally absurd. But they legitimately tried them and
they killed them so fast, and the four days before demo day, they
started a new idea which was Codecademy. And then it worked. And on Codecademy on launch day,
on demo day they had 300,000 users or something like that. Where like again, that's the landmine
effect of nothing, nothing, nothing, nothing, nothing and they were so
good about killing the ideas that weren't, they had no problem throwing out
something that wasn't going to work.

So yeah, I'm not sure yet. I don't think there's any magic
to finding the right idea. If you just kill the bad ideas fast
enough, you'll probably find something. Yeah.
>> Talk a little bit about so this seems like
a [INAUDIBLE] lines of code. And it's solving a significant but
relatively small, significant but small problem, I don't know if
that's the way to describe it. How do you go about pricing
something like that, when you're doing that customer discovery? >> Yeah so we didn't, the question is
how do you go about pricing something

like this where it's
just 500 lines of code, it seems like it solves
a small problem for people. We thought it was a small problem for
people, and we under priced hugely for a long time. It turns out that that problem that we, the 500 lines of code solves
is actually really valuable. There are, we have lots of customers now
that pay over $100,000 a year to solve that problem. And there's more to the product now
than the 500 lines of code, a lot more. But, it's nevertheless is, the size of the
business problem has almost nothing to do

with the amount of code written. And I think it did take us a little
while to revalue things in that respect. But to answer your question, most directly we spent the first
year just accumulating customers. So we just had lots of people adopting it, we got to maybe 1,000 or 2,000
companies sending data through Segment. And then we tried
experimenting with pricing. And then I'd say the biggest kick in the
**** we had to actually get to the right point here is we had a sales advisor. Who said, there were reasonably
sized companies using us like

Live Nation and Party O and
stuff like that. And so we would go to sales
meeting at these companies, and the sales advisor would basically
get me pumped up ahead of time. And he'd be like,
Peter you have to ask for $120,000 a year. And I'm like, that's crazy,
what are you talking about? And he's like, Peter,
if you don't ask for $120,000 a year, I'm quitting as your sales advisor. It's like, well all right,
here we go, right? And so I would ask, I'd turn beet red and then they would negotiate
it down to 18k a year. But if you keep doing it,
eventually someone is like,

well that seems reasonable, and so. >> [LAUGH]
>> And you have to keep testing
the value like that, right? because you don't know how much value
you're delivering until you start asking for money. So I think if I had to do it over again I
would start asking for money earlier, and I'd be a lot more comfortable with it. If you're solving a real business
problem people are going to be happy to pay for it. I'm not sure I totally answered
your question but, yeah >> How do you know you're asking the right
people about your product? What if there should maybe be a just
a small shift in who you should be asking

that you're not realizing, how did you? >> Yeah, so the question is, how do you know whether you're
asking the right people? And how do you know whether you should
slightly shift who you're talking to versus shift the product? Yeah, this is actually why it's so hard to
find product market fit the first time. And why it's so
much easier the second time, once you have some product market fit. Because once you do have a defined
customer set that you sell to, you'll pretty easily figure out by going
back to those same people whether or not you're solving an adjacent problem for
them. But its really hard, the thing that makes it super hard at the
beginning is its like you have two things.

And you can either slide the product or you can slide the market by
talking to different people. And I'm not sure that there's a magic
rule to know when to slide which. But I think founders get
slippery with themselves, they aren't totally honest with themselves
about which thing they're doing when. So I think it's most important
just to recognize that okay, if we make this shift, we are shifting
the audience versus shifting the product.

because I think otherwise you
can shift the product and the audience simultaneously, and
think you're doing well, but then realize you're not. So I'm not sure there's a slam-dunk
solution to that, other than just being really honest with yourselves about
which thing you're moving when. Any other questions? Last chance, yeah. >> So you mentioned that you guys
were trying to find proper markets. That before you guys found it there
was a lot of moments that like

kill this product, kill this product. How do you encourage people on your
team to be like let's keep focusing on discovering the next thing? What do you self-talk, and
what do you talk about as a product? >> Yeah, so I think there's a pretty big
difference between finding that first product market fit,
where you can kill a product, immediately move to
a totally new audience. Codecademy tried a whole bunch of
different stuff, with a whole bunch of, speaking of switching audiences,
they went from S and B, to programmers, to [SOUND] restaurants,
all over the place.

Later on, when you actually are building
a company around it, and you have product managers, and each product manager is
sort of searching for a big breakthrough. It's a little bit easier in that you
can't shift the audience anymore, the audience is fixed. And so now it's a slightly more
straightforward problem for sort of helping a product manager
understand how to go talk to customers. And sort of reveal what problems
those customers have, and then actually solve them. So it's most training in that loop. And it comes from talking to either larger
customers, or your best customers, or

your worst customers. And sort of trying to push the boundaries
around the core of very happy people have, to what other problems you can solve for
them. Yeah? >> When you talk to your customer,
what do you ask them? What's your script? >> You basically just, the question is,
when you're talking to customers and trying to sort of understand what
their problems are, what's the script? I think the biggest mistake is, trying to pitch your existing ideas,
there's a place for that.

Right, when you have some
product market in fit, and you want to test some of this things. But I think most founders, and
certainly we made this mistake early on, of pitching and trying to sell. Rather that actually trying to
understand what their problems were. So now when I talk to a customer, I just start by asking what their
business problems are, right? Hey, person at retail company,
do you spend a lot on Facebook? They're like yeah, yeah, yeah,
we spend a lot of Facebook. I'm like how do you measure whether
that spend is efficient or not? And they're like well,
we don't really know.

Okay, well is that a problem? They're like yeah,
yeah that's a big problem. Well okay,
tell me why you haven't solved it. And they're like well, we haven't
solved it because blah, blah, blah. And you just start digging
in to these problems, and then you're actually really set
up to do a sale then, right? You're like well,
I have just the thing, right? And then they're like, holy crap. But if you don't have a product yet, now you know exactly what
problem you can fix, right? So I think it's more about listening and
digging than it is about pitching an idea.

Yeah?
>> How do you find these customer? How do you be like hey, this is my
customer, this company or is company? >> Yeah, so the question is how
do you find these customers? At the beginning, it's hustle,
it's just emailing people cold, it's getting interest
through everyone you know. It's scratching and clawing your
way through your social network and through introductions. After you have customers in an area,
then it gets easier, because you have people coming to you
all the time, mostly if you're inbound. And so then there's a steady
stream of people to talk to.

But the initial piece, that's why people
talk about hustle being such an important founder quality,
is because no one's going to help you. You've just gotta go find them, right? LinkedIn, investors, friends,
it's whatever you can claw together. Yeah?
>> So at the beginning, when you lectured for these customer,
when you're cold emailing them, how many percentage of people
actually come back to you? The question is,
when you cold emailed customers, what percentage of
people come back to you?

I don't remember, that was awhile ago,
very low, single digits percentages maybe. You're going to send a lot
emails that are unanswered. But that's the nature of sales. Honestly, if you're in a business
to business kind of environment, you're going to send a lot of
emails that go unanswered. Sales is basically the exercise of getting
the door slammed in your face nine times out of ten, right? Yeah? >> What sort of second,
where do you think would be in five years? >> Go back to diagram.

So what we have today is basically
sort of stream processing. So we have both the ability
to collect data across some broad section of stuff here. As well as the ability to fan it
out to a bunch of different places. And so what we want to do is basically
provide a platform that all of these tools can build on top of. That's the super short version. So you can imagine basically every team in
a company needs access to customer data. And we want to become the platform
on which those tools are built, whether that's email marketing, or
push notifications, or analytics, or

help desks, or CRMs or
payment systems, fraud detection, etc. All of those things operate on
a core set of customer data, and that's the customer data that already
flows through Segment by nature of us being that integration connection layer. And so we just want to expose that data
to partners to build on top of it. So when I talked about building
a platform, at the beginning, that's the next step for us. >> Peter, we are out of time,
thank you very much. >> [APPLAUSE]
