0:00

So Mike, we've been discussing product market

0:02

fit on several new episodes this season,

0:04

but I realized we've never really done

0:07

a deep dive into what product market

0:09

fit is. Yeah, that's

0:11

true. That's true. So

0:13

today listeners, we think you're in for

0:15

a treat. Yeah, we're going to

0:17

take a deep dive into product market fit,

0:20

clarify your understanding of this commonly used phrase,

0:22

and help you better understand what to look

0:24

for in your own work. All

0:27

right. Well, let's roll that intro then and

0:29

dive right in. Welcome

0:33

to rocket ship. Fm. Mm.

0:36

Mm. Mm. Mm.

0:39

Mm. Mm. Mm.

0:43

Mm. Mm. Mm. Mm.

0:47

Mm. Mm. Mm. Mm.

0:50

Mm. Mm. Mm.

0:54

Mm. Mm. Mm.

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4:00

for most startups and

4:02

even companies, right? This can happen

4:04

at the product level and actually

4:06

happens continuously throughout a

4:08

product's life cycle. So it

4:11

marks the transition from uncertainty

4:13

to kind of

4:15

market leader or at least market fit.

4:18

It's a sweet spot where your product

4:20

resonates perfectly with your target market's needs

4:22

and kind of catapults you ahead of

4:24

much of your competition. Yeah

4:27

and this concept is crucial because it

4:29

not only signifies a growing customer base

4:31

and market recognition but it

4:33

strengthens the bond between your product and its

4:36

users. However, uncovering this

4:38

fit involves rigorous market research,

4:40

collating customer feedback, continuously refining

4:42

your value prop to align

4:44

what truly matters to market

4:46

segments. But the absence of

4:48

product market fit cited as a key

4:50

factor in startup failures, understanding and achieving

4:52

this alignment becomes paramount for success. As

4:56

we navigate the complexities of establishing

4:58

a solid product market fit, we'll

5:00

dive into the strategies that encompass

5:02

the understanding of our target market,

5:04

leveraging insights from market research and

5:06

iterating on your minimum viable product

5:08

based on possible feedback. The steps

5:10

to discover they're not quick, they're

5:12

not simple and this is because

5:14

of the diverse challenges that

5:17

are presented by ever changing market

5:19

dynamics. So in

5:21

this episode, we'll explore the quantitative and

5:24

qualitative metrics that signal a strong product

5:26

market fit. We'll talk about strategies that

5:28

you can hone your value proposition with

5:31

and we'll talk about the crucial role

5:33

of customer lifetime value in sustaining growth.

5:36

Our journey will also highlight success stories to

5:38

illustrate how achieving product market fit has propelled

5:40

startups to new heights. So

5:43

to start off, let's define product market fit.

5:46

Product market fit is akin to

5:48

solving a complex puzzle where every

5:50

piece represents a critical aspect of

5:52

your product's journey towards market acceptance

5:54

and success. Let's

5:56

break down this concept into digestible parts

5:58

to understand its nature. Product

6:02

market fit is achieved when your product

6:04

not only meets the needs of your

6:06

target market but does so in a

6:08

way that surpasses alternatives providing significant customer

6:11

value. The components are, well really

6:13

there are three of them. First, a

6:15

quality product that aligns with market demands.

6:18

Two, relevance to the target

6:20

audience's need and affordability. And

6:22

three, a good market

6:24

that's actively seeking the solutions that

6:27

your product offers. Now

6:29

when you reach product market fit or rather when

6:31

you achieve a milestone of product market fit, there's

6:33

a few signals. A segment

6:35

of potential customers indicating that they're switching

6:37

to your product from a competitor. Users

6:41

who have rejected similar products but they're

6:43

willing to try yours and

6:45

a positive retention rate when compared

6:48

to your competition. Achieving

6:50

product market fit, it's not a

6:52

one-time event. Instead, it's more of

6:54

a continuous process of iteration and

6:56

refinement based on market feedback and

6:58

evolving customer needs. By understanding

7:00

the core elements of product market fit

7:02

and actively working towards aligning your product

7:04

with your target market, you

7:06

set the foundation for sustained growth and

7:09

success. Let's

7:11

hear from Michael Siebel of Y

7:13

Combinator, from the official Y Combinator

7:15

podcast as he's talking about product

7:18

market fit. It's weird because

7:20

it sounds so close but it's not. I built

7:22

the thing that customers want. Right. And

7:25

like what's hilarious is product

7:27

market fit is what happens after you've built

7:29

the things the customers want. It turns out

7:31

the only way you know you've built them

7:33

the customers want is because they're using it

7:35

in an explosive and destructive way. And

7:38

like people want to separate these

7:40

two concepts. It's like so amazing. It's like

7:42

so like you can see intellectually why.

7:44

It's just so much easier to be able to

7:46

look at your thing and say this is what

7:48

customers want. Right. And not have to really have

7:51

any customers. Oh yeah totally. It's easier to say

7:53

that. And so man

7:55

people really just want to separate those two things

7:57

out and it's like if you are not getting

7:59

explosive use of you do not have what

8:01

customers want. In our quest to

8:03

achieve broad market fit, we've identified customer

8:05

feedback as the linchpin of success.

8:08

Here's how we leverage this invaluable asset. Openness

8:10

to feedback, right? We encourage an

8:12

environment where feedback is not just

8:15

welcomed, but actively sought. This is

8:17

key in your discovery process. This

8:19

involves being both receptive to

8:22

positive and negative insights. While

8:25

this sounds obvious, it's actually not an easy

8:27

environment to create. I've largely seen

8:29

a few distinct types of

8:32

organizations that fail at this. Ones

8:34

that think they're smarter than the customer. So

8:36

their customer's always wrong, or their customer's an

8:38

idiot. This feedback

8:41

doesn't actually resonate throughout the

8:43

organization, at least the feedback that needs

8:45

to get through. There's others that

8:47

are scared of their customer feedback, mainly

8:50

because it will kind of hurt their fragile

8:52

egos. I've seen

8:54

that a lot when it was very public companies

8:57

that were getting a lot of public scrutiny. So

9:00

they became scared to ship and scared to

9:02

iterate. Or ones that simply do

9:04

everything that the customer asked for.

9:08

But none of these are correct. In an organization that's truly

9:10

open to feedback, we want to

9:12

solve problems for the customers. They

9:14

actually have to be actively listening. And

9:17

doing what with what they're hearing? Yeah,

9:20

not a whole lot sometimes, right? They're listening

9:22

a lot, in fact. And

9:25

then they're slowly piecing together what the

9:27

customer needs are for a

9:29

comprehensive product solution. This doesn't mean

9:31

building exactly what the customer asked

9:33

for. This means listening, interpreting their

9:35

needs, and then building what they

9:38

need. But this process, it takes

9:40

time, as we'll hear. One

9:43

incredible tool for doing all this is

9:45

Jobs be Done. And we're going to

9:47

dive into a little bit about

9:49

Jobs be Done after a quick break.

9:55

Before the break, we were discussing how product

9:57

market fit is defined, some basic methods for

9:59

pursuing product... product market fit. And

10:01

one of those methods happens to be jobs to be

10:04

done. We're gonna play a clip here aren't we? Which

10:07

clip would that be? The

10:09

Clay Christensen, talking about the

10:11

McDonald's milkshake, yeah. Alright,

10:13

okay yeah. I was planning on some sort

10:15

of clip here. I think that is a

10:17

good one. Alright, well let's just play

10:20

the whole thing because it's too good to kind

10:22

of cut up. Alright, well let's

10:24

hear from Clayton Christensen, the late Clayton

10:26

Christensen, one of the founders of Jobs

10:28

to be Done on one

10:31

of the origin projects where he and

10:33

his team began to define what Jobs

10:35

to be Done actually is. Now this

10:37

is coming from the HubSpot podcast right

10:39

here. I approached this and

10:42

McDonald's is a very sophisticated

10:44

marketing company and

10:47

they have data up the gazoo and

10:50

they decided that they needed

10:52

to innovate in

10:54

their milkshake product line so that more

10:57

people will buy milkshakes. And

11:01

they had data that

11:03

allowed them to draw

11:07

a quintessential customer

11:09

of milkshake customers.

11:15

And they then would

11:17

identify this profile of a

11:19

milkshake customer. It turns

11:21

out I fit that profile very well. They

11:24

would then invite people who

11:27

hit the profile into

11:29

conference rooms and they'd ask them questions,

11:33

trying to understand how could we improve the milkshake

11:35

so you'll buy more of them. And

11:38

they'd get very clear feedback.

11:41

They would then improve the milkshake on

11:43

those dimensions of performance and

11:45

it had no impact on sales or

11:48

profits whatsoever. So

11:50

we invited ourselves and

11:52

they agreed that we

11:54

could try to approach it

11:57

in a very different way. You

12:01

know, there's a

12:03

job out there somewhere that

12:06

arises in people's life on occasion

12:09

that causes them to need to

12:11

buy a milkshake. And

12:14

we need to understand what

12:16

the job is that causes

12:18

people to buy a milkshake.

12:22

And so one of our colleagues stood in

12:24

a McDonald's restaurant for 18

12:27

hours one day and

12:29

just took very careful notes on

12:32

what time did he buy the milkshake, what

12:35

was he wearing, was he alone or

12:37

with other people, did he

12:39

buy other food with it or just the

12:41

milkshake, and did he eat it in

12:43

the restaurant or did he go off in the

12:45

car and take on. It

12:48

turned out that about

12:50

half of the milkshakes were sold

12:53

before 8.30 in the morning. It

12:56

was the only thing they bought, they were always

12:58

alone, and they always got in the

13:00

car and drove off with it. So

13:04

we came the next morning

13:07

and confronted and positioned ourselves outside

13:09

the restaurant so that we could

13:11

confront these people as they

13:13

were emerging with their milkshake. And

13:16

in language that they could better

13:19

understand, we asked them, excuse

13:22

me, you're

13:24

creating no trouble for me because I, can

13:27

you explain what job arose in your life that

13:29

caused you to come here at 6.30 in

13:32

the morning to hire a milkshake to get this,

13:34

what's the job to be done here, as

13:36

they would struggle to answer why they came at

13:38

6.30. We'd asked them

13:40

to step back a minute and

13:44

think about the last situation in

13:46

which you had the same situation

13:49

needing to get the same job done.

13:52

You didn't come here to hire a milkshake

13:54

from McDonald's, what did you hire to

13:56

get the job done? And one guy

13:59

said, yeah, I... hire donuts

14:01

to do the job, but I

14:03

can never hire just one. And

14:06

another guy said, I do bagels, you

14:08

know, but boy they're dry

14:10

and they're tasteless. And so

14:12

I have to put the crammed cheese on and

14:15

steer the car with my knees while

14:17

I'm putting cream cheese on. And

14:20

it turns out one of them said, you know, I

14:23

hired a Snickers bear to do the job.

14:26

But I feel so guilty. I've never hired

14:28

Snickers again. And one guy said, you know,

14:30

I never thought about it before, but last Friday

14:32

I hired a banana to do the job. But

14:35

it doesn't do the job very

14:37

well at all. You

14:42

finish it in less than a minute. But

14:44

let me tell you, when I go

14:47

to McDonald's, it is

14:49

so viscous. I can,

14:51

it takes me about 23 minutes to

14:54

suck it up that thin little straw. And

14:58

I don't care what the ingredients are. All

15:00

I know is that when 10 o'clock comes,

15:02

I'm still full. And the job

15:05

that all of these people were trying to

15:07

get done was I

15:09

have a long and boring drive to

15:11

work. And

15:13

I needed something that would

15:15

just keep me engaged with life while

15:18

I'm driving the car. I'm

15:21

not hungry yet, but I know

15:23

I'll be hungry by 10 o'clock. So

15:25

I also, part of the job is

15:28

I need something to eat that would

15:30

keep me so full when

15:33

10 o'clock happens. And

15:35

that's the job that they're hiring the

15:38

milkshake to do. That is, they have a long

15:41

and boring drive to work. And

15:43

they needed to add dimensions

15:45

of it to keep them

15:47

engaged with life. And

15:50

from the customer's point of view, the

15:54

milkshake does the job better than

15:56

any of the competitors. And

16:01

the competitors from the customer's point

16:04

of view are not just in

16:06

the product category, but

16:08

they drop from bananas and donuts

16:10

and bagels as I mentioned. And

16:14

so when you think about how big

16:16

the job is, you

16:18

have to look at who

16:20

the real competitors are from the

16:22

customer's point of view. And

16:25

they come from very different categories. And

16:28

this is absolutely one of my favorite product

16:30

stories. And the crazy

16:32

thing is when McDonald's applied this theory

16:34

and insights to their product, they

16:37

found the market to actually be eight

16:39

times bigger than they anticipated. Yeah,

16:42

that's because they stopped marketing to

16:44

a persona and they started marketing

16:46

to address a problem, a more

16:48

universal problem than any one persona

16:50

could ever capture. Now,

16:52

measuring and tracking the right metrics is

16:54

crucial to understanding if your product is

16:56

to hit the sweet spot of product

16:59

market fit. These metrics can be broadly

17:01

categorized into quantitative and qualitative types, each

17:03

offering unique insights into how well your

17:05

product resonates with your customers. Product

17:08

market fit can be determined by observing

17:10

a range of key metrics that provide

17:12

valuable insights into how a product is

17:14

performing in its intended market. However,

17:16

the following is not an exhaustive

17:18

list of metrics that can indicate

17:20

product market fit. What we're trying

17:23

to do is give you some high

17:25

level things to look for

17:28

in measuring your product market fit. So

17:30

you're probably going to customize these mix

17:32

and match, but we wanted to provide

17:34

you with at least a starting point.

17:37

Yeah, let's go through some of the

17:40

common metrics that are used for measuring.

17:43

Again, keep in mind, not definitive. And

17:45

yes, they can definitely be customized. Okay,

17:48

so let's start with customer retention rate.

17:51

A high customer retention rate indicates that

17:54

customers are finding value in

17:56

your product over time. It's a strong

17:58

sign of product market fit. It

18:00

shows your product in not just kind of

18:02

a one-time wonder, but something that customers continue

18:04

to use and derive value from. So

18:07

what you're measuring is your retained

18:09

customers month over month. And

18:12

you wanna make sure that based on

18:14

your industry, your retention rate is higher

18:17

than at least the average. That's

18:19

showing that your product is performing

18:22

better than at least half

18:24

of the market. Then we have Net

18:26

Promoter Score, NPS. Now NPS measures how

18:28

likely customers are to recommend your product

18:31

to others. A high NPS

18:33

means customers love your product and love it

18:35

enough to promote it, signaling strong

18:37

product market fit. However, NPS scores

18:39

can be better measured when the

18:41

pool of customers have reached more

18:43

of a consistent level. Also

18:46

when the business speaks to retained customers

18:48

as they're engaged with the business to

18:50

a greater extent and more likely to

18:52

recommend the product to others. While

18:55

hard to measure within a short window,

18:57

there are ways to predict changes in

18:59

customer lifetime value that can be used

19:02

in your tracking. Also known

19:04

as LTV, a high LTV indicates customers

19:06

find consistent value in your product much

19:08

like the retention rate. But what you're

19:11

actually measuring is the value that they're

19:13

spending with you. What is the amount

19:15

that the average customer spends with you

19:17

over the lifetime of their subscription or

19:20

a certain period of time

19:22

if you're more of a transactional business. And

19:25

so if customers don't provide a good

19:27

lifetime value, the marketing costs are gonna

19:29

increase. So that's why

19:31

a good LTV, this can

19:33

be used in conjunction with your customer

19:36

acquisition cost. Now customer acquisition

19:38

cost, it wouldn't be your only

19:40

indication of product market fit but

19:42

it could be used in conjunction

19:45

with customer lifetime value or something

19:47

like maybe average order value to

19:49

determine the effectiveness of your messaging

19:51

and product solution. The

19:53

cost of acquiring new customers is significantly

19:56

lower than the revenue that those customers

19:58

generate. It's a good sign for. product

20:00

market fit. It shows that the product

20:02

is valuable enough for customers to engage

20:04

and contribute to a positive return on

20:06

investment. If it's expensive to

20:08

acquire new customers, you may

20:10

want to make sure that they stick

20:12

around for a longer time so their

20:15

customer lifetime value surpasses that of the

20:17

cost of acquisition. Okay, let's

20:19

take a quick break and we'll be right back

20:21

with some of the common challenges in finding product

20:23

market fit. Before

20:27

the break, we were talking about product market

20:29

fit metrics and we should now

20:31

transition into some of the challenges behind

20:34

product market fit. First,

20:36

let's hear from Peter Reinhardt on Segment's

20:38

journey to finding product market fit from

20:40

a talk he did at Stanford University.

20:44

But we actually started as an education tool and it

20:46

was actually designed exactly for lectures like this. So this

20:48

is us coding in our Mountain View apartment in

20:50

the summer of 2011. And the

20:53

idea was that as a professor standing up talking

20:55

to a class, you have no idea if anyone

20:57

in the audience actually understands what you're saying. And

21:01

so we were students at the time at MIT in Rhode Island

21:03

School of Design and we said what we

21:05

really want to do is give students a button to push

21:07

where they can say, I'm confused, or

21:11

I get it, either one. And the professor would see

21:13

this graph over time of how confused the students were.

21:16

It might be helpful to me right now. And

21:21

so we built this. We wrote hundreds of thousands

21:23

of lines of code. It had commenting and notes

21:25

and all sorts of crazy stuff. And

21:27

we actually came to Stanford's campus. We convinced that.

21:29

It might have even been in this hall. Convinced

21:32

some professors. We would run up

21:34

to them after class. This is a picture from Berkeley.

21:36

We bounced on this professor right after class. And

21:39

we were testing for product market fit. We were

21:41

trying to convince, hey, professor, did

21:43

you get any feedback from your class during this class?

21:45

No. Okay, well, we have a solution for you. So

21:48

we were hustling to try to get people to actually

21:50

use this tool. But

21:53

we were mostly sort of ignoring any

21:56

test of real product market fit there. And

21:58

so professors would agree to test it out for a future.

22:00

a few lectures sort of out of pity

22:02

maybe for some students from MIT who were

22:06

trying to help. And

22:08

so basically we thought that this was product market fit, but it

22:10

really wasn't. And I'll show you why. Because

22:12

if you stand in the back of the classroom and look at what

22:14

people actually had on their screens, none

22:16

of them were using the product. People were

22:18

using all these different things. This is that same class

22:21

at Berkeley the next week, by the way.

22:24

It was horrifying. And

22:27

basically, as soon as students opened their laptops, they all

22:29

went and did other things. And

22:31

so basically putting a laptop into the classroom was the

22:34

most distracting thing you could conceivably do. So

22:38

as you can imagine, this was pretty horrifying, one

22:42

of the more embarrassing things that could have happened

22:44

to us. We had just raised 600k coming out

22:46

of Y Combinator demo day. And

22:48

we had sold this vision of this is how the future

22:50

of classrooms is going to work. It's

22:53

going to be digital. It's going to be online, much as

22:55

this is a MOOC, et cetera. And

22:58

it was a great vision. But

23:00

again, the market wins every time. It doesn't

23:03

matter what your vision is. It matters what the market

23:05

actually wants. And in this case, the

23:07

students didn't care. The students didn't actually get that

23:09

much value out of using the tool. And

23:12

actually, if you go back, we should have had an even

23:14

earlier warning sign, which is that the professors didn't really want

23:16

to use the tool either. When you

23:18

go and talk to the professors, they would sort of out of

23:21

pity agree to test it for a few lectures. But

23:23

that is not the same thing as product market

23:25

fit, where they're like, holy crap, that solved this

23:27

problem that I have. And

23:30

so sort of bullying customers into

23:32

using your product is not anything close to product market

23:35

fit, even if they sort of reticently agreed to do

23:37

it. And I think

23:39

being dismissive of users and having your clear vision

23:41

of the future that isn't necessarily

23:43

solving a problem for your customers is

23:46

a pretty stunning failure on our part. And it's like

23:48

a key thing that founders do again and again and

23:50

again in their sort of search for product market fit.

23:54

So then we had to do the awkward thing, which,

23:56

by the way, is the right thing of calling

23:58

back all the investors. and saying, this

24:01

was like four weeks after they'd signed the checks, right? By

24:05

the way, it turns out this is a terrible idea. We're going

24:07

to do something else. Do you

24:09

want your money back? And in

24:11

most of these cases, the investors did take or sorry, did

24:14

not take their money back. They said, we invested for the

24:16

team. Like, go find another idea.

24:18

We believe in you guys. Go

24:21

find something else. So I said,

24:23

OK, let's do it. And we

24:25

were all committed, very committed, working together as

24:27

a team of four founders. So

24:30

we shut down the lecture tool. We went and sort of

24:32

shut down all the classrooms. And then we went back to

24:35

the whiteboard. And we said, what is

24:37

something that is sort of interesting here? And we had always

24:39

felt like we should have been able to determine

24:41

that we didn't have product market fit, that the

24:43

product usage wasn't there, from our

24:45

actual data. The way that we actually figured this

24:47

out was we went and we stood

24:49

in the classroom, back where Sam is, and

24:53

looked at what was on all the laptop screens. And that

24:55

was how we figured out whether we had product

24:57

market fit or not. But we should have been

24:59

able to do that with the data. We should have been able

25:01

to just look at the analytics and figure out, not only are

25:03

people using it or not, but are

25:06

anthropology classes using a different than computer science

25:08

classes? And

25:11

so we decided to build basically an analytics tool,

25:14

which it turns out is a bad idea, in

25:17

case anyone was considering that. But that

25:19

wasn't the end. No, Segment goes on

25:21

to createanalytics.js to solve

25:23

their own problem as they're quickly running out of

25:25

money. Ian is like, you know what?

25:28

I have an idea. Remember that the

25:30

Analytics.js library that has been idling on

25:32

GitHub? I think that could be a

25:34

big business. And

25:36

I was like, you've got to be kidding me. That's the worst idea I've ever

25:38

heard. First of

25:40

all, it's open source. And second of all, it's 580 lines of code. So

25:43

who the heck is going to pay for that? How do you

25:45

build a business around that? It makes no sense.

25:47

And so we were fighting and fighting and fighting. I

25:49

went home and I was racking my brain to like,

25:51

how can I kill this idea? It's really bad. And

25:55

it's going to sink us. We only have one more shot. And

25:58

so I came in the next day and I was like, all right, guys. Here's what

26:00

we're gonna do. We're gonna build a landing

26:02

page. It's gonna be an awesome landing page. It's gonna be

26:04

beautiful. We're gonna put it up on Hacker News.

26:06

It's gonna pitch the product. And it'll have an

26:08

email sign up form at the bottom. And we'll use this to just

26:10

test whether it's a good idea or not. They

26:13

agree. Like, okay, great. So

26:15

I'm like, all right, totally done. We get ready to launch it

26:17

on Hacker News. I'm starting to think about other ideas. And

26:20

it goes straight to the top. So it

26:22

gets about 300 upvotes on Hacker News. Gets

26:25

a few thousand stars on GitHub. We

26:27

have people reaching out to us on LinkedIn, demanding access

26:30

to the beta. Like this guy says, what does

26:32

a brother have to do to get bumped up on your beta list? And

26:35

there were others like this, right? Like, holy

26:37

crap. So like, full stop, right? Like, compare

26:40

this to everything previously. Like,

26:43

everything changed. This is what product market fit looks

26:45

like. Where it's like, not just

26:47

a single metric like slowly starts moving. It's

26:50

not just a few random conversations where people

26:52

express vague interest, right? Like,

26:54

literally every single metric went totally

26:56

haywire. And

26:58

with our lecture tool and our analytics tool, we've

27:00

been sort of searching in the dark for

27:04

like, what features to build next. We

27:06

did not have that problem anymore, right? There were like thousands

27:08

of people who had signed up and they're like, your seven

27:10

integrations are good, but like, I need these 10 more. And

27:13

like, I'm deploying it tomorrow on like, blah, blah, blah, blah,

27:15

blah. And we're like, holy crap, like, okay, slow down. And

27:19

that's actually one of the key things. One of the key

27:21

things is that it flips from being something that you're like

27:23

pushing against the customer to all

27:25

of a sudden the customer's like running and

27:27

you're like, wait a minute, but like, hold on. Like,

27:30

wait, it's not quite ready yet. And

27:33

so another example, with our analytics tool, we

27:35

had this sort of sad unanswered questions and

27:38

chats. No one really seemed to care about

27:40

what we had built. But

27:42

now all of a sudden we had thousands of stars. People

27:44

were issuing pull requests. We got like 10 pull requests in

27:46

the first 48 hours or something like that. And

27:51

I guess the other key thing is with our lecture

27:53

tool and our analytics tool, we had had this huge

27:55

vision, right? We had a vision of like, here's how

27:57

the classroom should operate or like here's how companies should.

28:00

do analytics. And then we went about

28:02

trying to build a product that fit that vision. But

28:04

this was the total opposite, right? This is like

28:06

a little tiny library that we built for ourselves

28:09

that solved a real problem, had

28:12

no vision associated with it whatsoever at the beginning.

28:14

Now it does, because we have something

28:16

that we really want to go accomplish. But

28:19

at the beginning, it literally solves the tiniest of tiny

28:21

problems. And so to your question earlier, this is

28:24

that tiny little foothold. And

28:26

again, it's an open source library with 581

28:28

lines of code. That's a foothold,

28:31

right? And just like that, they found

28:33

product market fit. Quite unexpectedly, actually. But

28:35

Peter does an incredible job here outlining

28:37

what product market fit feels like and

28:40

what are some of those challenges that

28:42

they had to go through repeatedly

28:45

in order to find it. And

28:47

if you ask yourself, is this how

28:50

my product feels? Have we

28:52

reached this stage, or are we still looking

28:54

for it? Give yourself

28:56

an honest answer. And then

28:58

re-evaluate and possibly iterate, right?

29:01

So not every company has

29:03

every metric go haywire, like we heard

29:05

with segment. But there's an evolutionary process

29:08

from pushing your product to the market

29:10

to the market, actually pulling your product

29:12

to where it needs to be. And

29:14

that is what we're looking for

29:16

in product market fit. Yes. And that's going to

29:18

wrap us up for today. Hope

29:20

that you enjoyed this deep dive

29:23

into product market fit. For

29:25

Michael Saka, I'm Mike Bellcito. And

29:27

this is rocketchip.sm.