Chapter 1: What is the main topic of this podcast episode?
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All right. Let's see if this works. OK, cool. So I'm Ben. I'm going to talk about finding products market fit with data. A quick bit about myself. So this sort of talk is a lot of stuff drawn from a blog. This is kind of the vibe of that blog. But kind of more importantly for this talk, I am one of the founders of a company called Mode. Mode is a BI tool built for data teams. It looks like this.
It also looks like this. This is kind of the visualization, draggy, droppy BI stuff. And here are some of our customers. So these are some folks that use the products today. But obviously, this is not like what Mode was like in the early days. This has taken some time for us to build. Back in the early days, this was actually Mode's first product. It didn't look like all that fancy stuff.
It looked like this. And instead of having any customers, we had this. These were the other two co-founders of Mode, one of whom is here and talking in like 40 minutes later. So check them out. But anyway, so the point here was generally where Mode is today obviously is not where we started. And there were some rough moments along the way that took us some time to get there.
And so this is actually a message from one of the other co-founders back when things weren't doing so great that said, hey, you know what? I don't actually think things are looking so good. I don't think that Mode has product market fit right now. And so this was like what the world was like back before some of the stuff that we built and some of these customers we had.
And this was like kind of that world. So this is kind of a very basic graph of what product market fit is like. Prior to product market fit, everything sucks. You get a message like this from Josh. After product market fit, everything seems good. And so the question really to talk about here is how did things get better?
Like what is it that got us from the left side of that graph to the right side of that graph? And how do we actually figure out where we were before to get to where we are now? So I want to talk about two main reasons and two kind of big things to think about as you're trying to do this. One of those reasons is figuring out what people like.
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Chapter 2: How can data help in finding product-market fit?
When they first started using Mode, they actually recently just released a mobile app. So they had this kind of main website builder, but they also wanted to have a mobile app for people to be able to do the various things they need to do to plan their wedding and that kind of stuff. And that was this app, the 133rd most popular app in the lifestyle section of the App Store.
When they released it, it didn't do so hot. It was something that was not terribly popular. It was a really important initiative for them to figure out, hey, this is what we think of as the future of our business. We need to be able to figure out how to get people to do this sort of stuff on mobile as well as on the website. And so how do we build that?
And this app did not have sort of product market fit when they initially launched it. Their first set of experiments were basically build a bunch of features, go look at how they're used on something like Google Analytics, see if they work. If they don't, try again and kind of rinse and repeat.
That it was basically this iterative process of build features, see what happens, build features, see what happens, build features, see what happens. It didn't really go anywhere. The app basically stayed stuck where it was, and it never actually got popular.
And so what ended up happening was one of the folks who was the head of their data team started poking around how people were actually using the existing app. And he made this chart. This was a chart that he made in Mode. The specifics here kind of don't matter that much.
But basically, you can imagine each one of these kind of further out rings is people sort of progressing through different stages of the website. So the green bar might represent the home page. The purple bar might represent the registry page, whatever. And so it sort of tracks the different patterns of how people are moving through the site.
And so he made this chart just to kind of see how this behavior looked. And the thing that he noticed was this down here, which was this very kind of strange brown spike of like, hey, why are people continuing to come back to the same page over and over and over again? They're consistently doing it. Like, what's the deal with going on here?
Seems like there might be some pattern here to pay attention to. And what it turns out is it was this page. It was a page that was like a countdown for how long until your wedding. That it was a single page that said, hey, you're going to get married in 86 days, in 85 days, in 84 days.
And people kept coming back to it day in and day out, like taking screenshots of it and then sending it to their friends or posting on social media or whatever. And this was a small thing that they built. They didn't think it really mattered when they first built the app. But in putting it together, they realized this was how people were actually using it.
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Chapter 3: What are the two main reasons for achieving product-market fit?
One is Greenhouse, and the other is Front. So Greenhouse is an applicant tracking system. Basically, if you have job recs on your website, Greenhouse will power those. They will then help recruiters usher candidates through the job process. So they'll move people from one stage to the other. Candidate goes from an interview to an on-site to an offer, things like that.
And so it's a management system for that and for recruiters. Front is a shared inbox. It's basically for support teams to have a single inbox where people can email and multiple people for the company can then respond to that email and see the conversations that everybody's having.
And so the next few slides, I'm going to talk about these things using a graph that looks like this, which looks like a huge mess right now. But I can explain what it means in a much simpler version, which is this. So basically, this is looking at users and the days that they used a product.
And so if you look at this first user, for instance, what this is saying is the user used the product on day one, on day two, and on day four. Or on day two, you can say that users one, three, four, five all used the product, but number two didn't. Or if you look at an individual day, you can say, hey, this block represents user one using the product on day two.
And what you can pretty obviously calculate from this is retention rates. So on day one, all five of them used it. So it's 100%. Four out of five use it on day two. So it's 80% and so on out of where it's like 80, 60, 60, 40. So basically, if you then zoom back out of this, you can get a chart like this that shows the different patterns of how people are actually using the product.
And so this is hypothetical. It's like me making it up of what Greenhouse may well look like. I don't actually know. But you could imagine a pattern that looks like this for Greenhouse, where people use it sporadically, like recruiters aren't logging in every day. People aren't progressing necessarily through recruiting pipelines every day.
Some of these people might be hiring managers that are only coming in when they actually have interviews, things like that. So they have this kind of sporadic pattern of returning. But on this chart, if you look like 90 days after, like three months into this, actually 97% of these users still come back.
So in the time between 90 and 120 days, there's actually 97% of these 200 users are all still engaging with Greenhouse, which seems pretty good.
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Chapter 4: How did The Knot use data to improve their app's performance?
So this is Front. This is another version of this chart. Could represent the same thing. You can imagine these blocks mean the same thing. In Front's case, it's very much intended to be a daily usage app. It's something that's supposed to be like you use it every day. It's like your email. If you use your email every day, most people probably use their email every day.
They aren't using it once a week or something like that. So in this case, though, if you draw the same line because of this usage pattern, after three months, only 12% of these people are actually still using Front. So it's a very different sort of retention pattern, a very different group of people are using it. And in this case, you'd probably look at this and say, that doesn't look so good.
So looking at these two charts, on this top one from Greenhouse, you'd be like, OK, great. Now it's time to expand. It's time to scale. Let's go hire more salespeople. Let's jack up that marketing spend chart, all that stuff. We have product market fit. It's all great. For this chart, you'd be like, no, we're not there at all. We have some few people who like it.
We've sort of found maybe the path in the sand or the path in the grass that people are following, that a handful of these people really gravitate towards. But clearly, this is a product that does not seem terribly sticky yet and does not have product market fit. The problem here is if you just look at daily retention rates for these two things, they will look exactly the same.
The way this math works out is these two things can actually show the exact same retention rates on a day-by-day basis. And really what that means is that metric, even if that metric seems like a really good one, it hides all of these other patterns that may not actually show up in simple metrics like retention. And so the point here is what Steve Blank says.
Steve Blank is author of Four Steps to the Epiphany, which is one of the canon of Silicon Valley kind of stuff. Basically, these one size fits all approaches of just use retention or just use certain metrics do not work for all startups. You have to really think about what it is that your startup needs. And so what do you do instead of that?
Basically define what fit looks like for your very specific product. What does it mean for someone to use your product in the way that you intend them to use it or in the way they should be using it? And how would that actually show up in these sorts of retention metrics? So for instance, for Greenhouse, that may not look like just this three-month retention or daily retention.
It may actually be things like, how many hires do they make? This is actually, so both Greenhouse, the reason I use Greenhouse in front for these two things is they actually published a study a bit back with one of these VCs saying, hey, these are the metrics they use to decide when to scale. And this is actually what they said.
So it was like, OK, if we have a number of hires made, if that's staying steady, that's product market fit for us. That's a sign that we're ready to go. For Front, they want users to be engaged early and often. Again, it's not a sporadically used app. It's an app you're supposed to use every day. The point here is to use it early and often.
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Chapter 5: What iterative process did The Knot follow to find success?
This is not something where it's like you are pre and post product market fit forever. You are pre and post product market fit for particular types of buyers, for particular products, for particular segments, and all those sorts of things. So these two things, it's not actually like these are the two things you have to figure out is what people like and when to scale.
It's actually you have to constantly be figuring out what it is that people will like next, the people that you're trying to sell to, what's important to them next, and figure out when to scale further. So like how do you keep moving up that chart when you're ready to move to a new market, to a new product, to a new segment, all those sorts of things.
It's an ongoing part of continuing to build a startup. So that's where I'll stop. If you want anything more from me, this is the Twitter and the timestack. And you can also just search for my name on LinkedIn. It will show up. Or you can go to bin.work. That's it. That doesn't say questions. Cool. I'll stop there. And if there are questions, I can answer questions. All right. Thank you.