INSIGHT #10: Silicon Valley Visionary Rahul Vohra Reveals His Method for Measuring Product-Market Fit That Helped Him Build an $825M Startup

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Hello Hello.

Thank you for having me.

Awesome.

And just a few words. Raoul is an experienced entrepreneur. He has sold his first startup actually to LinkedIn over a decade ago and then he started Superhuman, which has grown into quite well known and well established and well liked product as we found out in email and productivity space. Rahul is also angel investor, frequently appearing in also a lot of media publications such as Forbes, Fortune, and you might have heard him on quite a few podcasts that I know our listeners also listen to like Acquired this week in Startups or My first million. Our goal today is to dive into product market fit. In cases of when there's totally lack of product market fit, founders clearly will see that something's wrong, right? And then if there is a great fit and everybody's buying your product, it's flying off the shelves. Also you probably know you're on the right track and I was interested in just starting discussion and thinking like there's a large space in the middle probably when you know you're getting some traction and month on month growth is there, does it mean that you have achieved the product market fit? If you just see good growth numbers.

Having some traction and growing month over month is great. But I think we know that those things alone don't mean you have products market fit. We have to zoom out and ask ourselves what is products market fit? Well, I think we all also know that product market fit is the number one reason why startups succeed and the lack of products market fit is the number one reason why startups fail. And I think it's helpful to turn to what some of the luminaries founding figures, if you will, in our industry have said about it. So Paul Graham, the founder of Y Combinator would say it's when you made something that people want. Sam Altman would say it's when users spontaneously tell other people to use your product. But I think it's Marc Andreessen who has the most vivid definition. He would say you can always feel it when Product Market Fit is not happening. Customers aren't quite getting value, users aren't growing that fast, word of mouth isn't spreading, the press, reviews are kind of blah, blah, and the sales cycle is too damn long. But you can always feel it when Products Market Fit is happening. Customers are buying as fast as you can, add servers, you're hiring sales and support as fast as you can, reporters are calling you about your hot new thing, investors are staking out your house, and money is piling up in your checking account. And this is indeed a vivid definition and one that I was staring at through tears in the summer of 2017 because it seems so subjective, so unactionable. What do you do if, by this definition, you don't have Products Market Fit? Indeed. Can you measure Products Market fit? Because if you can, then maybe you can optimize it. Maybe you can systematically, even numerically, increase Products Market Fit. And as it turns out, the answer is yes, you can measure Products Market fit and you can optimize it.

In order to even get there, I would say probably you need to go through a lot of iterations, for example, and there's also a lot of things that you need to learn and failing to learn would mean that you're not making progress. So maybe to start with that. So, for example, if you're doing this iterations and testing, what have you observed? What helps you to retain learnings and not to do the just empty cycles, but actually make progress with every cycle in terms of how to structure the team, what to tell the team, what to pay attention to?

Gosh, well, that's a very human journey. How do you make sure that you're iterating whilst you're learning, whilst you're structuring the team? And for folks who want the full process, which we probably don't have time to get into, it's available online, if you search on the web, if you Google for Superhuman Products Market Fit engine, there's actually an algorithmic process that I describe that will get you to Product Market Fit, with also tips on how to structure your team. But there's a human journey behind this that I think everyone would find interesting. And this isn't really written in the article, this is the process by which we got to the Product Market Fit engine and we need to understand where Superhuman came from. So all the way back in 2010, as you noted at the top of the episode, I started a company, and it was called Rapportive. We built the first Gmail plugin to scale to millions of users. When people emailed you, we showed you what they look like, where they work, their recent tweets, links to their social profiles. And we grew rapidly. And two years later, we were acquired by LinkedIn. Now, during those four years, I developed a very intimate view of email. I could see Gmail getting worse every single year, becoming more cluttered, using more memory, consuming more cpu, slowing down, your machine, still not working properly offline. And on top of this, people were installing plugins like ours reported. But you'll remember Boomerang and Mixmax and yesware, and you name it, people had these things. And each plugin took those problems of clutter, memory, CPU performance offline and made all of them dramatically worse. So we decided it was time for change. We imagined an email experience that is blazingly fast, where searches are instant, where every interaction is 100 milliseconds or less. An experience where you could do everything from your keyboard, I should say, so you could fly through your inbox. An experience triaged by AI so you could respond faster to what matters most. An experience that helps you follow up on time every time you would never drop the ball again. An experience that just worked offline so you could be productive anywhere. An experience that had all the features of the best Gmail plugins but built in natively. And an email experience that was subtle, minimal, and visually gorgeous. So with that, we built Superhuman. And today, as you said, Superhuman is the fastest email experience ever made. Our customers are getting through their inbox twice as fast as before. They're replying to important emails 8 to 13 hours sooner, and they're saving 4 hours or more every single week. Many of them actually see Inbox Zero for the first time in years. That was the beginning, and that's where we are today. And there's a very messy middle. So let's go back to the summer of 2015, because we started, much like any other software company, by writing code. And in the summer of 2016, we were still coding. And in the summer of 2017, we were still coding. I felt this incredible, intense pressure to launch, both from the team and also from within myself. After all, my last company reported had launched, scaled, and been acquired in less time. And yet here we were, two years in, and we still had not launched. But deep down inside, I knew no matter how intensely I felt this pressure, a launch would go very badly. I didn't believe we had products market fit. And although I knew it, I couldn't just say that to the team because these are super ambitious hyper intelligent engineers. They poured their hearts and souls into the product. I needed a plan. And so in 2017, two years in, I started my search for the holy Grail. For a way to define products market fit, for a metric to measure products market fit, and for a methodology to systematically increase products market fit. I searched high and low, I read everything I could find. I spoke with all the experts and I came across this guy, Sean Ellis and you might know the name Sean Rangroth in the early days at Dropbox, LogMeIn and Eventsprite. He coins the term growth hacker and he found a leading indicator of products market fits, one that is benchmarked and predictive. You just ask your users how would you feel if you could no longer use the product and measure the percent? That answer very disappointed. You give them three options. Not disappointed, somewhat and very. And after benchmarking hundreds of startups, dawn found that the companies that struggled to grow almost always got less than 40% very disappointed. Whereas the companies that grew most easily almost always got more than 40% very disappointed. In other words, if more than 40% of your users would be very disappointed without your product, you have initial products market fit and this metric is more objective than a feeling. It predicts success better than net promoter score. It's not only the best way to measure products market fit, you can actually use it to develop your very own products market fit engine. And it's with that engine that you now have a methodology for systematically increasing the metric. And the engine actually even automatically generates your roadmap for you. A roadmap that is guaranteed to increase product market fit.

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