412: Rahul Vohra, Head of Superhuman Mail: From Onboarding Users by Hand to an AI-Native Productivity Suite

00:11:34 · Rahul VohraPlay from here

And actually, not just parity, a polish that goes way beyond those products. Then we started looking at the the metrics of those users. And on every single metric that matters, whether it was churn, retention, net promoter score, product market fit score, virality, we were seeing a best in category numbers. And I was like, wow, is this the product? Is it the onboarding? Is it me? Am am I just that charming in these meetings? And so we started to change each variable one by one to disambiguate. And that the first thing we did was I no longer did the onboardings. So I asked our head of growth, actually, my my brother, who's well known now for for doing growth consulting to to to many amazing startups. And I was like, hey, Gaurav, you go do the onboardings. In fact, instead of five or 10 per week, why don't you do 20 per week? And I started sending him around San Francisco, and he was doing the exact same playbook I was doing. And his numbers were great too. And I was like, okay. It's not just me. Maybe it's him. We should hire a team of people to do this. Not like go crazy, just like a few people. And whenever you're doing this, you wanna hire more than one person, just just in case there's a false negative due to the person not being particularly good. So I think we hired two or three people simultaneously to start doing these onboardings. One of them didn't work out, but the other two had phenomenal numbers, almost as good as the numbers that we were putting out to begin with. And then I was like, well, we've got these calls down to half an hour each. I think we can design a schedule where folks do 44 or 45 of these calls per week. And I I did the the back of the envelope napkin math. And I was like, wow. This this actually works out. This this could be a whole new go to market motion that no one has actually ever really come up with before. We have this huge wait list of people who want access. So why don't we hire incredibly compelling people who are experts in productivity, who know email and productivity products inside out, can help our users become fully onboarded in a matter of minutes, something that no other software to my knowledge has tried to do at this scale. And so we hired a whole bunch of people, and we scaled that team up. And that became known as the the Superhuman Onboarding, which is the thing that you experienced.

00:14:03 · Rahul Vohra, Dan TurchinPlay from here

And that is the thing that, as you know, many companies since then have then copied. And to this day, I I still get founders asking, wait. What are all the the tips and tricks? How exactly did you do the Superhuman onboarding, and and how can we make that work for us? So it's an incredibly powerful technique. It doesn't work for every product, but for the products where it's well suited, it can have an outsized impact. [00:14:30] S1: So you didn't get your PhD in computer science. You famously dropped out. But I'm gonna declare right here that you got your PhD in another CS, customer success. [00:14:39] S0: Pretty much. Maybe. I... I'm a huge believer in the the customer is always right, or at the very least, they always have something to say. They may not be right. And it's our responsibility as founders, as builders, as leaders to take that feedback, whatever it is, and to figure out what to do with it, to interpret it. And so we built so many systems around doing just that. [00:15:02] S1: Early on, you sought the advice of arguably the founder of the growth hacking movement, a gentleman named Sean Ellis, who told you about what I think later became, like, the Sean Ellis rule, how to evaluate the success of customers with products. If you wouldn't mind, I think we've benefited our audience just to hear a little bit about how Sean thought about measuring growth and and some of the lessons that you took into Superhuman.

00:15:30 · Rahul VohraPlay from here

[00:15:30] S0: For sure. So this is where our product market fit engine came to be. And at Superhuman, we built our very own product market fit engine. The engine gives you a way to define products market fit, a metric to measure products market fit. It also even gives you a methodology to increase products market fit, and it can even write your roadmap for you. And that's a roadmap that will take you to products market fit over time. But in order to understand how it works, it's probably worth starting with what even is product market fit. It... You know, it's a phrase that gets thrown around, but what actually is it? Well, it turns out to be the number one reason why startups succeed. And the lack of product market fit, on the other hand, turns out to be the number one reason why startups fail. It was clearly super important. Paul Graham, who's the founder of Y Combinator, would say that it's when you made something people you want. Sam Altman would say it's when users spontaneously tell other people to use your product. But in my opinion, it is actually Marc Andreessen who has said 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 is not spreading. The press reviews are kind of blah, and the sales cycle takes too damn long. But you can almost always feel it when products market fit is happening because customers are buying as fast as you can add servers. You're hiring sales and support as fast as you can. Reporters are constantly calling you about your hot new thing. Investors are staking out outside your house and money is piling up in your checking account. And that is indeed a very vivid definition. It was one that I was staring at through tears in the summer of twenty seventeen because it seemed so subjective, so unactionable. What do you do if by that definition you don't have products market fit? Indeed, can you measure products market fit? Because if you can, then maybe you could optimize it. Maybe you could systematically even numerically increase it. So in the summer of twenty fifteen, we'd started much like any other software company by writing code. And in the summer of twenty sixteen, we were still coding. In the summer of twenty seventeen, we were still coding.

00:17:49 · Rahul VohraPlay from here

And I felt this incredible intense pressure to launch, both from the team, but also from within myself. Because after all, my last company that we talked about, Rapportive, that had launched, scaled, and been acquired in less time, And here we were two years in and we still hadn't launched. But deep down inside, I knew that no matter how intensely I felt pressure, a launch would go very badly. In other words, 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 put their hearts and souls into the product. So I needed a plan, and that's where Sean Ellis came in. In the April 2017, I started my search for that holy grail for a way to define products market fit, for a metric to measure it, and for a methodology to systematically increase it. And I searched high and low. I read everything I could find. I spoke literally with all the experts, and then I came across Sean. Now, he ran growth in the early days of Dropbox, LogMeIn, and Eventbrite. As you noted, he coined the term growth hacker. And he found a leading indicator, a product market fit, one that is benchmarked and predictive. You just ask your users the following question. How would you feel if you could no longer use the product and measure the percent who answered very disappointed? And after benchmarking hundreds of startups, Sean found that the companies who struggle to grow almost always got less than 40% very disappointed, whereas the companies that grew most easily almost always had more than 40%. So in other words, if more than 40% of your users would be very disappointed without your product, you have initial product market fit. And with that engine, we now have a methodology for systematically increasing product market fit. It even automatically generates our roadmap for us. The metric is even more objective than a feeling. It predicts success better than Net Promoter Score, and it's not only the best way to measure product market fit. We use it to develop our very own products market fit engine. And with this engine, we now have a methodology for systematically increasing product market fit, and it even automatically generates our roadmap for us. And if you look through the article, which goes through this in-depth, it's basically guaranteed to increase product market fit.

00:20:10 · Rahul Vohra, Dan TurchinPlay from here

So I rather infamously wrote this up. I think it was in 2019 or 2020 on First Round Review. And then since then, it's become the most shared entrepreneurship article of all time. So maybe we can put the the the link in in the notes for our listeners here. But I go into a lot of detail how you can build on Sean's question, build a whole algorithm around it, which, as I said, actually takes you to Product Market Fit. [00:20:35] S1: Great. That's what I wanted the audience to hear, and thank you. We we will share that link. I've heard you talk about, after Rapportive, your mission was to pick an impactful problem and go go about solving it. You already had a lot of success. I'm sure, you know, there's no shortage of things you could do. I would say probably, you know, on a on a short list of technologies that have impacted humanity, maybe after fire in the wheel, I'd say email is a close one. So it it obvious why eventually you decided that that would be the broken thing that you'd fix. Something I've never heard you talk about is what that process was like, and if you could share any examples of other big problems that you chose not to fix in order to focus on email.

00:21:25 · Rahul VohraPlay from here

[00:21:25] S0: Yeah. Well, as you noted, when I started Superhuman, I wanted to solve the biggest possible problem. And email, of course, turns out to be a way bigger problem than most people realize. And it hit me just after I'd sold Rapportive to LinkedIn. I was commuting from work in Mountain View, that's where LinkedIn is headquartered, to where I live in San Francisco. And that's, if in case you haven't done that, roughly a one hour journey. But this was 2014 and both Uber and Lyft were having their moment. So I happened to be in the back of an Uber. And on this trip, I finished writing some documents, I cleared out my email, I checked my calendar for tomorrow, I made dinner plans with my girlfriend and I even called my mother. So I was simultaneously being a productive employee, a proactive boyfriend, and a present son. And I realized in that moment that the real magic of Uber was not commuting. It was not about getting from a to b. It was about time, being able to do these other things. So I asked myself the question, where do we spend all of our time? And this is a little bit of a trick question, but but I'll ask you. Can you guess the one thing that professionals spend the most time doing? That's not email. Scheduling. Scheduling is a very good guess because that's up there with email. It's a little bit less time than email because a lot of email actually is scheduling, but it is in fact sleeping. We all spend more time sleeping than we do basically any other activity in our life. And as a lifelong sufferer of sleep apnea, it hits me particularly hard. And, I I thought a lot about that, But I also had no real idea how to fix it. And after sleep, it of course turns out to be email. In fact, there are roughly 1,000,000,000 professionals in the world and on average, spend three hours a day reading and writing email. So that's three billion hours every single day or north of a trillion hours every single year that go into email. And that's how I knew it was a problem worth solving. I couldn't imagine something bigger than a trillion hours per year. Now alongside this, I could see Gmail and Outlook getting worse every single year becoming more cluttered, using more memory, consuming more CPUs, slowing down your machine, still not working properly offline.