Can you talk about any time early in Superhuman’s existence where it felt like dark days? If so, how did you motivate yourself and your team to push through?

rahulvohra's avatar
2 years ago

Hello! 👋

The hardest period, for sure, was about two years in.

We started coding Superhuman in 2015. A year later, we had grown to 7 people — and we were still coding. By the summer of 2017, we were 14 people — and we were still furiously coding!

I felt this incredible intense pressure to launch, both from the team and also from within myself. After all, my last startup had launched, scaled, and been acquired in less time! Yet here we were, two years in, and we still had not launched.

Deep down inside I knew — no matter how intensely I felt pressure— a launch would go very badly. I did not believe we had product-market fit.

And although I knew it, I couldn’t just say that to the team.  These are super-ambitious, hyper-intelligent engineers. They poured their hearts and souls into the product. I couldn't just bring a problem. I had to find the solution.

So in April of 2017, I started my search for the holy grail.  For a way to define product-market fit, for a metric to measure product-market fit, and for a methodology to systematically increase product-market fit.

I searched high and low. I read everything I could find. Spoke with all the experts. And then I came across Sean Ellis.

Sean ran growth in the early days at Dropbox, LogMeIn, and Eventbrite. He coined the term “growth hacker”.

Sean had found a leading indicator of product-market fit. One that is benchmarked and predictive.

Just ask your users this: “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed”.

After benchmarking 100s of startups, Sean found the companies that struggle to grow always get less than 40% very disappointed… and the companies that grow easily almost always get more than 40%.

In other words: if more than 40% of your users would be very disappointed without your product, you have initial product-market fit.

This metric is more objective than a feeling. This metric predicts success better than Net Promoter Score.

We took this metric to its logical conclusion. We now use it not only as a measure of product-market fit, but as the basis for a whole product-market fit engine. With this engine, we have a methodology for systematically increasing product-market fit. It even writes our roadmap for us.

Read more here!

3 points

This question is part of an AMA with Rahul Vohra.

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