Every so often I come across a podcast interview that is so profound and thoughtful that I stop everything and concentrate on the content. Last week I listened to an interview with Rahul Vohra, the founder of Superhuman, and it was one such show. There were a number of takeaways over the course of an hour, but the most interesting part for me was about thinking through product/market fit.
Here are a few notes on Rahul’s ideas starting around the 13 minute mark:
- Raise as much capital as you can upfront
- Make decisions as long term as you can
- Don’t put out a minimum viable product, put out a maximally delightful product
- Listen very closely to your users
- Don’t fail fast, succeed inevitably
Here are a few notes on the Superhuman product/market fit engine:
- Ask your users “How would you feel if you could no longer use the product?” with possible answers Very Disappointed, Somewhat Disappointed, Not Disappointed
- Companies that struggle to grow always have less than 40% Very Disappointed
- Companies that grow easily have more than 40% Very Disappointed
- Predicts success better than Net Promoter Score
- Expand on the first question with three more:
- What type of person do you think would most benefit from the product?
- What is the main benefit you received from the product?
- How can we improve the product for you?
- Analyze the results to the first question “How would you feel if you could no longer use the product?”
- If startup is in the 5-15% Very Disappointed region, consider pivoting the product or market to find a higher scoring segment
- Find the High eXpectation Customer (HXC), the most discerning person in your target market, by going back to the survey results and taking all the users that said they would be Very Disappointed, then analyze their answers to the next question “What type of person do you think would most benefit from the product?” as happy users will use the words that most accurately reflect themselves, then use these words to create your own definition of HXC
- Go back to all your surveys, assign a persona to each response, then take the users that most love your product, and use them to narrow the market
- Superhuman was scoring less well with sales, engineering, and data science people but scoring well with managers and entrepreneurs
- Deliberately ignore the personas that aren’t HXCs (resegment where you don’t change the product, but do change the market)
- Repeat this process on a loop to further increase the focus on the HXCs ultimately driving an eventual Very Disappointed segment being greater than 40% of your users, and thus achieving product/market fit (Superhuman was able to increase their score from 22% to 58% over several quarters)
Every entrepreneur searching for product/market fit should go over and listen to the interview with Rahul Vohra. There’s no way to guarantee success, but this product/market fit engine from Superhuman is the best I’ve heard.
2 thoughts on “Running the Product/Market Fit Engine the Superhuman Way”
This is great! Asking HXCs about “What type of person do you think would most benefit from the product?” is genius. I see a lot of people simply ignoring bad NPS responses 🙈 Hopefully this new way of thinking catches on 🏆
Thank you for the summary!