(2020-08-04) Rahul Vohra Shares Superhumans Product Market Fit Framework

Rahul Vohra Shares Superhuman's Product Market Fit Framework. We had set up shop and started coding Superhuman in 2015. A year later, our team had grown to seven and we were still furiously coding. By the summer of 2017, we had reached 14 people — and we were still coding. I felt intense pressure to launch, from the team and also from within myself.

Note that in tweets he clarifies that he counts non-response as a negative response in his PMF/NPS surveys. Which roughly no other NPS users do. I agree with him.

I couldn’t just tell the team how I felt. These super-ambitious engineers had poured their hearts and souls into the product. I had no way of telling the team we weren’t ready, and worse yet, no strategy for getting out of the situation

the descriptions of product/market fit I found were immensely helpful for companies post-launch.

what if you could measure product/market fit? Because if you could measure product/market fit, then maybe you could optimize it. And then maybe you could systematically increase product/market fit until you achieved it.

ANCHORING AROUND A METRIC: A LEADING INDICATOR FOR PRODUCT/MARKET FIT

Sean Ellis had found a leading indicator: just ask users “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed.” (2009-11-30) Ellis The Startup Pyramid Via Product Market Fit

After benchmarking nearly a hundred startups with his customer development survey, Ellis found that the magic number was 40%. Companies that struggled to find growth almost always had less than 40% of users respond “very disappointed,” whereas companies with strong traction almost always exceeded that threshold.

We identified users who recently experienced the core of our product, following Ellis’ recommendation to focus on those who used the product at least twice in the last two weeks.

We then emailed these users a link to a Typeform survey

With the responses collected

only 22% opting for the “very disappointed” answer, it was clear that Superhuman had not reached product/market fit

FROM BENCHMARK TO ENGINE: THE FOUR-STEP MANUAL FOR OPTIMIZING PRODUCT/MARKET FIT

1) Segment to find your supporters and paint a picture of your high-expectation customers

use the "very disappointed" group of survey respondents as a lens to narrow the market

find pockets in which Superhuman might have better product/market fit

We then assigned a persona to each person who filled out a survey.

we focused on founders, managers, executives and business development  — temporarily ignoring all other personas.

By segmenting down to the very disappointed group that loved our product most, our product/market fit score jumped by 10%

I turned to Julie Supan’s high-expectation customer (HXC) framework as a tool

the most discerning person within your target demographic. Most importantly, they will enjoy your product for its greatest benefit and help spread the word

We took only users who would be very disappointed without our product and analyzed their responses to the second question in our survey: “What type of people do you think would most benefit from Superhuman?”

happy users will almost always describe themselves, not other people, using the words that matter most to them

Using our customers' words and Supan’s tips for building a profile, we crafted a rich and detailed vision of the Superhuman HXC:

With our HXC in mind, we had a tool to focus the entire company on serving that narrow segment better than anybody else.

2) Analyze feedback to convert on-the-fence users into fanatics.

we once again turned to the segment of those who would be very disappointed without our product. This time, we looked at their answers to the third question on our survey: “What is the main benefit you receive from Superhuman?”

throwing the responses into a word cloud, some common themes emerged: the users who loved our product most appreciated Superhuman for its speed, focus and keyboard shortcuts.

we turned our attention to figuring out how we could help more people love Superhuman.

This batch of not disappointed users should not impact your product strategy in any way. They’ll request distracting features, present ill-fitting use cases and probably be very vocal, all before they churn out and leave you with a mangled, muddled roadmap.

That leaves the users who would be somewhat disappointed without your product. On the one hand, the ‘somewhat’ indicates an opening.

From analyzing our third survey question, we knew that happy Superhuman users enjoyed speed as their main benefit, so we used this as a filter for the somewhat disappointed group:

Somewhat disappointed users for whom speed was the main benefit: we paid very close attention to this group, because our main benefit did resonate. Something  —  probably something small  —  held them back.

Focusing on this last group, we looked more closely at their answers to the fourth question on our survey: “How can we improve Superhuman for you?” This is what we saw:

After some analysis, we found that the main thing holding back our users was simple: our lack of a mobile app

Probing further, we found some less obvious and more interesting requests: integrations, attachment handling, calendaring, unified inbox, better search, read receipts and so on into the long tail.

3) Build your roadmap by doubling down on what users love and addressing what holds others back.

If you only double down on what users love, your product/market fit score won’t increase. If you only address what holds users back, your competition will likely overtake you

To double down on what our very disappointed users loved, half of our roadmap was devoted to the following themes:

To gain ground with our speed-loving-yet-somewhat-disappoin

To stack-rank amongst these initiatives, we used a very simple cost-impact analysis: we labelled each potential project as low/medium/high cost, and similarly low/medium/high impact. For the second half of the roadmap, addressing what held people back, the impact was clear from the number of requests any given improvement had. For the first half of the roadmap, doubling down on what people love, we had to intuit the impact

4) Repeat the process and make the product/market fit score the most important metric.

The percent of users who answered "very disappointed" quickly became our most important number.

We also refocused the product team, creating an OKR where the only key result was the very disappointed percentage so we could ensure that we continually increased our product/market fit.

After segmenting to focus on the very disappointed set of users, we were at 33%. Within just three quarters of our work to improve the product, the score nearly doubled to 58%.

Early adopters are more forgiving, and will enjoy your product's primary benefit despite its inevitable shortcomings. But as you push beyond this group, users become much more demanding, requiring feature parity with their current products. Your product/market fit score may well drop as a result.

you simply have to keep on improving the product as the pool of users expands.

Our team has grown to 22 people and our NPS has increased right alongside our product/market fit score.

If you would like to try this engine for yourself, checkout this interactive tool, with a sample of actual Superhuman results. You can see the word clouds change as you play around with it — and you can also put in your own data and use it to build your own product.


Edited:    |       |    Search Twitter for discussion