The elusive product market fit…
If you are like me (who follows A16z, YC, Greylock, FirstRound Capital and the rest of the startup community fervently), you’d probably come across the term “product/market fit” more frequently than you’d like.
Just like what Dan Olsen (Author of “The Lean Product Playbook”) said, the phrase has been treated as a binary post-hoc evaluation of an startup. xxx failed because it didn’t have product market fit; xxx succeeded because it achieved great product market fit. For those of us who are in the daily going-ons of things, the concept isn’t exactly prescriptive. Dan Olsen spoke of how he approached it to make it more useful for the rest of us, which I really like. However, it is really this article from First Round capital that hits it home for me.
“How Superhuman Built an Engine to Find product market fit”
The article itself focuses on Superhuman, a startup founded by Rahul Vohra and funded the likes of Andreesen Horowitz, FirstRound. It is a souped up version of email client, currently in closed beta. I tried getting access but I am still on the waitlist. Maybe their product is really as amazing as they it is but I haven’t really tested myself to really claim that. For those who are interested, here’s their website: https://superhuman.com/
What I really want to discuss here is their framework in quantitatively approach the concept of “Product Market” fit and transformed it into a leading indicator for future growth.
As Rahul attested, the original product market fit leading metric was found by Sean Ellis:
Ask the users “how would you feel if you could no longer user the product?” and measure the percentage who answer very disappointed.
The magic number is 40%. If 40% of your customers would be “very disappointed” if your product is gone, then there’s a strong indication that you’d grow. If less than that, you will not.
1. Benchmark
For Slack, that number was 51%. Superhuman took hint and sent out a simple survey with the following questions to benchmark their level of product market fit.
- How would you feel if you could no longer use SuperHuman? (A. Very disappointed, somewhat disappointed, not disappointed)
- What type of people do you think would most benefit from superhuman?
- What is the main benefit you receive from SuperHuman?
- How can we improve SuperHuman for you?
After the survey went out, only 22% of Superhuman user based answered “Very Disappointed”.
2. Framework to optimize Product Market Fit based on the Benchmark (The creation of the product market fit engine)
This framework has 4 components:
A. Segment to find supporters and paint a picture of your high-expectation customers
Use the “very disappointed” segment of the user base to narrow the lens of the market, the data can help pinpoint where the product has a better product market fit.
After publishing the survey, the SuperHuman team assigned persona to each who filled out a survey. In this first step of the analysis, SuperHuman focused on the persona that are in the very disappointed group, temporarily ignored the others. The team then focused only on the personas that were in the very disappointed group. That means, the team extrapolated the personas and looked for the same persona in their entire user data base.
The next task for the SuperHuman team wanted to better understand users who really love the product. They leveraged Julie Supan’s High expectation customer framework (I don’t know what it is but I will definitely write about it and learn from it in a subsequent article).
The designation of HXC (high expectation customer) isn’t an all encompassing persona but rather the most discerning person within the target demographic. Most importantly, they will enjoy the product for its greatest benefit and help spread the word.
The reason to go with this approach is that “It is better to make something that a small number of people want a large amount, rather than a product that a large number of people want a small amount.” The product market fit process is to narrow the market massively optimize for product that a small number of people want a large amount.
B. Analyze feedback to convert on the fence users into fanatics.
This step is to convert more users into the fanatics segment by answering:
- Why do people love the product in the HXC segment?
- What holds people back from loving the product?
In the survey, the third question “what is the main benefit you receive from SuperHuman?” The SuperHuman team looked to the answers of the disappointed power users’ answers .
The SuperHuman team analyzed the responses and generated a word Cloud that looks like this:
Armed with this knowledge of the product’s appeal, the team turned their attention to figure out how to help more people love SuperHuman. At this point, the disappointed users don’t matter because:
- They shouldn’t impact the product strategy
- They request distracting features
- They present ill fitting use cases
- They probably are very vocal, before churning out and leave you with a mangled, muddled roadmap
The somewhat disappointed group indicates an opening. They may or may not fall in love with the product. Now to further focus on this group, try and understand within the somewhat disappointed group, who enjoyed the product for the main benefit that align with the set of users who are very disappointed.
The rationale here is that probably only a small improvement dedicated to these users can help them really fall in love with the product. And use the fourth survey question to guide what can be done to make them fall in love “How can we improve SuperHuman for you?”
This created the following WordCloud:
C. Build roadmap by doubling down on what users love and addressing what holds others back.
Superhuman team decided that if only focusing on what users love, the product market fit score won’t increase. If only focusing on what’s holding the users back, the competition would catch up. So from a Roadmap perspective, half of the effort was around doubling down the features people love and the other half is to address what hold them back.
D. Repeat the process and make the product/market fit score the most important metric
The rest is to repeat and reorient around this single metric.
