How PLG Achieves PMF: The Case of Superhuman

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Superhuman is an email client that explores product direction in its early days and realizes Product
The process of Market Fit is very helpful for many PLG products now.

Determine whether the product reaches PMF
There are many standards, both qualitative and quantitative, among which Sean Ellis, the father of growth hacking
An important indicator of recommendations is to ask users “if you can no longer use this product”, the percentage of users who will be “very disappointed”, if 40%
of users will be “very disappointed”, which means that the product has a good reputation in the market

To this end, Superhuman
Four questions were designed to be sent to users who have used the product at least twice in the past two weeks:

  1. if you can no longer use
    Superhuman, how would you feel? A) very disappointed B) somewhat disappointed C)
    not disappointed
  2. What type of person do you think would be best from Superhuman
    benefit from it?
  3. you from Superhuman
    What are the main benefits you get?
  4. How we can improve for you

Don’t think that these four questions are just simple user questionnaires. In fact, the four questions are interlinked. Through data analysis and user classification, the simple four questions can help the team find the next direction.

The initial results were: 22% were very disappointed, 52% were somewhat disappointed, and 26% were not disappointed. Distance 40%
There is still a certain gap, so the next goal is very simple, to increase the proportion of “very disappointed” users.

Identify product advocates and build profiles of high-expectation users

The first step is actually to know what kind of customers are the real supporters of your product, and such users are the users you should really serve. Because through early marketing you may have absorbed a variety of users, many of whom may not be the real target users.

According to the answer to the first question, the user’s occupation is grouped (the original text does not say how to obtain the user’s occupation, I guess it is the onboarding when the new user registers
collected during the process):

  • twenty two%
    Very Disappointed: Founder, Manager, Executive, BD
  • 52%
    A bit disappointed: Sales, Founder, Customer Success, Engineer, Manager, BD
  • 26%
    Not Disappointed: Engineers, Data Scientists, Executives, Sales, BD

At this time, I have clearly seen the high-expectation user portraits: founders, managers, executives, BDs. By focusing on serving users of this portrait, the scores have changed: 32%
Very disappointed, 45% somewhat disappointed, 23% not disappointed.

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

Happy users will almost always describe themselves, not others, in the words that matter most to them. This lets you know who the product works for, and the language that resonates with them (and also provides valuable insight into your marketing copy).

A rich and specific high-expectation user persona has been established:

Nicole is a hard working professional who works with many people. For example, she might be an executive, founder, manager, or in business development. Nicole works long hours and often works into the weekend. She considers herself busy and wishes she had more time. Nicole feels like she’s very productive, but she’s self-aware enough that she can do better and occasionally researches how to improve. She spends most of her day in her inbox, typically reading 100-200 emails and sending 15-40 (up to 80 in very busy situations). Nicole sees responsiveness as part of her job, and she’s proud to be able to do it. She knows that a slow response can hinder her team, damage her reputation, or lead to missed opportunities. Her goal is to reach zero inboxes, but only two or three times a week at most. She usually has a growth mindset. While she’s open to new products and keeps pace with technology, she probably has a fixed mindset about email. While open to a new mail client, she was skeptical that the new client would be faster.

Analyzing feedback to convert users on the sidelines into fanatics

Attracting more avid users requires figuring out two things first:

  • why people like
  • What’s holding back people from liking it?

To this end, Superhuman
Looking again at the “Very Disappointed” user’s response to the third question: “You from Superhuman
What are the main benefits you get? “

The team threw these responses into a “word cloud” and found that the key words were mainly “speed”, “focus mode” and “keyboard shortcuts”.

Next, the team ignores users who will not be disappointed if they can’t use the product. They believe that this group of users who are not disappointed should not influence your product strategy in any way, and that meeting their needs will instead seek
went astray in the process.

Therefore, the “somewhat disappointed” users are divided into two parts:

  • The primary benefit is not “fast”: ignore them because the primary benefit of the product doesn’t resonate, and they’re unlikely to fall in love with the product even after building everything they want.
  • The main benefit is also “fast”: a lot of focus on this group because the main benefit really resonates. But something — maybe a little something — was holding them back from their love for the product.

So study their answer to the fourth question: “How can we improve for you
Superhuman? “

After the same word cloud analysis, the answer stood out: “mobile version”. At this point the team realized that the mobile version of the product had become a hindrance
the elements of.

Digging further, the team found some less obvious but more interesting requirements: integration, attachment handling, calendaring, unified inbox, better search, reading receipts, and more. Internal
Calendar isn’t used a lot, and based on e-mail intuition, the team doesn’t prioritize calendars at all. As such, this process of mining feedback greatly elevates the calendar’s place on the product priority list.

After a clear understanding of key strengths and missing features, Superhuman
All it takes is to feed those insights back into how to build the product. Feedback from implementing this segmentation will help users who are a little disappointed to get out of the crowd and into the ranks of enthusiastic advocates.

Build product roadmaps by doubling down on what users love and solving problems that hold them back

If you only go the extra mile for what your users love, your
PMF scores will not improve. If you only solve the problems that hold your users back, your competitors are likely to overtake you. This insight guides Superhuman
The product planning process for:

  • Going the extra mile on what “very disappointed” users love: more speed, more shortcuts, more automation…
  • To win over users who like speed but are “a little disappointed”: mobile apps are developed, integrations are added, attachment handling, calendar functionality…

Next is ordering those requirements: the team labels each potential requirement as low/medium/high cost, and equally low/medium/high impact, and prioritizes addressing low-cost, high-impact requirements.

For the second half of the roadmap, solving problems that hold people back, the impact can be seen in the number of requests for any particular improvement; for the first half of the roadmap, going the extra mile on what people love, one has to intuit its Influence, aka the role of “product intuition”, requires experience and a deep empathy for the user (the previous high-expectation personas exercise goes a long way toward developing this ability).

Repeat the process with PMF score as the most important metric

Over time, the team continually surveys new users to track
How PMF scores have changed. (The team made sure not to survey users more than once to avoid affecting the 40%

The percentage of users who answered “very disappointed” quickly became the most important number. This is the most compelling metric, and the team tracks it weekly, monthly, and quarterly. To make this metric easier to measure, the team built some custom tools that constantly survey new users and update the total number for each time period. Superhuman
Also adjusted the focus of the product team, creating a
OKRs, where the only key result is a percentage of “Very Disappointing”, which ensures continuous improvement of product/market fit.

around this single indicator pair
Superhuman’s repositioning has paid off. When the team began this journey in the summer of 2017, the PMF score was
twenty two%. After a breakdown, focusing on the “very disappointed” user base, the score was 33%. In just three quarters of work to improve the product, the score nearly doubled to reach

Finally, the founder shared two

  1. Investors advising early-stage teams should avoid PMF
    Before driving growth, the result was disaster. Pressure to grow prematurely is all too common, and startups need time and space to find their fit and launch the right way.
  2. for anyone looking to find
    For the founders of PMF, this engine should be re-tuned according to their own situation. When you finally reach the PMF you’re after
    , my advice is to keep the pedal down and go as fast as you can.


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