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• Superhuman’s PMF Holy Grail 💌

• Asteroids ☄️

• Trending Tools ⚒️

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Superhuman’s PMF Holy Grail 💌

Superhuman is an email client like Gmail - except faster, more powerful and better designed. 

Its founder, Rahul Vohra describes it succinctly:

Superhuman wants you to get to inbox zero. For $30/mo.

They’ve successfully built a premium consumer-focused SaaS product, but their product wasn’t perfect from the start. 

One of Superhuman’s standout achievements, beyond their stellar product, is their innovative Engine for finding Product-Market Fit (PMF) that has been pivotal in guiding the team to create a product that users truly want.

One of the most amazing things produced by Superhuman apart from their product is their Engine for finding PMF that guided the team to build a product that users truly want.

The Elusive Quest for Product Market Fit

For founders, achieving Product Market Fit is the holy grail.

If you have PMF - your product is working and is the talk of the town. However, if you do not have PMF, getting there should be the only goal. 

But the million dollar question is: How do you find PMF? 

Everyone understands the importance of it but when it comes to understanding what Product-Market Fit really is and how to find it, there really isn’t a playbook that one can follow. 

This was the same question plaguing Rahul Vohra. 

In a quest to tackle his own problems for Superhuman, he reversed engineered PMF to create an engine that could quantifiably tell how close (or far) was PMF instead of the traditional definition of a feeling you get when you have it. 

The Magic Metric

Sean Ellis, who ran early growth at Dropbox, LogMeln and Evenbrite proposed a simple survey question to ask users:

How would you feel if you could no longer use the product? 

And measure the percentage of people who answer: Very Disappointed.

After benchmarking nearly a hundred startups with his survey, Ellis had found that the magic number for the above question was 40%. 

Companies who had strong traction always had more than 40% of the customers respond Very Disappointed if they could no longer use the product and vice-versa.

Anchoring themselves around this metric, Superhuman created a four-step process to build an engine that helped them to find Product/Market Fit. 

They created a survey with the following questions that would form the backbone of their engine.

1. How would you feel if you could no longer use Superhuman?

2. What type of people do you think would most benefit from Superhuman?

3. What is the main benefit you receive from Superhuman?

4. How can we improve Superhuman for you?

Step 1: Segmentation

Not all users are created equal. As an early-stage startup, you might have attracted all kinds of users. 

However, at this stage, you have to find out the people for whom you are truly solving a problem better than everyone else or better yet, solving something no one else is solving.

This leads to the value in being hyper-focused on the right people - your perfect customers.

Superhuman found this category of users by looking at the answers to the survey’s first question:

How would you feel if you could no longer use Superhuman?

This is the response they got.

From their initial survey, it was evident that Superhuman still hadn’t reached PMF with only 22% respondents being Very Disappointed compared to the 40% magic number. 

However, focusing on this group of people who would be Very Disappointed if they couldn’t use Superhuman helped them define their HXC - High Expectation Customer. 

The term high-expectation customer, or HXC, was coined by Julie Supan — a G2M and positioning expert who’s worked with Airbnb, Dropbox, and Thumbtack.

To pinpoint Superhuman’s HXC, they took the users who would be Very Disappointed without their product and analyzed their answer to second question:

What type of people do you think would most benefit from Superhuman?

After analyzing the responses to this, the team realized that their HXCs were founders, managers and executives. 

Step 2: Analyze

Now that they had identified their HXCs, they needed to dig deeper to figure out why these people loved Superhuman - and how they could bump up more users into this segment. 

To understand why users loved Superhuman, they looked at the answers of the HXCs to the third question:

What is the main benefit you receive from Superhuman?

This allowed them to find clear themes around what people valued most - which they could then use to help answer the question - How can we convert more people into this HXC group?

This is the word cloud for the responses they received. 

It was quite apparent that the main benefit of Superhuman was speed, focus and keyboard shortcuts.

They turned their attention to figuring out how they could help more people love Superhuman. 

Their next step was counter-intuitive. Interestingly, they disregarded all the people who wouldn’t be disappointed without the product.

So they focused their attention to the Somewhat Disappointed group and within that group as well, they sub-segmented it to the users with whom Superhuman’s core values were resonating i.e. Speed and Focus.

This group of users essentially represented people who liked Superhuman’s main value proposition but there was just something else - probably something small - which was holding them back from being absolute fans of the product. 

They took out the fourth weapon in their arsenal and looked at the responses to the following question:

How can we improve Superhuman for you?

With the same word cloud exercise, they found some common themes such as the lack of a mobile app and integrations that was holding these users from becoming HXCs.

Step 3: Utilize the Feedback

Equipped with a clear understanding of what their main proposition was and what they needed to work on to push the Somewhat Disappointed users over the edge, the team dedicated half their time doubling down on what users already loved and the other half on addressing what’s holding others back.

This involved working on further improving speed, introducing more shortcuts and automations for their HXCs and working on a mobile app and integrations for their to be HXCs.

Step 4: The Golden Loop

It is pretty evident that doing this once itself is super valuable to guide in finding PMF. 

But repeating this as a system and creating a PMF engine? That’s GOLD.

Superhuman repeated the process and made the PMF score their most important metric. And reorienting the company around this metric paid off. Within just three quarters, their PMF score shot up to 58%.

Superhuman’s journey to Product-Market Fit is a masterclass for every startup.

By anchoring around a key metric, focusing on high-expectation customers, and continuously iterating based on targeted feedback, founders can transform their product’s market fit and ignite growth.

So, take a page from Superhuman’s playbook and start building your PMF engine today!

Also check it out here: Superhuman.

☄️ Asteroids ☄️

Built by Buymeacoffee’s Jijo Sunny, Voicenotes stands out with AI that queries past notes, creates summaries, and reformats notes into blogs, tweets, or emails. 🌟🌌

Apple might team up with Google to use the Gemini AI model for iPhone features, enhancing iOS with AI power! 📱✨

OnlyFans management firm accused of having employees talk to fans as influencers. Customers who paid to chat with influencers may have been confessing fetishes to someone else. 💬❌ 

⚒️ Trending Tools ⚒️

📽️ Backdrop - Expense management for Film, TV, Music & Events

🌐 Stunning - Build websites with AI in seconds for agencies

📹️ Creatify - The fastest way to create short video ads

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