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Product/Market Fit Engine
Fetchit Survey

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Analyze feedback

Judiciously use feedback to convert ambivalent users into people who love your product.
Now that your segments and personas are defined, you can analyze your data to discover (1) why people love your product, and (2) how you can help more people love your product. You can then funnel these insights back into your focusing on shifting the "somewhat disappointed" to become enthusiastic advocates.

A. Segment to target personas

Examine the users who would be very disappointed without your product — these are the people who most love your product — and use their personas to narrow the market. In our case, our very disappointed group was 22% and contained primarily founders, managers, executives, and business development. We focused entirely on these personas, and temporarily ignored all others. Just by segmenting down to these personas, our product/market fit scored jumped by 10% from 22% to 33%!
You can toggle the personas below to see how changing the market affects the product/market fit score. This is much more efficient than changing your product! Once you are ready, you can replace the personas with your own.
Instructions: Check the Is Target Persona boxes below to decide which personas to include in your target market. Then, look at the charts below to determine if you have product market fit within your selected basket of personas.
Is Target Persona?
% Very Disappointed
% of Responses
🖊️ Highschool
📕 City College - 2 Year
👓 University - 4 Year
📒 Private University - 4 Year
🥼 Specialized University - 4 Year
There are no rows in this table

Survey Results

The important number to look at here is the “very disappointed” percentage. If it’s over 40%, you have product-market fit.
Product market fit threshold:
Selected personas above:
% very disappointed →
You've achieved product/market fit 😃

All survey respondents:
% very disappointed →
You've achieved product/market fit 😃

Segmenting to target personas is usually not enough. In our case, we were still below the 40% threshold, and so we needed to understand why these users really loved Superhuman—and how we could help more users love Superhuman.
I found it helpful to focus on these key questions:
Why do people love the product?
What holds people back from loving the product?

B. Why do people love the product?

Method: What benefits do very disappointed users enjoy?
To understand why people love the product, we take the users who would be very disappointed without the product, and analyze their responses to question #3: "What is the main benefit you receive from Superhuman?". I like to collect these results into a word cloud like the one below. When you do, some themes will emerge. For us, it became obvious: users love Superhuman for its speed, focus, and keyboard shortcuts.

"Main Benefit" responses from target persona users who would be
Very disappointed
if they couldn't use Superhuman anymore:

C. What holds people back from loving the product?

Method: What improvements do somewhat disappointed users want?
To understand what holds people back from loving the product, we take the users who would be somewhat disappointed without the product, and focus on the subset for whom the main benefit of the product matches the theme we just identified in the previous step — in our case, speed. (These are the users who are on the verge of loving the product, but something — and likely something small — is holding them back.) We then analyze their responses to question #4: "How can we improve Superhuman for you?".
You can use the keyword search below to explore how Superhuman's benefits corresponded to areas of improvement.

Segment the "somewhat disappointed" responses by your main benefit keyword

Enter a keyword for the main benefit you would like to analyze:
Segmented score
Count of responses
Very disappointed
Not disappointed
Target: Somewhat disappointed (main benefit includes your keyword)
Non-Target: Somewhat disappointed (main benefit does not include your keyword)
There are no rows in this table

Word cloud of responses to "How can we improve Superhuman for you?" from somewhat disappointed users who found the main benefit

In 2015, the heyday of "mobile-first", we had taken the contrarian approach of starting on desktop. Most emails are sent from desktop, so that is where we thought we could add most value. We were always planning on building a mobile app, but at the start — like every other startup — we had the chips for just one bet. In 2017, it was clear that we delay this no longer, and that mobile had become critical for product/market fit. This leads straight to our next step: the !

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