FitSignal
Resource · The PMF Method

The 40% benchmark,
explained in plain terms.

Everything FitSignal measures is built on a published, battle-tested methodology — the framework Rahul Vohra developed at Superhuman in 2018. This guide explains how it works, why the threshold is 40%, and what to do with your result.

< 25%
Keep searching

The market doesn’t need this yet — at least not this audience. Change the segment, the problem, or the product before optimizing anything.

25–40%
Close — segment harder

Somebody here loves you. Find the persona already above 40%, focus the product on them, and let the rest go for now.

40%+
You have fit — compound it

Companies above this line went on to scale; those below stalled. Protect what your core loves and fix the blockers of the almost-convinced.

The core idea

Measure disappointment, not satisfaction.

Satisfaction questions invite politeness. The Superhuman method instead asks users to imagine loss: “How would you feel if you could no longer use this product?” People who would be very disappointed have organized part of their work or life around you — that’s what fit actually is. Vohra benchmarked hundreds of startups and found 40% “very disappointed” was the line that separated the ones that scaled from the ones that stalled.

Two rules keep the number meaningful: survey users after real usage (at least two product sessions, typically two weeks in), and never ask the same person twice — repeat answers regress toward politeness. FitSignal enforces both automatically.

The instrument

Seven questions, each with a job.

Q1
How would you feel if you could no longer use it?
The PMF question. Very / somewhat / not disappointed — the only scored answer.
→ your score
Q2
Why did you choose that answer?
Context for everything else. The sentence behind the checkbox.
→ context
Q3
What would you use as an alternative?
Your real competitive set — usually not who you think it is.
→ competitors
Q4
What is the main benefit you receive?
From “very disappointed” users only: this is why people love you. Protect it.
→ love cloud
Q5
What type of person benefits most?
Your users describe your ideal customer better than you can.
→ ICP
Q6
How can we improve for you?
From “somewhat disappointed” users: the blockers worth fixing, ranked by the AI analysis.
→ roadmap
Q7
What’s your job title?
Feeds persona assignment, so every other answer can be segmented.
→ personas
After the score

The four-step improvement playbook.

1
Segment to find your beachhead

Slice the score by persona. Somewhere in your data is a segment already above 40% — that’s who you’re building for now.

2
Name what your core loves

Use the love cloud (Q4, very disappointed only). This is the main benefit — every roadmap decision must protect it.

3
Fix the right blockers only

Listen to “somewhat disappointed” users who already want your main benefit — and politely ignore the rest. That’s what the linked blocker cloud and impact ranking compute for you.

4
Re-measure the next cohort

Ship, then survey newly eligible users — never repeats. Watch the trend line, not any single week’s number.

Sources & further reading
  • Rahul Vohra — “How Superhuman Built an Engine to Find Product/Market Fit” (First Round Review, 2018). The original framework this product implements.
  • Sean Ellis — the original “very disappointed” survey question and the 40% observation across his startup portfolio.
  • Fred Reichheld / Bain & Company — the Net Promoter® methodology behind our NPS® surveys.

Theory’s done. Measure for real.

Start free →See the survey feature