Product Market Fit Survey designed for scaling products
A Product Market Fit Survey (PMF survey) helps product leaders answer the most important question in product development: Do customers actually need this product or could they live without it?
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HintGo is an AI-first product market fit survey tool and product market fit survey software built for startup founders, product leaders, and product teams.
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How to calculate your Product Market Fit score (PMF scoring)
If you want to track product market fit over time or compare against a PMF benchmark, you need consistent PMF scoring.
This section is your how to calculate PMF score guide and the same logic used by our built-in PMF calculator and PMF score calculator.
1
Ask the Product Market Fit question (PMF questionnaire standard)
Run the same core question in your product market fit survey every time:
"How would you feel if you could no longer use [Product]?"
Standard response options:
- • Very disappointed
- • Somewhat disappointed
- • Not disappointed
- • N/A I no longer use the product (optional)
This is the standard product market fit questionnaire used for benchmarkable PMF scoring.
2
Count "Very disappointed" responses
For product market fit scoring, the key signal is the share of respondents who select Very disappointed.
Example with 50 responses:
3
Calculate the PMF score (Product Market Fit scoring)
Divide the number of Very disappointed responses by total responses (excluding N/A), then multiply by 100.
PMF score = (Very disappointed ÷ Total responses) × 100
Example:
This is the standard PMF scoring model and the foundation of any PMF calculator, PMF score calculator, or product market fit calculator.
4
Interpret the Product Market Fit benchmark
A widely used product market fit benchmark is the 40% test:
PMF score < 40%
→ PMF not achieved yet (iterate and narrow)
PMF score ≥ 40%
→ strong PMF signal (prepare to scale)
Product Market Fit Survey Templates
Launch faster with a proven product market fit survey template. Every HintGo PMF survey template includes the standard product market fit questionnaire, adaptive follow-up product market fit survey questions, and built-in summaries and routing.
Here are PMF survey templates commonly used by product teams and founders:
HintGo vs alternatives
A product market fit survey is easy to launch with almost any tool. The real difference is what happens after the PMF score.
Standard PMF survey question
Follow-up questions after PMF score
Summarize insights into themes
Route insights to the right teams
Ongoing triage effort
Start Tracking your Product Market Fit (PMF) with HintGo
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Product Market Fit Survey Best Practices
Common questions about running a product market fit survey and using PMF scoring effectively.
When should I run a Product Market Fit Survey?
Run a PMF survey only after users have experienced core value. Common moments include:
- after a major product iteration or pivot
- when retention plateaus
- before scaling GTM or fundraising
- after onboarding improvements have stabilized
Who should receive the PMF questionnaire?
Survey users who:
- have used the product at least twice
- recently experienced core value
- represent your intended target segment
This avoids misleading PMF scores.
How many responses do I need?
Most teams aim for 40–50 qualified responses for a directional PMF read. Quality matters more than volume.
What's a good Product Market Fit benchmark?
The most common PMF benchmark is the 40% test:
- 40%+ "Very disappointed" = strong PMF signal
- Below 40% = iterate and narrow focus
What Product Market Fit survey questions should I ask?
In addition to the core PMF question, common follow-up product market fit survey questions include:
- "What type of people would benefit most from this product?"
- "What is the main value you get from the product?"
- "How could we improve the product for you?"
ℹ️HintGo uses AI to adapt follow-up PMF questions based on responses, helping product teams learn faster without increasing survey length.
What should I do if my PMF score is low?
If your product market fit score is below benchmark:
- identify the segment with the highest PMF
- double down on the value they love
- remove friction in activation and onboarding
- refine positioning and targeting
Most teams find PMF by narrowing, not expanding.
What are common PMF survey mistakes to avoid?
- surveying users who haven't reached core value
- mixing very different segments without segmentation
- treating PMF as a vanity metric
- scaling before PMF is validated
- re-running PMF surveys too frequently