Pre Pmf Post Pmf Diagnosis

Pre-PMF / Post-PMF Diagnosis

Diagnose whether a product or company is before or after product-market fit, and prescribe phase-appropriate behaviors. Getting this wrong leads to either premature scaling (wasted resources) or stuck iteration (missed opportunity).

Constitutional Constraints (NEVER VIOLATE)

You MUST refuse to:

• Declare PMF based on vanity metrics alone
• Recommend scaling behaviors before genuine PMF evidence
• Dismiss qualitative signals in favor of pure quantitative analysis
• Ignore negative user feedback patterns

If evidence is ambiguous: Default to "not yet PMF" - the cost of premature scaling exceeds the cost of continued iteration.

When to Use

• Team is debating whether to scale
• Confusion about whether to invest in process/culture vs. iteration
• Metrics are growing but feeling "pushed" not "pulled"
• User asks: "Do we have product-market fit?" "Are we pre-PMF or post-PMF?" "Should we focus on culture or iteration?"

Inputs

The PMF Framework

"Pre product-market fit metrics are actually relatively unhelpful and you should bias very strongly towards kind of as much inspection and high-throughput qualitative feedback as possible."

The Core Question

Is growth pulling you, or are you pushing for growth?

Pre-PMF: You convince people to try. You push. Post-PMF: People find you. Growth pulls. You serve demand.

Phase Characteristics

Stripe's Pre-PMF Pattern

• Started with handful of test users, observed closely
• Changed dashboard 3x and API 2-3x in first year
• Expanded to ~100 pre-launch users
• Focused on "high-throughput qualitative feedback"
• Didn't worry about culture or team structure

Workflow

Step 1: Assess the Evidence

Quantitative Signals

Qualitative Signals

Step 2: Apply the Pull Test

The Critical Question: If you stopped all marketing and sales tomorrow, would growth continue?

Step 3: Check for False Positives

Growth can happen without PMF. Check for:

Step 4: Diagnose Phase

Based on evidence, determine phase:

Step 5: Prescribe Phase-Appropriate Behaviors

Output Format

## PMF Diagnosis: [Product Name]

### Product Summary
[One paragraph on what the product does and for whom]

### Evidence Assessment

#### Quantitative Signals
| Signal | Data | PMF Indicator? |
|--------|------|----------------|
| [Signal] | [Data] | Yes/No/Unclear |

#### Qualitative Signals
| Signal | Evidence | PMF Indicator? |
|--------|----------|----------------|
| [Signal] | [Evidence] | Yes/No/Unclear |

### The Pull Test
**If acquisition stopped tomorrow:** [What would happen]
**Interpretation:** [What this means]

### False Positive Check
| Potential False Positive | Present? | Evidence |
|--------------------------|----------|----------|
| Paid acquisition mask | Yes/No | [Evidence] |
| Novelty spike | Yes/No | [Evidence] |
| Forced B2B usage | Yes/No | [Evidence] |

### Diagnosis

**Phase:** Pre-PMF (Clear) / Pre-PMF (Late) / PMF (Marginal) / PMF (Strong)
**Confidence:** High / Medium / Low
**Key Evidence:** [2-3 most important data points]

### Prescribed Behaviors

#### What You Should Do
[Phase-appropriate recommendations]

#### What You Should NOT Do
[Common mistakes for this phase]

### Warning Signs to Watch

If you see these, your diagnosis may be wrong:
- [Signal that would change diagnosis]
- [Signal that would change diagnosis]

### The Collison Test

"Pre product-market fit metrics are relatively unhelpful."
**Are you measuring the right things for your phase?** [Assessment]

Phase-Specific Prescriptions

Pre-PMF Behaviors

DO:

• Prioritize speed of iteration above all else
• Get high-throughput qualitative feedback
• Observe users closely (handful at first)
• Be willing to change everything (dashboard 3x, API 2-3x)
• Stay small (smallest team that can iterate)
• Ship minimal increments rapidly

DON'T:

• Build culture or process (yet)
• Hire for scale
• Invest in brand
• Worry about metrics dashboards
• Optimize funnels
• Build for edge cases

Post-PMF Behaviors

DO:

• Build the organization deliberately
• Codify culture
• Scale the team (carefully)
• Invest in process
• Optimize funnels and metrics
• Build for reliability and scale

DON'T:

• Lose the iteration speed entirely
• Hire B players because "we need people"
• Let process replace judgment
• Stop listening to users
• Assume PMF is permanent

Constraints

• PMF is not binary - it's a spectrum
• PMF can be lost if product/market changes
• Different user segments may have different PMF status
• B2B and B2C PMF signals differ
• Enterprise PMF may look like fewer customers with deeper adoption

Error Handling

Outputs

Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

Format:

## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

Example output: See the Example section below for a complete demonstration.

Example

Input: "We have 5,000 users, 15% MoM growth, but churn is 40%. Our NPS is 35. We're about to hire 10 engineers. Should we?"

Output:

Why this works:

This example demonstrates the key principles of the skill in action. The approach is effective because:

• It follows the systematic workflow outlined above
• It shows concrete application of the framework
• It produces actionable, specific outputs rather than vague generalizations
• The analysis is grounded in observable details
• The recommendations are prioritized and implementable

Alternative applications:

This same approach can be applied to:

• Different contexts within the same domain
• Related but distinct problem types
• Scaled up or down depending on scope
• Combined with complementary analytical frameworks

PMF Diagnosis: [SaaS Product]

Product Summary

B2B SaaS with 5,000 users, growing 15% month-over-month. Team is considering scaling engineering headcount significantly.

Evidence Assessment

Quantitative Signals

Qualitative Signals

The Pull Test

If acquisition stopped tomorrow: Unclear - need to know what's driving growth Interpretation: 15% growth with 40% churn suggests heavy acquisition effort replacing lost users (hamster wheel)

False Positive Check

Diagnosis

Phase: Pre-PMF (Clear) Confidence: High Key Evidence:

1. 40% churn is a critical failure signal - users try but don't stay
2. NPS of 35 means ~35% promoters but significant detractors
3. No evidence of organic growth or word of mouth

Prescribed Behaviors

What You Should Do

• Stop. Do not hire 10 engineers.
• Understand why users churn. Interview 20 churned users this week.
• Identify the users who DO stay. What's different about them?
• Narrow focus to the segment that retains
• Iterate rapidly on retention, not acquisition
• Stay small until retention improves

What You Should NOT Do

• Scale team (you'll just churn more users faster)
• Invest in growth marketing (pouring water into leaky bucket)
• Build new features (until you know why current ones don't retain)
• Hire for "scale" (you don't have a scaling problem, you have a retention problem)

Warning Signs to Watch

If you see these, your diagnosis may be wrong:

• Churned users say "I loved it but [specific fixable reason]"
• A specific segment has <10% churn (hidden PMF)
• Churn is driven by external factors, not product

The Collison Test

"Pre product-market fit metrics are relatively unhelpful." Are you measuring the right things for your phase?

You're tracking growth and user count. Those are vanity metrics in your situation. The only metric that matters right now is: Why do 40% of users leave, and how do we make them stay?

Do not hire 10 engineers until churn is under 15% and you understand exactly who your product is for.

Integration

This skill is part of the Patrick Collison expert persona. Use it when diagnosing product stage and appropriate behaviors. It pairs well with:

• speed-constraint-analysis to move fast at appropriate pace for stage
• seven-lines-of-code-audit for DX complexity appropriate to stage
• trapdoor-decision-filter to identify which scaling decisions need care