# Product-Market Fit Signals

## Purpose
Identify and measure whether you've achieved product-market fit—the point where your product satisfies strong market demand and growth becomes organic.

## When to Use
- Deciding whether to scale or continue iterating
- Assessing startup health and investment readiness
- Determining if you should pivot or persevere
- Communicating progress to stakeholders
- Planning the transition from search to execution

## Process

### 1. Understand Product-Market Fit

**Marc Andreessen's definition:**
"Product-market fit means being in a good market with a product that can satisfy that market."

**How it feels:**
- Before PMF: Pushing a boulder uphill
- After PMF: The market pulls the product out of you

**The key question:**
If you ask "Do we have product-market fit?", you probably don't. When you have it, you know.

### 2. Qualitative PMF Signals

**Strong positive signals:**

- **Customers actively recommend you** - Unprompted referrals
- **Organic usage grows** - Usage increases without marketing
- **Customers find you** - Inbound exceeds outbound
- **They'd be "very disappointed" without you** - Emotional attachment
- **You're turning away customers** - Demand exceeds capacity
- **Sales cycles shorten** - Less convincing required
- **Low-touch conversion** - Customers self-serve to purchase

**Weak or absent signals:**

- Only paid/incentivized users
- High churn even with engaged users
- Customers need constant convincing
- Features feel like "nice to have"
- Growth plateaus when marketing stops
- Heavy discounting required to close

### 3. Quantitative PMF Signals

**The Sean Ellis Test (Most common)**

Survey users: "How would you feel if you could no longer use [product]?"
- Very disappointed
- Somewhat disappointed
- Not disappointed

**PMF threshold:** 40%+ "Very disappointed"

**Retention Curves**

Track cohort retention over time:
- **Before PMF:** Retention declines to near zero
- **At PMF:** Retention curve flattens (stable base of retained users)
- **After PMF:** Retention improves with product maturity

**Example retention curves:**
```
Before PMF:  100% → 50% → 25% → 10% → 3% → 1%
At PMF:      100% → 60% → 45% → 40% → 38% → 38%
Strong PMF:  100% → 70% → 55% → 50% → 50% → 52%
```

**Net Revenue Retention (NRR)**

For subscription businesses:
NRR = (Starting MRR + Expansion - Contraction - Churn) / Starting MRR

- **<100%:** Revenue shrinking (no PMF)
- **100-110%:** Stable (approaching PMF)
- **>110%:** Growing from existing customers (strong PMF)
- **>120%:** Excellent (customers expanding significantly)

**Engagement Ratios**

DAU/MAU ratio (daily active / monthly active):
- **<10%:** Low engagement (no PMF)
- **10-20%:** Moderate (signs of PMF)
- **20-30%:** Strong (clear PMF)
- **>30%:** Very strong (social/daily use products)

**Unit Economics**

- **LTV > 3x CAC:** Sustainable acquisition
- **Payback period < 12 months:** Healthy cash flow
- **Gross margin > 70%:** Software economics

### 4. Measure Your PMF Score

Create a PMF dashboard:

| Signal | Metric | Your Score | Target |
|--------|--------|------------|--------|
| Sean Ellis Test | % "Very disappointed" | ? | >40% |
| Month 3 Retention | % still active | ? | >30% |
| NRR | Revenue retention | ? | >100% |
| DAU/MAU | Engagement ratio | ? | >15% |
| LTV:CAC | Unit economics | ? | >3:1 |
| Organic % | % growth from organic | ? | >30% |

**PMF strength levels:**

- **0-2 signals:** Pre-PMF (keep iterating)
- **3-4 signals:** Approaching PMF (validate and tune)
- **5-6 signals:** PMF achieved (begin scaling)

### 5. PMF for Different Business Models

**B2C/Consumer:**
- Focus on: Engagement (DAU/MAU), retention, viral coefficient
- Key test: Would users be very disappointed?
- Time to PMF: 6-18 months typically

**B2B SaaS:**
- Focus on: NRR, expansion revenue, sales cycle length
- Key test: Are they renewing without discounts?
- Time to PMF: 12-24 months typically

**Marketplace:**
- Focus on: Liquidity (matches made), repeat usage, NPS both sides
- Key test: Do supply and demand keep coming back?
- Time to PMF: 18-36 months typically

**E-commerce:**
- Focus on: Repeat purchase rate, LTV, CAC payback
- Key test: Do customers buy again?
- Time to PMF: 6-12 months typically

### 6. False PMF Signals

**Beware of:**

- **Heavily subsidized growth** - Free/discounted users aren't PMF
- **One customer concentration** - 50% revenue from one account
- **Founder-led sales only** - Can others sell it?
- **Feature-specific engagement** - One feature, rest ignored
- **Vanity metrics** - Signups without activation
- **Small sample size** - 10 happy users isn't PMF

**Validate with:**
- Remove discounts—do they still buy?
- Have non-founders sell—does it work?
- Track retention over 6+ months, not weeks
- Measure across hundreds, not dozens of users

### 7. What to Do at Each Stage

**Pre-PMF (no clear signals):**
- Talk to customers weekly
- Run rapid experiments (weekly iterations)
- Don't scale marketing/sales
- Focus: Learn and iterate

**Approaching PMF (some signals):**
- Double down on what's working
- Narrow focus to best segment
- Begin building repeatable processes
- Focus: Validate and tune

**PMF achieved (strong signals):**
- Scale marketing and sales
- Hire beyond founders
- Expand product scope carefully
- Focus: Grow while maintaining quality

### 8. Synthesis

After assessing PMF:
- Which signals do you have? Which are missing?
- What would need to change to achieve the missing signals?
- Is your current trajectory moving toward PMF?
- What's the biggest blocker to PMF right now?
- Should you continue iterating or is it time to pivot?

## Example

**Product**: B2B sales engagement platform

### PMF Assessment (Month 12)

**Qualitative signals:**

| Signal | Evidence | Status |
|--------|----------|--------|
| Unprompted referrals | 4 referrals this month | 🟡 Weak |
| Organic growth | 30% of new users organic | 🟢 Present |
| Inbound > outbound | 60% inbound | 🟢 Present |
| Sales cycle shortening | 45 days → 38 days | 🟢 Present |
| Customer emotion | "Can't live without it" x 3 | 🟡 Weak |

**Quantitative metrics:**

| Metric | Score | Target | Status |
|--------|-------|--------|--------|
| Sean Ellis Test | 35% | 40% | 🟡 Close |
| Month 3 Retention | 42% | 30% | 🟢 Achieved |
| NRR | 108% | 100% | 🟢 Achieved |
| DAU/MAU | 22% | 15% | 🟢 Achieved |
| LTV:CAC | 2.8:1 | 3:1 | 🟡 Close |
| Organic % | 30% | 30% | 🟢 Achieved |

### Assessment

**PMF Score: 4/6 strong, 2/6 close**

**Diagnosis:** Approaching PMF but not fully there
- Retention and engagement are strong
- Unit economics nearly viable
- Sean Ellis test just under threshold
- Referrals not yet organic

### Action Plan

**To achieve full PMF:**

1. **Improve Sean Ellis score:**
   - Survey users who churned: why not "very disappointed"?
   - Identify and double down on "must-have" features
   - Target: 40%+ in 90 days

2. **Improve LTV:CAC:**
   - Test price increase (10%)
   - Reduce CAC through content marketing
   - Target: 3.5:1 in 90 days

3. **Increase referrals:**
   - Launch formal referral program
   - Make sharing easier in-product
   - Target: 8 referrals/month

### 90-Day Re-Assessment

| Metric | Before | After | Change |
|--------|--------|-------|--------|
| Sean Ellis Test | 35% | 44% | +9% ✓ |
| LTV:CAC | 2.8:1 | 3.4:1 | +0.6 ✓ |
| Referrals/month | 4 | 11 | +175% ✓ |

**New PMF Score: 6/6**

**Decision:** PMF achieved. Begin scaling.

- Hire 2 additional salespeople
- Increase marketing budget 3x
- Begin Series A fundraising conversations

**Insight:** PMF wasn't binary—it was a spectrum. The 90-day focus on the weakest signals (Sean Ellis, LTV:CAC, referrals) pushed us over the threshold. Measuring objectively prevented both premature scaling and unnecessary pivoting.
