| name | pm-growth |
| description | Growth workflow - product-market fit, growth loops, pricing, retention, activation, behavioral design, and experimentation |
| triggers | ["growth","PMF","product-market fit","growth loops","pricing","retention","engagement","activation","onboarding","A/B test","experiment","behavioral design","marketplace"] |
PM Growth: Grow the Product
Growth is the discipline of systematically increasing the value a product delivers and captures. It starts with PMF and compounds through loops, not one-off campaigns.
When to Use This Module
- Measuring or validating product-market fit
- Designing growth loops and activation funnels
- Setting pricing strategy
- Improving retention and engagement
- Designing and prioritizing experiments
Workflow Overview
Measure PMF → Design Growth Model → Optimize Activation → Build Loops → Retain → Monetize
Skills
1. Measuring Product-Market Fit
Context: PMF is not a binary event. It's a spectrum you measure and push toward.
Framework — Sean Ellis Test:
- Survey users: "How would you feel if you could no longer use [product]?"
- Very disappointed responses:
- <25% → No PMF. Go back to discovery.
- 25-40% → Getting close. Double down on what resonates.
-
40% → PMF achieved. Time to grow.
Complementary Signals:
- Organic word-of-mouth (users tell others without prompting)
- Retention curve flattens (cohorts stabilize, not decay to zero)
- Users complain when it's down (they depend on it)
- Pull > push (inbound demand exceeds outbound effort)
Anti-patterns:
- Declaring PMF based on vanity metrics (signups, downloads)
- Equating revenue with PMF (enterprise contracts ≠ product love)
- Moving to growth before PMF (scaling a leaky bucket)
2. Designing Growth Loops
Context: Loops are self-reinforcing cycles where output of one step becomes input to the next. They compound; channels don't.
Framework — Growth Loop Design:
- Identify the loop: New user → [gets value] → [shares/creates] → [attracts new user]
- Map the steps: Each step has a conversion rate you can measure and optimize
- Find the fuel: What makes the loop spin faster? (content, invites, SEO, data network effects)
- Remove friction: Every unnecessary step in the loop is a leak
Common Loop Types:
| Loop | Example | Fuel |
|---|
| Viral | User invites → friend joins → friend invites | Social value |
| Content | User creates → Google indexes → searcher finds → searcher creates | SEO + UGC |
| Paid | Revenue → ad spend → new user → revenue | Unit economics |
| Data network | More users → better product → more users | Data quality |
3. Pricing Strategy
Context: Pricing is the most underleveraged growth lever. A 1% improvement in pricing yields more than a 1% improvement in acquisition.
Framework — Pricing Design:
- Value metric: What unit reflects value delivered? (seats, messages, storage, transactions)
- Willingness to pay: Survey users with Van Westendorp or Gabor-Granger
- Competitive anchoring: Price relative to alternatives, not your costs
- Packaging: 3 tiers is the sweet spot — Starter / Pro / Enterprise
- Iteration: Review pricing annually. Most startups underprice.
Pricing Principles:
- Charge for value, not features
- Free plans should be a growth loop input, not charity
- Usage-based pricing aligns incentives (you grow when they grow)
- Annual discounts improve cash flow and reduce churn
4. Retention & Engagement
Context: Retention is the foundation of all growth. No loop works if users don't come back.
Framework — Retention Analysis:
- Define your retention event: What action = "active"? (not just login)
- Cohort analysis: Group users by signup week, plot % active over time
- Find the magic number: What behavior in week 1 predicts long-term retention? (e.g., "users who create 3+ documents in week 1 retain at 2x")
- Build habits: Use the Hook Model (Trigger → Action → Variable Reward → Investment)
- Reactivation: For churned users, identify the trigger that brings them back
Retention Benchmarks (monthly):
- Consumer social: 25%+ good, 50%+ great
- Consumer SaaS: 40%+ good, 60%+ great
- B2B SaaS: 60%+ good, 80%+ great
5. User Onboarding
Context: Activation is the steepest drop in the funnel. Most products lose 60-80% of users before they experience core value.
Framework — Time to Value (TTV):
- Define the "aha moment" — the first time the user gets real value
- Map every step from signup to aha moment
- Remove every step that isn't absolutely necessary
- For necessary steps, make them progressive (don't front-load all setup)
- Measure: % of signups reaching aha moment within [time window]
Onboarding Patterns:
- Product-led: Empty states that teach, interactive tutorials, sample data
- Human-assisted: Onboarding calls, white-glove setup (high ACV products)
- Community-led: Templates, starter kits, community forums
6. Behavioral Product Design
Context: Products that align with human psychology grow faster. This isn't manipulation — it's reducing friction and increasing value perception.
Framework — BJ Fogg Behavior Model (B = MAP):
- Motivation: Does the user want to do this? (pain/pleasure, hope/fear, social acceptance)
- Ability: Is it easy enough? (time, money, effort, cognitive load)
- Prompt: Is there a clear trigger? (notification, visual cue, habit stack)
- All three must be present simultaneously. If behavior isn't happening, diagnose which is missing.
Design Principles:
- Reduce cognitive load (fewer choices = more action)
- Show social proof at decision points
- Default to the behavior you want (opt-out > opt-in)
- Celebrate milestones (progress bars, streaks, congratulations)
7. Launch Marketing
Context: A launch is a growth event, not a PR event. Plan for sustained impact, not a one-day spike.
Framework — Launch Phases:
- Pre-launch (4 weeks): Build waitlist, seed content, brief press
- Launch week: Product Hunt, HN, social, email blast, press hits
- Sustain (4 weeks post): Content marketing, case studies, SEO
- Iterate: Measure CAC from launch activities, double down on winners
Anti-patterns:
- All energy on launch day, nothing after
- Launching before the product is good enough (first impression = lasting impression)
- Not measuring which launch channel drove actual retention (not just signups)
8. Marketplace Liquidity Management
Context: Two-sided marketplaces have a cold-start problem. Neither side has value without the other.
Framework — Marketplace Growth Playbook:
- Pick a side: Start by subsidizing supply or demand (usually supply)
- Geo-concentrate: Launch in one city/market until it works, then expand
- Constrain to create liquidity: Narrow the market until supply/demand ratio feels abundant
- Cross-side network effects: More supply → better matches → more demand → more supply
- Measure liquidity: Search-to-fill rate, time-to-match, utilization rate
Experimentation Framework
Designing Experiments
- Hypothesis: "If we [change], then [metric] will [improve by X%] because [reason]"
- Metrics: Primary metric + guardrail metrics (make sure you don't break something else)
- Sample size: Calculate power analysis before running (avoid peeking)
- Duration: Run for full business cycles (min 1 week for B2C, 2 weeks for B2B)
- Decision criteria: Pre-commit — "We ship if primary metric improves ≥X% with p<0.05"
Experiment Prioritization (ICE)
- Impact: How much will this move the metric? (1-10)
- Confidence: How sure are you it will work? (1-10)
- Ease: How fast can you run it? (1-10)
- Score = I × C × E. Run highest-scoring experiments first.
Module Checklist
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