Develop a Growth Hacking Playbook

Description

This prompt helps define growth hacking strategies using the AARRR framework (Acquisition, Activation, Retention, Revenue, Referral) to ensure sustainable product growth.

You are a **growth product manager** building a data-driven growth strategy. Create a comprehensive growth hacking playbook for **[Type your product]** using the AARRR (Pirate Metrics) framework.

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## Product Context

| Field | Details |
|---|---|
| **Product type** | {{product_type}} |
| **Current stage** | {{growth_stage}} (e.g., pre-launch, early traction, scaling) |
| **Primary growth goal** | {{growth_goal}} |
| **Current key metrics** | (fill in: MAU, activation rate, retention, revenue, referral rate) |

---

## AARRR Growth Playbook

### 1. Acquisition — How do we get users?

| Channel | Estimated CAC | Effort | 30-day Target |
|---|---|---|---|
| Channel 1 (organic) | | S/M/L | |
| Channel 2 (organic) | | S/M/L | |
| Channel 3 (paid) | | S/M/L | |

**Experiments:**
For each experiment, use this format:
- **Hypothesis:** "If we [change], then [metric] will improve by [X%] because [reason]."
- **Test design:** What to build, audience size, duration
- **Success criteria:** Specific metric threshold to declare a winner

### 2. Activation — How do we deliver the aha moment?

- **Define the aha moment:** The single action that predicts long-term retention (e.g., "Invited 2 teammates within first session")
- **Critical path mapping:**

| Step | Action | Expected Drop-off | Optimization Lever |
|---|---|---|---|
| 1 | Sign up | --% | |
| 2 | First key action | --% | |
| 3 | Aha moment reached | --% | |

**Experiments:** 2 experiments to reduce onboarding friction (use hypothesis format above)

### 3. Retention — How do we keep users engaged?

- **Retention curve analysis:** Identify top 3 drop-off points (Day 1, Day 7, Day 30)
- **Habit loop design:**

| Component | Implementation |
|---|---|
| **Trigger** | What prompts the user to return? (internal/external) |
| **Action** | What is the core repeated behavior? |
| **Variable Reward** | What keeps it interesting each time? |
| **Investment** | What stored value makes leaving costly? |

- **Re-engagement tactics:** Propose specific campaigns for dormant users (7-day, 14-day, 30-day inactive segments) with channel (email/push/in-app) and messaging

### 4. Revenue — How do we monetize?

- **Monetization model:** Recommend the best-fit model for our product type and stage
- **Pricing experiments:**

| Experiment | Hypothesis | Metric to Track | Duration |
|---|---|---|---|
| Experiment 1 (e.g., trial length) | | | |
| Experiment 2 (e.g., tier structure) | | | |

- **LTV levers:** Identify top 3 levers to increase lifetime value, with expected impact

### 5. Referral — How do we drive virality?

- **Referral mechanism:** Design the specific loop (who shares, what they share, incentive for both sides)
- **Viral math:** Estimate K-factor = (invites/user) x (conversion/invite). Target K and viral cycle time.
- **Experiment:** One experiment to boost referral rate by 20%+ (use hypothesis format)

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## Growth Roadmap

### Prioritized Experiment Backlog

| # | Experiment | AARRR Stage | Impact (1-5) | Effort (S/M/L) | Priority Score |
|---|---|---|---|---|---|
| 1 | | | | | |
| 2 | | | | | |
| 3 | | | | | |

### 30-60-90 Day Plan
- **Days 1-30:** Focus on [stage] — run experiments listed above
- **Days 31-60:** Focus on [stage] — run experiments listed above
- **Days 61-90:** Focus on [stage] — run experiments listed above

### Metrics Dashboard

| Level | Metric | Current | 30-day Target | 90-day Target |
|---|---|---|---|---|
| **North Star** | | | | |
| Acquisition | | | | |
| Activation | | | | |
| Retention | | | | |
| Revenue | | | | |
| Referral | | | | |

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