I found comprehensive growth context:

- **Target Personas**: [Quote persona behaviors and channels]
- **Value Proposition**: [Quote unique differentiation]
- **Pricing**: [Quote model and conversion targets]
- **GTM**: [Quote initial channels and traction]
- **Unit Economics**: [Quote LTV:CAC, payback period]

I'll design a growth playbook with high-leverage experiments tailored to your personas, economics, and channels.

Ready to build your growth engine?

I found partial context:

[Quote available data]

I have some foundation but need additional information about your current growth metrics, acquisition channels, and product engagement to design optimal experiments.

Ready to proceed?

I'll help you build a comprehensive growth hacking playbook.

We'll design:
- Growth loops (viral, content, paid, sales-led)
- Channel prioritization (which channels to focus on)
- Activation tactics (get users to "aha moment" fast)
- Referral mechanics (turn users into advocates)
- North Star Metric (what measures real growth)
- 90-day experimentation roadmap

First, I need to understand your product, users, and current growth situation.

Ready to start?

What product are you growing, and who uses it?

Be specific:
- Product/service description
- Target user (role, demographics, behaviors)
- Core value proposition (what problem do you solve?)
- Product-market fit status (pre-PMF, early PMF, strong PMF)
- Current stage (pre-launch, 0-100 users, 100-1K, 1K-10K, 10K+)

What's your current growth state?

**Users/Customers**:
- Total users: [X]
- Active users (MAU/WAU): [X]
- Paying customers: [X]
- Growth rate: [X% month-over-month]

**Acquisition**:
- Primary acquisition channels: [List channels]
- CAC (Customer Acquisition Cost): $[X]
- Acquisition rate: [X new users/month]

**Activation**:
- Sign-up to activation rate: [X%]
- Time to activation: [X hours/days]
- What counts as "activated"? [Define activation event]

**Retention**:
- Day 1 retention: [X%]
- Day 7 retention: [X%]
- Day 30 retention: [X%]

**Revenue** (if applicable):
- MRR/ARR: $[X]
- ARPU: $[X]
- LTV: $[X]

**Referral**:
- Referral rate: [X% of users refer]
- Viral coefficient (K-factor): [X] (users invited per user)

If you don't have these metrics, state "Need to establish baseline."

What ONE metric best represents real value delivered to users?

Examples:
- **Slack**: Messages sent (more messages = more value)
- **Airbnb**: Nights booked (core transaction)
- **Dropbox**: Files saved (usage = value)
- **Stripe**: Payment volume processed
- **LinkedIn**: Connections made

**Your North Star Metric**: [Metric name]

**Why this metric**:
- Represents real value to users (not vanity)
- Leads to revenue (eventually)
- Reflects user engagement (not just sign-ups)
- Team can influence (actionable)

**Current NSM**: [X per month]
**Target NSM** (6 months): [X per month]

What type of growth model fits your product?

**Viral Growth** (users invite users):
- Products: Social networks, communication tools, referral-driven
- Examples: Dropbox, Zoom, WhatsApp
- Metric: Viral coefficient (K-factor) > 1
- Fit for you? [Yes/No, why]

**Paid Growth** (buy users profitably):
- Products: High LTV, clear paid channels, strong unit economics
- Examples: SaaS, e-commerce, B2B tools
- Metric: LTV:CAC > 3, payback < 12 months
- Fit for you? [Yes/No, why]

**Content/SEO Growth** (organic traffic):
- Products: Search-driven, educational, high-intent keywords
- Examples: HubSpot, Shopify, Canva
- Metric: Organic traffic growth, keyword rankings
- Fit for you? [Yes/No, why]

**Sales-Led Growth** (sales team drives growth):
- Products: Enterprise, complex, high-touch
- Examples: Salesforce, Workday, large B2B
- Metric: Pipeline, close rate, ACV
- Fit for you? [Yes/No, why]

**Product-Led Growth** (product drives acquisition):
- Products: Freemium, self-serve, viral, network effects
- Examples: Slack, Notion, Figma, Airtable
- Metric: Free-to-paid conversion, product qualified leads
- Fit for you? [Yes/No, why]

Which 1-2 models best fit your product?

A growth loop is a self-reinforcing cycle where output becomes input.

Example (Dropbox referral loop):
1. User signs up
2. User invites friends (incentivized with storage)
3. Friends sign up
4. Friends invite their friends
5. Loop repeats (viral growth)

**Your Primary Growth Loop**:

**Loop Type**: [Viral / Content / Paid / Sales]

**Loop Steps**:
1. [Input: e.g., "User discovers product via X"]
2. [Action: e.g., "User experiences value"]
3. [Output: e.g., "User shares/invites/creates content"]
4. [Amplification: e.g., "New users discover product"]
5. [Loop back to step 1]

**Loop Velocity**: [How fast does loop cycle? Hours? Days? Weeks?]

**Loop Strength**: [How many new users per existing user? K-factor = X]

**Bottleneck**: [What slows the loop? Where do users drop off?]

Most successful companies have multiple loops.

Do you have secondary loops?

**Loop 2** (optional):
- **Type**: [Viral / Content / Paid / Sales]
- **Description**: [How it works]
- **Current Strength**: [Strong/Weak/Non-existent]

**Loop 3** (optional):
- **Type**: [Viral / Content / Paid / Sales]
- **Description**: [How it works]
- **Current Strength**: [Strong/Weak/Non-existent]

If no secondary loops, state "Focus on single loop first."

How do users discover your product?

**Current Acquisition Channels** (rank by volume):
1. [Channel 1]: [X% of signups, $X CAC]
2. [Channel 2]: [X% of signups, $X CAC]
3. [Channel 3]: [X% of signups, $X CAC]

**Conversion Rates**:
- Landing page visit → Sign-up: [X%]
- Ad click → Sign-up: [X%]
- Referral visit → Sign-up: [X%]

**Biggest Acquisition Problem**:
[e.g., "CAC too high", "No clear winner channel", "Low conversion rate"]

What's your "aha moment" (first value experience)?

**Activation Definition**: [What action signals user "gets it"?]
Examples:
- Slack: Team sends 2,000 messages
- Twitter: Follow 30 accounts
- Dropbox: Save first file
- Airbnb: Book first stay

**Your Activation Event**: [Specific action]

**Activation Metrics**:
- Sign-up → Activation: [X%]
- Time to activation: [X hours/days]
- Activation rate by channel: [Channel A: X%, Channel B: X%]

**Biggest Activation Problem**:
[e.g., "Onboarding too slow", "Users don't understand value", "Too many steps to activation"]

How well do you retain users?

**Retention Curve**:
- Day 1: [X%]
- Day 7: [X%]
- Day 30: [X%]
- Day 90: [X%]

**Retention by Cohort** (if available):
- Cohort 1 (Month X): [Retention curve]
- Cohort 2 (Month Y): [Retention curve]
- Improving or declining?

**Power Users**:
- What % of users are power users (daily/weekly active)? [X%]
- What do power users do differently? [Behaviors]

**Biggest Retention Problem**:
[e.g., "Churn after 30 days", "No habit formation", "Users don't return"]

Do users refer others?

**Current Referral Mechanics**:
- Referral program? [Yes/No - describe]
- Incentives? [What do users get for referring?]
- Viral coefficient (K-factor): [X] (invites per user × conversion rate)
  - Example: 5 invites × 20% conversion = 1.0 K-factor
- Referral rate: [X% of users refer]

**Viral Loop Calculation**:

**Biggest Referral Problem**:
[e.g., "No referral program", "Low incentive", "Not viral by nature"]

How do you monetize?

**Revenue Model**: [Subscription / Transaction / License / Freemium / Usage-based]

**Conversion Funnel**:
- Free user → Paying customer: [X%]
- Trial → Paid: [X%]
- Time to conversion: [X days]

**Revenue Metrics**:
- MRR/ARR: $[X]
- ARPU: $[X/month]
- LTV: $[X]
- LTV:CAC: [X:1]

**Biggest Revenue Problem**:
[e.g., "Low free-to-paid conversion", "High churn", "Low pricing"]

The Bullseye Framework helps identify your best acquisition channel.

For each channel, rate 1-10 on:
- **Reach** (how many users can you reach?)
- **Cost** (how expensive per user?)
- **Conversion** (how well do they convert?)
- **Control** (how sustainable is the channel?)

**Viral Channels**:
- **Referral Program**: Reach [X/10], Cost [X/10], Conversion [X/10], Control [X/10]
- **Word of Mouth**: [Scores]
- **Invite Mechanics**: [Scores]

**Organic Channels**:
- **SEO/Content**: [Scores]
- **Social Media**: [Scores]
- **Community**: [Scores]

**Paid Channels**:
- **Google Ads**: [Scores]
- **Facebook/Instagram Ads**: [Scores]
- **LinkedIn Ads**: [Scores]

**Sales Channels**:
- **Outbound Sales**: [Scores]
- **Partnerships**: [Scores]

**Product Channels**:
- **Product Hunt**: [Scores]
- **Integrations**: [Scores]
- **API/Platform**: [Scores]

Based on scores, what are your top 3 channels to focus on?

ICE Score = Impact × Confidence × Ease

For each growth experiment, rate 1-10:
- **Impact**: How much will this move the needle?
- **Confidence**: How sure are you it will work?
- **Ease**: How easy/fast to implement?

List 5-10 growth experiment ideas:

**Experiment 1**: [Description]
- Impact: [X/10]
- Confidence: [X/10]
- Ease: [X/10]
- **ICE Score**: [X/30]

**Experiment 2**: [Description]
- Impact: [X/10]
- Confidence: [X/10]
- Ease: [X/10]
- **ICE Score**: [X/30]

[Repeat for 5-10 experiments]

Top 3 experiments by ICE score: [List]

To achieve viral growth, K-factor (viral coefficient) must be > 1.

**Current K-factor**: [X]

**K-factor Calculation**:

**To improve K-factor, you can**:
1. **Increase invites sent** (make inviting easier, incentivize)
2. **Increase conversion rate** (make signup easier, improve invite messaging)

**Your Strategy**:
- Current: [X invites × X% conversion = X K-factor]
- Target: [X invites × X% conversion = X K-factor]
- How to get there: [Tactics]

If implementing referral program, design the mechanics:

**Incentive Structure**:
- **Referrer gets**: [What reward? Credits, cash, features?]
- **Referee gets**: [What does invited user get?]
- **Example**: Dropbox gave 500MB to both referrer and referee

**Your Incentive**:
- Referrer: [Reward]
- Referee: [Reward]
- Cost to you: $[X per referral]

**Referral Triggers**:
- When do you prompt for referral? (After activation, after value received, periodic prompts)
- How easy is sharing? (One-click, link, email invites)

**Referral Tracking**:
- How do you track? (Unique links, referral codes)
- Attribution window: [X days]

Map your current onboarding flow from sign-up to activation:

**Step 1**: [Sign-up form]
- Friction: [What fields required? Social auth available?]
- Drop-off rate: [X%]

**Step 2**: [e.g., "Email verification"]
- Friction: [Required? Can user skip?]
- Drop-off rate: [X%]

**Step 3**: [e.g., "Profile setup"]
- Friction: [How many fields? How long?]
- Drop-off rate: [X%]

**Step 4**: [e.g., "First action"]
- Friction: [What's required to get value?]
- Drop-off rate: [X%]

**Activation Event**: [When user achieves "aha moment"]

**Overall Sign-up → Activation Rate**: [X%]

**Biggest Onboarding Friction**: [What slows users down most?]

How long does it take from sign-up to first value?

**Current Time to Value**: [X minutes/hours/days]

**Benchmark**:
- Consumer apps: <5 minutes ideal
- B2B SaaS: <24 hours ideal
- Complex tools: <7 days ideal

**Your Target**: [X time to value]

**How to reduce**:
- [Tactic 1: e.g., "Pre-fill data with integrations"]
- [Tactic 2: e.g., "Skip optional steps"]
- [Tactic 3: e.g., "Show value before work"]

undefined

Awareness → Acquisition → Activation → Revenue

User Acquisition → User Engagement → User Action (sharing/content/invites) → New User Acquisition (loop repeats)

1. [Input: e.g., "New user signs up"]
   ↓
2. [Activation: e.g., "User experiences core value"]
   ↓
3. [Action: e.g., "User invites 5 friends"]
   ↓
4. [Amplification: e.g., "Friends sign up"]
   ↓
5. [Loop back to step 1]

If 100 users enter loop:
- 100 users × 5 invites = 500 invites sent
- 500 invites × 20% conversion = 100 new users
- 100 new users cycle through loop again
= 1.0x loop (breakeven, not growing)

Goal: Achieve >1.0x (exponential growth)

Awareness (X visitors/mo)
   ↓ [X% conversion]
Interest (X landing page visits)
   ↓ [X% conversion]
Sign-up (X new users/mo)

Sign-up (X users/mo)
   ↓ [X% complete Step 1]
[Step 1: e.g., Email verification] (X users)
   ↓ [X% complete Step 2]
[Step 2: e.g., Profile setup] (X users)
   ↓ [X% complete Step 3]
[Step 3: e.g., First core action] (X users)
   ↓ [X% reach activation]
Activated Users (X users/mo)

K = (Invites sent per user) × (Invite-to-signup conversion rate)

Current K = [X invites] × [X% conversion] = [X]

Goal K = [X invites] × [X% conversion] = [X]

Free Users (X users)
   ↓ [X% convert]
Paying Customers (X customers)

K = (Avg invites sent per user) × (Invite-to-signup conversion rate)

[X invites] × [X% conversion] = [X K-factor]