growth-hacking

Getting Started

1. Install the skill using the command above
2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
3. Reference the skill in your prompt
4. The AI will use the skill's capabilities automatically

Example Prompts

• "Generate a professional invoice for the consulting work done in January"
• "Draft an NDA for our upcoming partnership with Acme Corp"

Overview

Design and run growth experiments that drive user acquisition, activation, retention, and revenue. Build viral loops, optimize funnels, and scale what works.

Instructions

Growth experiment framework

Every growth initiative starts as an experiment:

## Experiment: [Name]

**Hypothesis**: If we [change], then [metric] will [improve] because [reason].
**Primary Metric**: [e.g., signup conversion rate]
**Success Criteria**: [e.g., +15% conversion with 95% confidence]
**Sample Size Needed**: [calculated based on baseline and MDE]
**Duration**: [e.g., 2 weeks or until statistical significance]

Run experiments in this order of impact:

1. Activation — get users to the "aha moment" faster
2. Retention — keep users coming back
3. Acquisition — bring more users in
4. Revenue — monetize effectively
5. Referral — turn users into advocates

Activation and retention come first because acquiring users into a leaky funnel wastes money.

Viral loop design

The key metric is the viral coefficient (K-factor):

K = invites_per_user × conversion_rate_per_invite
K > 1.0 = exponential growth (rare, aim for K > 0.5 as amplifier)

Types of viral loops:

• Organic virality: The product requires others (Slack, Zoom, Figma). Build sharing into the core workflow.
• Incentivized virality: Reward both sides (Dropbox: 500MB free for both). Reward must connect to core value.
• Content virality: Users create shareable content (Canva watermark, Substack sharing).

Referral program design: Double-sided rewards convert 2-3x better than single-sided. Trigger on qualifying action (not just signup) to prevent fraud. Cap rewards per user to limit abuse. Short expiry creates urgency.

Funnel optimization

Map the full journey and measure drop-off:

Visitor → Signup → Activation → Retention → Revenue → Referral

Example baseline:
Landing → Signup:     3.2% (benchmark: 2-5%)
Signup → Activated:   34%  (benchmark: 20-40%)
Activated → Day 7:    28%  (benchmark: 20-35%)
Active → Paid:        4.8% (benchmark: 2-5%)
Paid → Referrer:      12%  (benchmark: 5-15%)

Focus on the biggest drop-off first. A 10% improvement on 34% activation adds more users than 10% on 3.2% signup.

Activation optimization

The "aha moment" is the action predicting long-term retention. Find it by comparing retained vs. churned user behavior:

• Slack: sending 2000+ team messages
• Dropbox: putting one file in a shared folder
• Facebook: adding 7 friends in 10 days

Once identified, redesign onboarding to get users there as fast as possible. Remove every step that doesn't lead to it.

Cohort analysis

Track behavior by signup cohort to measure retention trends:

         Week 0  Week 1  Week 2  Week 3  Week 4
Jan W1   100%    42%     28%     22%     19%
Jan W2   100%    45%     31%     25%     21%
Feb W1   100%    52%     38%     31%     --

If newer cohorts retain better, product improvements are working. If retention flattens at a certain week, that's your natural floor — focus on raising it.

Product-led growth

• Freemium: Free tier delivers real value, paid tier unlocked by usage limits or team features. Don't gate behind credit cards.
• Reverse trial: Full paid features for 14 days, then downgrade. Users decide about keeping vs. imagining.
• Usage-based pricing: Charge based on value consumed. Low barrier, scales with success.

A/B testing

Calculate required sample size before launching:

n per variant = (Z² × p × (1-p)) / MDE²
Example: baseline 5%, detect +1% → n = 18,271 per variant

Don't peek at results early — wait for full sample size. Priority: Headlines/CTAs → Pricing → Onboarding → Social proof → Form length.

Retention strategies

• Habit loops: Trigger → Action → Variable Reward → Investment
• Re-engagement: Segment churned users by last action, send targeted emails
• Milestone celebrations: Acknowledge achievements (first project, 100th task, 1-year anniversary)

Growth metrics dashboard

ACQUISITION: New signups, signup conversion, CAC by channel
ACTIVATION: Activation rate, time to activate, drop-off steps
RETENTION: Day 1/7/30 retention, cohort trend, churn rate
REVENUE: MRR, ARPU, LTV, LTV:CAC ratio
REFERRAL: Viral coefficient (K), referral rate, referral conversion

Examples

Design a referral program for a SaaS product

Design a referral program for our project management SaaS. We have 5,000 active users, $49/mo average plan, and 3% monthly churn. We want to reduce CAC (currently $180) and increase organic growth. Propose the incentive structure, qualifying actions, fraud prevention, and projected K-factor.

Optimize onboarding activation rate

Our activation rate is 23% (user creates first project within 48 hours of signup). Analyze our current 6-step onboarding flow, identify likely drop-off points, and propose experiments to get activation above 35%. Include A/B test designs with sample size calculations.

Build a growth metrics dashboard

Set up a weekly growth dashboard for our marketplace. We need to track supply-side (sellers) and demand-side (buyers) separately, with cohort retention, unit economics, and liquidity metrics. Recommend the metrics, alert thresholds, and review cadence.

Guidelines

• Always prioritize activation and retention experiments before acquisition — fix the leaky funnel first
• Never peek at A/B test results early; wait for statistical significance or use sequential testing
• Use double-sided incentives for referral programs (2-3x better conversion than single-sided)
• Choose a North Star metric that is measurable, leading, actionable, and connected to revenue
• Re-engagement campaigns should segment by last user action, not blast the same message to all churned users
• Run experiments for a minimum of 1-2 weeks; don't call winners after a few days
• Track cohort retention weekly to validate that product changes actually improve outcomes