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name: growth-hacking description: >- Design and execute growth experiments for rapid user acquisition and retention. Use when tasks involve viral loops, referral programs, conversion funnel optimization, A/B testing strategies, CAC/LTV analysis, product-led growth, activation rate improvement, cohort analysis, or scaling user growth through data-driven experimentation. Covers the full growth lifecycle from acquisition through retention. license: Apache-2.0 compatibility: "No special requirements" metadata: author: terminal-skills version: "1.1.0" category: business tags: - growth - marketing - acquisition - retention - experimentation

Growth Hacking

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 (both inviter and invitee) usually outperform single-sided ones; test it on your own audience. 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. Funnel steps multiply, so an equal relative lift at any step gives the same lift in paying users; pick the step where a lift is cheapest and the gap to its benchmark is largest. Going from 34% to 44% activation (+29% relative) beats +10% relative on signup.

Activation optimization

The "aha moment" is the action predicting long-term retention. Find it by comparing retained vs. churned user behavior: Widely quoted examples (company folklore, not audited data):

• Slack: a team sending about 2,000 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 based on features they have actually used.
• 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 ≈ (Zα/2·√(2·p̄(1-p̄)) + Zβ·√(p1(1-p1)+p2(1-p2)))² / (p2 - p1)²
Defaults: 95% confidence (Zα/2 = 1.96), 80% power (Zβ = 0.84), p̄ = average of p1 and p2
Example: baseline 5%, detect 5% → 6% → about 8,160 per variant (16,300 total)

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
• Prefer double-sided incentives for referral programs and A/B test the reward size
• 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