# Viral Loop Design — Referral, Invite & Share Mechanics

Products don't go viral by accident. This playbook teaches you to engineer virality into your product from day one.

## Viral Loop Anatomy

The mechanics behind referral, invite, and content loops:

- **Referral loops**: User → invites friend → friend converts → friend invites more friends
- **Content loops**: User creates content → content is shared publicly → viewers become users
- **Invite loops**: User needs collaborators → invites team → team invites their teams
- **Organic loops**: User achieves outcome → shares result → observers want same outcome
- **Paid-amplified loops**: Organic viral loop + paid boost at the top of funnel

## K-Factor Math

Calculate your viral coefficient and model growth scenarios:

- **K = invites per user × conversion rate**: K > 1 means exponential growth
- **Viral cycle time**: How fast each generation completes (shorter = faster compounding)
- **Effective K-factor**: Account for churn, inactive users, and invitation fatigue
- **Modeling spreadsheet**: Project user growth at different K values (0.3, 0.7, 1.2, 2.0)
- **Benchmarks**: Most products achieve K = 0.2–0.5; K > 0.7 is exceptional

## Incentive Design

What to offer referrers and referred users (and what doesn't work):

- **Double-sided rewards**: Both parties get value (Dropbox: 500MB each)
- **Credit/usage-based**: Give product currency rather than cash (higher perceived value)
- **Tiered milestones**: Escalating rewards at 3, 10, 25 referrals (gamification)
- **Anti-patterns**: Cash rewards attract fraudsters; discounts devalue your product
- **Testing framework**: A/B test incentive types, amounts, and placement

## Share Triggers

Moments in the user journey where sharing feels natural:

- **Achievement moments**: User hits a milestone → prompt sharing their win
- **Collaboration needs**: Task requires others → invite flow is the natural next step
- **Content creation**: User produces something → sharing is inherent to the workflow
- **Delight moments**: Something unexpectedly great → user wants to show others
- **Value realization**: "Aha moment" → user thinks of who else would benefit

## Case Studies

How leading companies built their viral engines:

- **Dropbox**: Referral program (60% of signups); double-sided storage incentive; viral K=0.7
- **Notion**: Template sharing + team workspace invites; content-as-distribution
- **Loom**: Video sharing is the product; every viewer is a potential user; K=1.2 at peak
- **Slack**: Team-invite loop; every workspace needs critical mass; network effects compound
- **Calendly**: Scheduling link shared with every meeting invitee; passive viral distribution

## Related Gingiris Skills
- Full version: https://clawhub.ai/skill/gingiris-launch
- All skills: https://clawhub.ai/user/gingiris
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