| name | referral-program |
| description | Referral program design - referrer / referee incentive structure, viral mechanics, fraud and abuse controls, attribution, and channel placement. Use when: referral program, refer a friend, viral loop design, K-factor, advocacy referrals, partner referrals, customer referral incentives, referral attribution, viral coefficient. |
Referral Program (RIPPLE Framework)
Design a referral program with a real viral mechanic - not a "refer a friend" button buried in settings. RIPPLE forces explicit design of who refers, why they refer, what the receiver gets, where the program lives, and how it's measured against a viral coefficient.
Core Principle
Referral programs fail because they optimize for the sender's reward and ignore the receiver's trust. A high-K loop requires both. RIPPLE designs both sides of the exchange and instruments the loop end-to-end.
The RIPPLE Framework
| Letter | Stage | The Question |
|---|
| R | Reward Architecture | What does the referrer get, what does the referee get, and when? |
| I | Invite Mechanic | How is the invite sent, and how low-friction is the share? |
| P | Placement | Where in the product / journey does the ask appear? |
| P | Proof | What social proof and trust signals accompany the invite? |
| L | Loop Math | What's the viral coefficient target, and which lever moves it? |
| E | Evaluate & Defend | How is fraud, cannibalization, and incremental lift measured? |
Reward Architecture
The most common failure mode is single-sided rewards.
| Type | Pattern | Best For |
|---|
| Double-sided | Both referrer and referee get reward | Most consumer / SMB programs |
| Single-sided (referrer) | Only referrer rewarded | Pure-advocacy programs (low conversion lift) |
| Single-sided (referee) | Only referee rewarded | When referrer reward feels mercenary (e.g., enterprise) |
| Tiered | Reward escalates with N successful referrals | Power-user motivation |
Reward type considerations:
| Reward | Pros | Cons |
|---|
| Cash / credit | Simple, easy attribution | Attracts abuse, low brand lift |
| Product credit | Reinforces product use | Less appealing if not active user |
| Account upgrade | Aligns with retention | Limited liability cap |
| Cause donation | High-trust, brand-aligned | Smaller activation lift |
| Exclusive access | Status-driven, low cost | Niche appeal |
Invite Mechanic
Friction is the silent killer of K-factor:
| Lever | High-Friction | Low-Friction |
|---|
| Channel | Email-only | Email + SMS + share link + native share sheet |
| Personalization | Generic copy | Pre-filled referrer name + custom note field |
| Tracking | Manual code | Auto-attributed unique link |
| Recipient onboarding | Standard signup | Landing page with referrer context |
Placement
Placement determines who sees the ask and when.
| Placement | When It Works |
|---|
| Post-aha moment | After the first clear value event - referrer is intrinsically motivated |
| Account / settings page | Permanent home, low discoverability |
| Email lifecycle | Anniversary, milestone, or NPS positive |
| In-app banner | High visibility; must be dismissible |
| CSM / sales triggered | B2B; manual but high quality |
Loop Math
| Metric | Definition | Target |
|---|
| Referral rate | % of eligible customers who refer at least once in window | 5-15% strong |
| Invites per referrer | Average invites sent by active referrer | 3-8 strong |
| Conversion rate | % of invitees who become customers | 5-25% varies by motion |
| K-factor | Referral rate × Invites × Conversion | > 1.0 = self-sustaining loop |
| Cycle time | Days from invite to converted referee | Shorter = faster compounding |
Fraud & Cannibalization Controls
| Risk | Control |
|---|
| Self-referral | Device / IP / payment-instrument matching |
| Fake account farms | Rate limits + manual review thresholds |
| Reward abuse | Cap rewards per referrer per window |
| Cannibalization | Match referrer-influenced cohort against organic; measure incrementality |
| Channel arbitrage | Block paid-media referrers if program is meant for organic |
Output
Save to outputs/referral-program-[motion]-[YYYY-MM-DD].md
| Artifact | Description |
|---|
| Reward Design | Sender + receiver rewards, tier escalation, liability cap |
| Invite Spec | Channels, copy, personalization, tracking |
| Placement Map | Where the ask appears across product / lifecycle |
| Loop Math Model | K-factor projection with sensitivity analysis |
| Fraud Controls | Detection rules and reward holds |
| Attribution Spec | Tracking schema, incrementality test design |
| KPIs Dashboard | Referral rate, invites/referrer, conversion, K-factor, fraud rate |
Process
- Pick reward architecture with sender + receiver explicit
- Strip friction from the invite mechanic; benchmark every step
- Place the ask at intrinsic-motivation moments (post-aha is gold)
- Add proof - testimonials, "X people have invited friends," referrer endorsement
- Model the loop math with sensitivities; identify the binding constraint
- Instrument fraud and incrementality before launching, not after
Tips
- K-factor < 1 is fine if it lowers blended CAC; don't only chase virality
- Reward at successful action, not invite, to align with revenue
- Run a holdout to prove incrementality - most teams skip this
- Refresh rewards quarterly; novelty drives participation
- B2B referrals often work better as advocacy plays than cash bounties
Pairs With
- customer-advocacy - Top advocates are the highest-K referrers
- community-catalyst - Communities amplify referral loops
- loyalty-lifecycle - Tiered status integrates with referral milestones
- demand-engine - Channel mix that promotes the program
