git:20260516.4225f52 to git:20260522.2e90392

12 added, 12 removed. Audit A to A.

---

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.'

+ 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.'

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# 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.

+ 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 |

+ | **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 |

+ | **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

1. **Pick reward architecture** with sender + receiver explicit

2. **Strip friction** from the invite mechanic; benchmark every step

3. **Place the ask** at intrinsic-motivation moments (post-aha is gold)

- 4. **Add proof** — testimonials, "X people have invited friends," referrer endorsement

+ 4. **Add proof** - testimonials, "X people have invited friends," referrer endorsement

5. **Model the loop math** with sensitivities; identify the binding constraint

6. **Instrument fraud and incrementality** before launching, not after

## Tips

1. **K-factor < 1 is fine** if it lowers blended CAC; don't only chase virality

2. **Reward at successful action**, not invite, to align with revenue

- 3. **Run a holdout** to prove incrementality — most teams skip this

+ 3. **Run a holdout** to prove incrementality - most teams skip this

4. **Refresh rewards** quarterly; novelty drives participation

5. **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

+ - **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