# Referral Program

> Diseña programas de referidos y loops de crecimiento viral: usarlo para sistemas de referidos, mecánicas virales, campañas de boca a boca o programas de embajadores.

Fuente: https://skillsagentes.com/skills/openclaudia/openclaudia-skills/referral-program
Markdown: https://skillsagentes.com/skills/openclaudia/openclaudia-skills/referral-program.md

Repositorio: https://github.com/OpenClaudia/openclaudia-skills
Autor: OpenClaudia
Licencia: MIT
Actualizado: hace 7 meses
Coste de contexto: 83 tok instalada, 2.6k tok al activarse, 2.6k tok con todos los archivos del bundle
Bundle: 1 archivo, 10 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add OpenClaudia/openclaudia-skills --skill referral-program --agent claude-code
# Cursor
npx -y skills add OpenClaudia/openclaudia-skills --skill referral-program --agent cursor
# Codex
npx -y skills add OpenClaudia/openclaudia-skills --skill referral-program --agent codex
# Gemini CLI
npx -y skills add OpenClaudia/openclaudia-skills --skill referral-program --agent gemini
# Windsurf
npx -y skills add OpenClaudia/openclaudia-skills --skill referral-program --agent windsurf
# Cline
npx -y skills add OpenClaudia/openclaudia-skills --skill referral-program --agent cline
```

## Qué hace

- Diseña estructuras de programas de referidos (unilateral, bilateral, escalonado)
- Calcula el coeficiente viral (K) y el tiempo de ciclo viral
- Genera plantillas de landing page, emails y mensajes para canales sociales
- Aporta checklist de implementación técnica y prevención de fraude
- Define métricas clave y tests A/B para medir el programa

## Cuándo usarla

- Se pide crear sistemas de referidos, mecánicas virales o campañas de boca a boca
- Se necesitan loops de crecimiento, sistemas de invitación o programas de ambajadores
- Se busca calcular o mejorar el coeficiente viral de un producto

## Qué la activa

- "Diseña un programa de referidos bilateral para mi app"
- "Calcula el coeficiente viral de mi producto con estos datos"
- "Necesito plantillas de email para mi campaña de referidos"
- "Crea una landing page para referidos estilo Airbnb"

## Archivos

- SKILL.md — 10 KB

## SKILL.md

Reproducido tal cual desde OpenClaudia/openclaudia-skills bajo MIT. Esta sección es el documento original y está en inglés.

# Referral Program Design

Design effective referral programs and viral loops that drive sustainable growth.

---

## 1. Referral Program Frameworks

### One-Sided Incentives

Only the referrer gets rewarded.

**Best for:**
- Products with strong organic word-of-mouth
- Low-friction signups where the referred user needs no extra motivation
- Cost-sensitive businesses

**Examples:**
- Uber: "$10 credit for every friend you refer"
- Amazon Associates: Commission on referred purchases

**Template:**
```
Refer a friend and get [reward].
Share your unique link: [referral_url]
```

### Two-Sided Incentives

Both referrer and referred user get rewarded.

**Best for:**
- Products requiring activation effort from new users
- Competitive markets where new users need a nudge
- Subscription businesses

**Examples:**
- Dropbox: Both get 500MB extra storage
- Airbnb: Referrer gets $25 credit, friend gets $40 off first stay
- PayPal: Both get $10 when friend makes first transaction

**Template:**
```
Give [friend_reward], get [referrer_reward].
Share your link and you both win: [referral_url]
```

### Tiered Incentives

Rewards increase with number of successful referrals.

**Example tier structure:**
| Referrals | Reward |
|-----------|--------|
| 1 | Free month |
| 3 | Exclusive feature unlock |
| 5 | Premium plan for 3 months |
| 10 | Lifetime premium access |
| 25 | Cash payout or swag box |

**Best for:**
- Creating power referrers / ambassadors
- Products with passionate user bases
- Building a referral leaderboard culture

---

## 2. Viral Coefficient Calculation

The viral coefficient (K-factor) determines whether your referral loop is self-sustaining.

### Formula

```
K = i * c

Where:
  i = number of invites sent per user
  c = conversion rate of each invite

If K > 1: viral growth (each user brings more than one new user)
If K < 1: referrals supplement but don't replace other acquisition
```

### Example Calculation

```
Users send an average of 5 invites (i = 5)
15% of invites convert to signups (c = 0.15)
K = 5 * 0.15 = 0.75

With 1,000 initial users:
- Cycle 1: 1,000 * 0.75 = 750 new users
- Cycle 2: 750 * 0.75 = 563 new users
- Cycle 3: 563 * 0.75 = 422 new users
- Total after 10 cycles: ~3,570 additional users from referrals
```

### Viral Cycle Time

The speed of the viral loop matters as much as K:

```
Effective growth = K / cycle_time

A K of 0.5 with a 1-day cycle > K of 0.8 with a 30-day cycle
```

### How to Improve K

| Lever | Action |
|-------|--------|
| Increase invites (i) | Make sharing frictionless, prompt at key moments |
| Increase conversion (c) | Better landing page, stronger incentive, social proof |
| Reduce cycle time | Instant reward delivery, real-time notifications |

---

## 3. Referral Channels

### Email Referral

**Pros:** High conversion, personal, trackable
**Cons:** Lower volume, requires email access

Template:
```
Subject: I thought you'd like [Product] -- here's [reward] to try it

Hey [Name],

I've been using [Product] for [time] and it's been great for [specific benefit].

I wanted to share my referral link so you can get [friend_reward]:
[referral_url]

[Your name]
```

### Social Media Sharing

**Pros:** High reach, low effort, viral potential
**Cons:** Lower conversion rate, less personal

Platform-specific templates:

**Twitter/X:**
```
I just [achievement/milestone] with @Product! If you want to try it,
use my link and we both get [reward]: [referral_url]
```

**LinkedIn:**
```
I've been using [Product] to [professional benefit] and the results
have been impressive: [specific metric].

If you're looking for [solution], here's my referral link
(we both get [reward]): [referral_url]
```

**WhatsApp/SMS:**
```
Hey! I've been using [Product] and really like it. They have a
referral deal -- we both get [reward] if you sign up through my link:
[referral_url]
```

### In-App Referral

**Pros:** Highest intent, contextual, frictionless
**Cons:** Only reaches existing users

Best practices:
- Show referral prompt after a success moment (completed task, achievement, positive outcome)
- Pre-populate sharing message
- Show referral progress and rewards earned
- Add referral widget to account/settings page

### Unique Link vs. Referral Code

| Method | Pros | Cons |
|--------|------|------|
| Unique link | Frictionless, works in any channel | Harder to share verbally |
| Referral code | Easy to remember, shareable verbally | Extra step at signup |
| Both | Maximum flexibility | More complex to implement |

---

## 4. Referral Landing Page Template

The page a referred user sees when they click the referral link.

### Structure

```
[Hero Section]
- Headline: "[Referrer's name] invited you to [Product]"
- Subheadline: "Sign up now and get [friend_reward]"
- CTA button: "Claim your [reward]"

[Social Proof]
- "[Referrer's name] and X others use [Product]"
- Logos of known customers
- Key metric: "Trusted by X users"

[Value Proposition]
- 3 key benefits with icons
- Brief product description

[How It Works]
- Step 1: Sign up (takes 30 seconds)
- Step 2: [Key activation action]
- Step 3: Enjoy [reward] + the product

[CTA Repeat]
- "Join [Referrer's name] on [Product]"
- Urgency: "This offer expires in [X days]"

[FAQ]
- When do I get my reward?
- What does [Product] do?
- Is there a catch?
```

---

## 5. Case Studies

### Dropbox (2008-2010)

- **Mechanic:** Two-sided -- both get 500MB extra storage
- **Result:** 3,900% growth in 15 months (100K to 4M users)
- **Key insight:** The reward (storage) was the product itself, making it self-reinforcing
- **Viral coefficient:** Estimated ~0.6-0.7 (supplemental, not purely viral)

### Airbnb (2014)

- **Mechanic:** Two-sided -- $25 travel credit for referrer, $40 off first booking for friend
- **Result:** 300% increase in bookings from referral program 2.0
- **Key insight:** Redesigned referral page to feel like a gift, not spam. Personalized with referrer's photo and travel history
- **Viral coefficient:** Low K but high LTV per referred user

### PayPal (1999-2000)

- **Mechanic:** Two-sided -- $20 per referral (later reduced to $10, then $5)
- **Result:** 7-10% daily growth, reaching 1M users in first year
- **Key insight:** Cash incentive drove massive initial growth, then reduced as network effects kicked in
- **Cost:** $60-70M in referral bonuses, but each user was worth much more in LTV

### Robinhood (2014-2015)

- **Mechanic:** Wait-list with move-up-the-line incentive + free stock for referrals
- **Result:** 1M waitlist signups before launch
- **Key insight:** Scarcity (waitlist) + social proof (your position) + tangible reward (free stock)

---

## 6. Implementation Checklist

### Technical Setup

- [ ] Generate unique referral links/codes per user
- [ ] Build referral tracking (link clicks, signups, activations, rewards)
- [ ] Set up attribution window (how long after click does signup count?)
- [ ] Implement fraud detection (self-referral, fake accounts, VPN abuse)
- [ ] Build reward fulfillment (automatic credit, manual approval, or hybrid)
- [ ] Create referral dashboard for users (invites sent, pending, completed, rewards earned)

### Fraud Prevention

- [ ] Block self-referrals (same IP, same device, same email domain)
- [ ] Require activation action before reward (not just signup)
- [ ] Set daily/weekly referral limits per user
- [ ] Flag suspicious patterns (bulk signups from same IP range)
- [ ] Implement clawback for fraudulent referrals

### Program Design Decisions

| Decision | Options |
|----------|---------|
| Reward type | Cash, credit, product features, swag, charity donation |
| Reward timing | On signup, on activation, on first purchase |
| Reward amount | Test multiple amounts; higher is not always better |
| Expiration | Referral links expire after X days? Rewards expire? |
| Limit | Max referrals per user per month? |
| Eligibility | All users or only paid/active users? |

---

## 7. Referral Email Sequences

### Invite Reminder (to existing users)

```
Subject: You have [X] invites waiting

Hey [Name],

Did you know you can give your friends [reward] and get [reward] for yourself?

You've invited [0/N] friends so far. Share your link:
[referral_url]

Here's what [Product] users are saying:
"[Testimonial]" -- [Customer name]
```

### Referred User Welcome

```
Subject: [Referrer] sent you a gift -- [reward] inside

Hey there!

[Referrer's name] thinks you'd love [Product], so they're giving you [reward] to try it.

[CTA: Claim your reward]

What is [Product]?
[1-sentence description]

What you'll get:
- [Benefit 1]
- [Benefit 2]
- [Benefit 3]
- Plus [friend_reward] from [Referrer's name]

This offer expires in [X] days.
```

### Referral Success Notification

```
Subject: [Friend's name] just signed up -- you earned [reward]!

Great news, [Name]!

[Friend's name] accepted your invitation and signed up for [Product].
Your [reward] has been added to your account.

Keep sharing: you've referred [X] friends so far.
[referral_url]

[Show progress toward next tier if using tiered rewards]
```

---

## 8. Measuring Success

### Key Metrics

| Metric | Formula | Good Benchmark |
|--------|---------|----------------|
| Participation rate | Users who share / total users | 15-25% |
| Shares per user | Total shares / participating users | 3-5 |
| Click-through rate | Link clicks / shares | 20-40% |
| Conversion rate | Signups / link clicks | 10-25% |
| Viral coefficient (K) | Invites * conversion rate | 0.3-0.7 typical |
| Viral cycle time | Avg days from invite to new user's first invite | 1-7 days |
| Referral revenue | Revenue from referred users | Track separately |
| CAC via referral | Reward cost / acquired users | Compare to paid CAC |

### A/B Tests to Run

1. Reward amount ($10 vs $20 vs $50)
2. Reward type (credit vs cash vs feature unlock)
3. One-sided vs two-sided incentive
4. Referral prompt timing (after signup vs after first success)
5. Landing page copy (gift framing vs deal framing)
6. Email subject lines for referral invites
7. Social sharing copy variations

## Dónde encaja

- Categoría: [Marketing](https://skillsagentes.com/categorias/marketing.md) — Campañas, copy, lanzamientos y ciclos de crecimiento.
- Creador: [OpenClaudia](https://skillsagentes.com/creators/openclaudia.md) — 76 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Competitor Traffic Report](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/competitor-traffic-report.md): Genera un informe HTML autocontenible de tráfico competitivo: visitas mensuales (SimilarWeb), tráfico orgánico y Domain Rating (Ahrefs), con gráficos ranked, tendencias y tabla de datos.
- [Brand Dev](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/brand-dev.md): Obtiene datos de marca (nombre, descripción, logos, industria) desde la API de brand.dev y guarda los logos localmente.
- [Gsc Portfolio Audit](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/gsc-portfolio-audit.md): Audita TODAS las propiedades de Google Search Console a la vez: ranking por clics e impresiones con deltas, y diff de keywords por sitio (nuevas, suben, bajan, perdidas, o bien rankeadas sin clics).
- [Wechat Moments](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/wechat-moments.md): Clasifica y resume el feed de WeChat Moments (朋友圈) del usuario para que los eventos reales y la información genuina destaquen sobre la promoción, ponderando según cuánto le escribe el usuario a cada autor.
- [Podcast Edit](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/podcast-edit.md): Edita audio o video de podcast: recorta charla previa/posterior, quita muletillas, corta silencios, mejora el audio y aplica el mismo corte a una versión en video.

---

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