referral-program（推荐计划）

Referral Program

Production-grade referral and affiliate program framework covering the 4-stage referral loop, incentive design methodology, trigger moment optimization, share mechanics, viral coefficient modeling, affiliate program architecture, and systematic optimization playbook. Designed to build programs that compound, not collect dust.

Use when

• The user asks to "design a referral program", "launch an affiliate program", or "improve viral growth"
• The decision between customer referral vs affiliate program needs to be made
• An existing referral program has stalled (K-factor <1, low share rate, low referred-user conversion)
• Reward structure needs sizing against CAC, margin, or LTV
• Trigger moments need to be identified (when to ask, which in-product events, which lifecycle emails)
• The user says "word-of-mouth isn't working" or "we want to add a refer-a-friend flow"

Table of Contents

• Referral vs Affiliate Decision
• The 4-Stage Referral Loop
• Incentive Design
• Trigger Moment Architecture
• Share Mechanics
• Referred User Experience
• Viral Coefficient Modeling
• Affiliate Program Framework
• Optimization Playbook
• Metrics and Benchmarks
• Program Copy Templates
• Output Artifacts
• Related Skills

Referral vs Affiliate Decision

Decision rule: If your customers are enthusiastic and social, start with customer referrals. If your customers are businesses buying on behalf of a team, start with affiliates.

The 4-Stage Referral Loop

Every referral program runs on this loop. If any stage is weak, the entire program underperforms. Work the stages in order — a broken Stage 1 (trigger) can't be fixed by better rewards at Stage 4.

[Trigger Moment] → [Share Action] → [Referred User Converts] → [Reward Delivered] → Loop

• *Validate Stage 1:* trigger fires on a real satisfaction event, not at signup or in a generic monthly email
• *Validate Stage 2:* share friction is <3 taps/clicks and pre-filled copy is channel-specific
• *Validate Stage 3:* referred user lands on a referral-specific page, not the generic homepage
• *Validate Stage 4:* reward delivery is automatic and notified (manual reward ops kill the loop)

Stage 1: Trigger Moment

When you ask customers to refer. Timing is everything.

High-signal trigger moments:

What does NOT work:

• Asking at signup (no value experienced yet)
• Asking in every email footer (becomes invisible)
• Asking during onboarding (too early, too distracted)
• Generic monthly "refer a friend" email (no trigger, no urgency)

Stage 2: Share Action

Remove every point of friction between wanting to share and actually sharing.

Required share mechanics:

• Personal referral link (unique per user, trackable)
• Pre-filled share message (editable, not locked)
• Multiple share channels: email invite, link copy, social share
• For B2B: Slack/Teams share option
• One-click send on mobile (native share sheet)

Share message rules:

• Written in first person (sounds like it is from a friend, not marketing)
• Includes the specific benefit the referrer experienced
• Short (2-3 sentences max)
• Includes the referral link with clear CTA

Stage 3: Referred User Converts

The referred user lands on your product. Their experience must:

• Show personalization: "Your friend [Name] invited you"
• Display the incentive clearly above the fold
• Reduce signup friction (pre-fill email if available, offer SSO)
• Track attribution from landing through conversion (multi-session)

Stage 4: Reward Delivered

The reward must be fast and clear. Delayed rewards break the loop.

Incentive Design

Single-Sided vs Double-Sided

Decision rule: If referral rate < 1%, go double-sided. If > 5%, single-sided is more profitable.

Reward Types

Tiered Rewards (Gamification)

For referrers who go beyond 1 referral:

Rules:

• Maximum 3 tiers (more is confusing)
• Each tier should feel meaningfully better, not just marginally
• Show progress toward next tier in the dashboard

Reward Economics

Maximum reward per referral = LTV x Target referral CAC ratio

Example:
  Average LTV: $2,000
  Target referral CAC: 15% of LTV
  Maximum reward: $300

  If double-sided:
    Referrer reward: $150
    Referred reward: $150 (or equivalent credit/discount)

Trigger Moment Architecture

In-Product Trigger Points

Email Trigger Points

Share Mechanics

Share Channel Priority

Share Message Templates

Email (B2B):

Subject: I think you'd like [Product]

Hey [Name],

I've been using [Product] for [task/workflow] and it's saved me [specific benefit].
Thought you might find it useful too.

Here's my referral link -- you'll get [referred benefit] when you sign up:
[Referral Link]

[Referrer Name]

Social (B2C):

Been using [Product] for [timeframe] and I'm genuinely impressed.
[Specific thing I love about it].

If you want to try it, use my link and we both get [reward]:
[Referral Link]

Referred User Experience

Referral Landing Page

┌──────────────────────────────────────────┐
│  [Referrer Name] invited you to          │
│  [Product]                               │
│                                          │
│  [Referrer's photo if available]         │
│                                          │
│  Your reward: [Incentive details]        │
│                                          │
│  [Sign Up and Claim Your Reward]         │
│                                          │
│  What [Product] does:                    │
│  - Benefit 1                            │
│  - Benefit 2                            │
│  - Benefit 3                            │
│                                          │
│  "Quote from a customer"                │
└──────────────────────────────────────────┘

Attribution Rules

Viral Coefficient Modeling

K-Factor Calculation

K = i x c

i = average invitations sent per user
c = conversion rate of invitations

Example:
  Average user sends 3 invitations
  15% of those invitations convert
  K = 3 x 0.15 = 0.45

K > 1.0 = viral growth (rare outside social products)
K = 0.3-0.7 = strong referral contribution
K < 0.1 = referral program needs work

Improving K-Factor

Affiliate Program Framework

Program Structure

Affiliate Tier System

Affiliate Toolkit

Every affiliate needs:

• Unique tracking link
• Pre-written email copy (3 variants)
• Social media copy (Twitter, LinkedIn)
• Banner ads (3 sizes minimum)
• Product description sheet (features, benefits, pricing)
• Comparison table (vs competitors)
• Landing page optimized for affiliate traffic

Affiliate Recruitment

Recruitment rule: Personalized outreach only. Generic "join our affiliate program" emails convert at < 1%.

Optimization Playbook

Diagnose Before Optimizing

Optimization Priority

1. Fix awareness first -- If users do not know the program exists, nothing else matters
2. Fix the share flow -- If users know but do not share, the friction is too high
3. Fix the referred experience -- If users share but referrals do not convert, the landing page fails
4. Optimize the incentive -- Only change the reward after the mechanics work

Metrics and Benchmarks

Key Metrics

Revenue Impact Model

Monthly referral revenue = Active users x Referral rate x Conversion rate x ACV / 12

Example:
  10,000 active users x 10% referral rate x 20% conversion rate x $600 ACV / 12
  = $10,000/month in new referral-driven MRR

  Annual impact: $120,000 in new ARR
  Reward cost (at $50/referral): 200 referrals x $50 = $10,000
  ROI: 12x return on reward investment

Program Copy Templates

In-App Prompt

Know someone who'd love [Product]?

Give [reward], Get [reward]

Share your unique link and you'll both get [reward] when they sign up.

[Share Now]  [Learn More]

Referral Dashboard

Your Referral Stats

Referrals Sent: [N]
Friends Joined: [N]
Rewards Earned: $[X]

[Share Your Link]

Your link: [referral-url]  [Copy]

Progress to next reward:
[Progress bar: 2 of 3 referrals for Silver tier]

Referral Email (Post-Activation)

Subject: Share [Product] and earn [reward]

Hi [Name],

Glad you're enjoying [Product]!

Share your personal referral link with colleagues, and you'll both get [reward]:

[Referral Link]

So far, you've earned $[X] from [N] referrals.

[Share Now]

Output Artifacts

Tool Reference

1. referral_economics_calculator.py

Calculates referral program economics including reward sizing, K-factor, referral CAC, ROI projections, and break-even analysis. Models double-sided vs single-sided reward structures.

python scripts/referral_economics_calculator.py program.json --format text
python scripts/referral_economics_calculator.py program.json --format json

2. referral_funnel_analyzer.py

Analyzes the 4-stage referral loop (trigger, share, convert, reward) with stage-over-stage conversion, identifies the weakest stage, and provides prioritized improvement recommendations.

python scripts/referral_funnel_analyzer.py funnel.json --format text
python scripts/referral_funnel_analyzer.py funnel.json --format json

3. affiliate_commission_modeler.py

Models affiliate program commission structures across tier levels. Calculates per-tier economics, lifetime partner value, and compares commission models (flat fee vs recurring percentage).

python scripts/affiliate_commission_modeler.py affiliate.json --format text
python scripts/affiliate_commission_modeler.py affiliate.json --format json

Troubleshooting

Success Criteria

• K-factor reaches 0.3-0.7 range within 90 days of program launch (strong referral contribution without requiring virality)
• Referral CAC is below 50% of other acquisition channel CAC
• Active referrer percentage reaches 5-15% of active users
• Referral-sourced revenue contributes 10-25% of total new revenue within 6 months
• Referred customer LTV exceeds non-referred customer LTV (typical: 16-25% higher per industry data)
• Reward redemption rate exceeds 70% within 30 days of earning
• Double-sided program achieves 2x+ conversion rate compared to single-sided (validate within first 1,000 referrals)

Scope & Limitations

In scope: Customer referral program design (4-stage loop), incentive structure (single-sided, double-sided, tiered), trigger moment architecture, share mechanics, referral landing page specifications, viral coefficient modeling, affiliate program framework (commission models, tier systems, recruitment), and systematic optimization playbook.

Out of scope: Referral landing page visual design and CRO (use page-cro), signup flow optimization for referred users (use signup-flow-cro), post-signup onboarding for referred users (use onboarding-cro), churn prevention for referred customers (use churn-prevention), and reward pricing alignment (use pricing-strategy). Scripts operate on local data only -- no integrations with referral platforms (ReferralHero, Viral Loops, PartnerStack, etc.).

Limitations: K-factor benchmarks assume consumer or prosumer SaaS; B2B enterprise referral programs have different dynamics (lower K but higher per-referral value). Affiliate commission benchmarks (20-30% recurring) are SaaS-specific; marketplace and e-commerce commissions follow different models. Attribution windows (30-90 day cookies) face increasing limitations from browser privacy features (Safari ITP, Chrome third-party cookie deprecation). Revenue projections are estimates based on provided conversion rates.

Integration Points

• pricing-strategy -- Referral reward sizing must align with pricing margins and LTV; reward should be <30% of first payment
• signup-flow-cro -- Referred user signup flow should pre-fill email, show referrer context, and minimize friction
• onboarding-cro -- Referred users may need different onboarding path (they arrive with context from the referrer)
• churn-prevention -- Monitor referred customer retention separately; high referral churn wastes acquisition spend
• page-cro -- Referral landing page conversion optimization follows page-cro methodology
• popup-cro -- Post-purchase or post-milestone popups are natural referral trigger points

Anti-patterns

Related Skills

• pricing-strategy -- Use when referral reward sizing needs to align with pricing and margin structure.
• signup-flow-cro -- Use for optimizing the signup flow that referred users go through.
• onboarding-cro -- Use for optimizing the post-signup experience for referred users.
• churn-prevention -- Use to ensure referred customers retain at high rates (referral CAC is wasted if they churn).
• page-cro -- Use for optimizing the referral landing page conversion rate.