referral-program · diff

v1.0.0 to v1.1.0

25 added, 554 removed. Audit A to A.

---

name: referral-program

description: >

Referral and affiliate program design covering referral loop architecture,

incentive design, trigger moment optimization, viral coefficient modeling,

affiliate program structure, and optimization playbook.

license: MIT + Commons Clause

metadata:

- version: 1.0.0

+ version: 1.1.0

author: borghei

category: business-growth

- updated: 2026-03-31

+ updated: 2026-06-15

tags: [referral, affiliate, growth, viral, word-of-mouth, acquisition]

---

# 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

+ ## Core Capabilities

+ - **Program type & loop design** — referral vs affiliate decision, plus the 4-stage loop (trigger → share → convert → reward)

+ - **Incentive design** — single- vs double-sided, reward types, tiered gamification, reward economics against LTV/CAC

+ - **Trigger & share mechanics** — in-product and email trigger points, share channel priority, first-person share copy

+ - **Referred-user experience** — referral landing page, attribution rules, program copy set (prompts, emails, dashboards)

+ - **Growth math** — K-factor modeling, revenue impact models, and lever-by-lever K improvement

+ - **Affiliate framework** — commission models, tier systems, partner toolkit, recruitment

+ - **Optimization** — diagnose-before-optimize playbook, metric benchmarks, troubleshooting, and three Python tools

+ ## When to Use

- 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](#referral-vs-affiliate-decision)

- - [The 4-Stage Referral Loop](#the-4-stage-referral-loop)

- - [Incentive Design](#incentive-design)

- - [Trigger Moment Architecture](#trigger-moment-architecture)

- - [Share Mechanics](#share-mechanics)

- - [Referred User Experience](#referred-user-experience)

- - [Viral Coefficient Modeling](#viral-coefficient-modeling)

- - [Affiliate Program Framework](#affiliate-program-framework)

- - [Optimization Playbook](#optimization-playbook)

- - [Metrics and Benchmarks](#metrics-and-benchmarks)

- - [Program Copy Templates](#program-copy-templates)

- - [Output Artifacts](#output-artifacts)

- - [Related Skills](#related-skills)

- ---

- ## Referral vs Affiliate Decision

- | Factor | Customer Referral | Affiliate Program |

- |--------|------------------|-------------------|

- | Who promotes | Your existing customers | External partners, bloggers, influencers |

- | Motivation | Loyalty, reward, social currency | Commission, audience monetization |

- | Best for | B2C, prosumer, SMB SaaS | B2B SaaS, high LTV, content-heavy niches |

- | Activation | Triggered by product satisfaction | Recruited and onboarded proactively |

- | Payout | Account credit, discount, or cash reward | Revenue share or flat fee per conversion |

- | CAC impact | Low -- reward is typically < 30% of first payment | Variable -- commission determines economics |

- | Scale | Scales with active user base | Scales with partner recruitment |

- **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:**

- | Trigger | Why It Works | When to Fire |

- |---------|-------------|-------------|

- | After aha moment | User just experienced core value, highest satisfaction | After activation event |

- | After milestone | Celebrates achievement, creates social sharing impulse | "You just saved your 100th hour" |

- | After great support | Gratitude creates sharing impulse | Post-resolution, NPS 9-10 |

- | After renewal/upgrade | Commitment signal, satisfied customer | Day of renewal |

- | After public win | Customer tweets about you or posts a case study | Within 24 hours |

- | After team growth | New team members = new potential referrers | After Nth team member joins |

- **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.

- | Action | Implementation |

- |--------|---------------|

- | Immediate confirmation | "Your friend just signed up! Here's your reward" |

- | In-product visibility | Dashboard: "2 friends joined -- you've earned $40" |

- | Email notification | Instant notification when referral converts |

- | Easy redemption | Auto-applied credit or one-click claim |

- ---

- ## 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:

- | Tier | Reward | Design Rule |

- |------|--------|-------------|

- | 1 referral | $20 credit | Easy to reach, immediate gratification |

- | 3 referrals | $75 credit + bonus feature | Meaningful step-up, not just 3x |

- | 10 referrals | $300 cash + ambassador status | Significant reward, social recognition |

- **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

- | Location | Trigger Type | Copy Example |

- |----------|-------------|-------------|

- | Dashboard widget | Persistent, low-key | "Know someone who'd love [Product]? Give $20, get $20" |

- | Post-milestone modal | Celebration moment | "You just hit 1,000 contacts! Share [Product] with a colleague?" |

- | Settings/account page | Always available | "Referral Program: Earn $20 for every friend who joins" |

- | Success state | After positive outcome | "Great results! Know someone who'd find this useful?" |

- | Team invite flow | Natural sharing moment | "Or invite them via referral link and you both get $20" |

- ### Email Trigger Points

- | Trigger | Email Content | Timing |

- |---------|-------------|--------|

- | Post-activation (first value delivered) | "Loving [Product]? Share it and earn rewards" | 3-5 days after activation |

- | Post-NPS (score 9-10) | "Glad you love us! Here's an easy way to share" | Immediately after NPS |

- | Post-renewal | "Thanks for staying with us! Share the love" | Day of renewal |

- | Monthly digest | "Your referral status: [N] referrals, $[X] earned" | Monthly |

- ---

- ## Share Mechanics

- ### Share Channel Priority

- |---------|-------------|-------------|----------------|

- | SMS | Medium (mobile) | Low | Pre-filled text message |

- ### 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

- | Scenario | Attribution |

- |----------|-----------|

- | User clicks link and signs up same session | Attributed to referrer |

- | User clicks link, returns 3 days later, signs up | Attributed (30-day cookie) |

- | User clicks link but signs up via Google search | Attributed if within cookie window |

- | User receives two referral links from different people | First click wins (or last click -- choose one rule) |

- | Referred user was already a lead in CRM | Exclude from referral program |

- ---

- ## 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

- | Element | Recommendation |

- |---------|---------------|

- | Commission model | 20-30% recurring for SaaS, or flat fee per conversion |

- | Cookie window | 30 days minimum, 90 days for B2B |

- | Payment terms | Monthly, $50 minimum threshold |

- | Payment method | PayPal, wire transfer, or affiliate platform payout |

- | Tracking platform | PartnerStack, Impact, Rewardful, or custom |

- ### 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

- | Source | Approach | Volume |

- |--------|---------|--------|

- | Existing customers (top advocates) | Personal outreach | 10-20 initial |

- | Complementary SaaS companies | Partnership pitch | 5-10 |

- | Industry bloggers/creators | Outreach with product demo | 10-20 |

- | Newsletter curators | Sponsorship conversion to affiliate | 5-10 |

- | Review sites | Listing with affiliate link | Ongoing |

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

- ---

- ## Optimization Playbook

- ### Diagnose Before Optimizing

- |--------|-----------|----------|-----|

- | Program awareness | > 40% of active users know it exists | Promote in-app, post-activation emails, dashboard widget |

- | Active referrers | 5-15% of active users | Improve trigger moments, timing, and incentive |

- | Share rate | 20-40% of those who see the prompt | Simplify share flow, improve message copy |

- | Referred conversion rate | 15-25% | Improve referral landing page, add incentive |

- | Reward redemption | > 70% within 30 days | Reduce redemption friction, send reminders |

- ### 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

- | Metric | Formula | Target |

- |--------|---------|--------|

- | Referral rate | Referrals sent / Active users | 5-15% |

- | Active referrers % | Users who sent 1+ referral / Active users | 5-15% |

- | Referral conversion rate | Referred signups / Referrals sent | 15-25% |

- | Referral CAC | Total reward cost / Referral-acquired customers | < 50% of other CAC |

- | Referral revenue % | Revenue from referred customers / Total revenue | 10-25% |

- | K-factor | Invitations per user x Conversion rate | 0.3-0.7 |

- | Referred customer LTV | LTV of referred vs non-referred | Referred should be higher |

- ### 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

- | Artifact | Format | Description |

- |----------|--------|-------------|

- | Referral Program Design | Full spec | Loop design, incentive structure, trigger moments, share mechanics |

- | Incentive ROI Model | Revenue calculation | Reward sizing against LTV/CAC with multiple scenarios |

- | Program Copy Set | Complete copy | In-app prompts, emails, share messages, landing page |

- | Affiliate Program Spec | Structure + toolkit | Commission model, tiers, recruitment list, partner assets |

- | K-Factor Model | Calculation + improvement plan | Current K, target K, lever-by-lever improvement plan |

- | Optimization Audit | Metric scorecard | Current metrics vs benchmarks with prioritized fixes |

- | Dashboard Specification | UI design | Referral stats, link sharing, progress tracking |

- ---

- ## 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.

- ```bash

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

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

- ```

- | Flag | Type | Description |

- |------|------|-------------|

- | `program.json` | positional | Path to JSON file with program economics data |

- | `--format` | optional | Output format: `text` (default) or `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.

- ```bash

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

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

- ```

- | Flag | Type | Description |

- |------|------|-------------|

- | `funnel.json` | positional | Path to JSON file with referral funnel metrics |

- | `--format` | optional | Output format: `text` (default) or `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).

- ```bash

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

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

- ```

- | Flag | Type | Description |

- |------|------|-------------|

- | `affiliate.json` | positional | Path to JSON file with affiliate program data |

- | `--format` | optional | Output format: `text` (default) or `json` |

- ---

- ## Troubleshooting

- | Problem | Likely Cause | Resolution |

- |---------|-------------|------------|

- | Program awareness below 40% of active users | Referral program is buried in settings or only mentioned in email footers | Add persistent dashboard widget, post-activation prompt, and post-NPS trigger; desktop sharing now outperforms mobile (2026 data) |

- | Users see prompt but share rate is below 20% | Share flow has too much friction or pre-filled message is not compelling | Add one-click copy link, native share sheet on mobile, pre-filled first-person message; ensure multiple channels (email, Slack, social) |

- | Referrals sent but conversion rate below 15% | Referral landing page lacks personalization or incentive is not prominent | Add referrer name/photo, display incentive above fold, reduce signup friction; mobile-referred users convert 2-3x (2026 data) |

- | K-factor below 0.1 | Fundamental program design issue -- either low awareness, high friction, or weak incentive | Diagnose in sequence: fix awareness first, then share flow, then landing page, then incentive (optimize mechanics before rewards) |

- | Reward redemption below 70% | Reward delivery is delayed or redemption process is complicated | Auto-apply credits immediately, send instant notification, make redemption one-click; show running total in dashboard |

- | Referred customers churn faster than organic | Referral incentive attracts low-intent users or onboarding for referred users is inadequate | Shift from cash/discount rewards to product-value rewards (feature unlock, extended trial); add referred-user onboarding path |

- | Affiliate partners not producing conversions | Partners lack proper toolkit or audience mismatch | Provide pre-written copy, banner assets, comparison tables, and dedicated landing pages; audit partner audience fit |

+ ## Quick Start

- ---

+ 1. **Pick the program type** — use the Referral vs Affiliate Decision table (enthusiastic/social customers → referral; team buyers → affiliate).

+ 2. **Build the loop in order** — trigger → share → convert → reward; a broken Stage 1 can't be fixed by a bigger reward at Stage 4.

+ 3. **Size the incentive** — cap reward at <30% of first payment; go double-sided if referral rate <1%.

+ 4. **Model and validate** — run the scripts (`referral_economics_calculator.py`, `referral_funnel_analyzer.py`, `affiliate_commission_modeler.py`) to size rewards, find the weakest stage, and model affiliate tiers.

+ 5. **Optimize by priority** — fix awareness first, then share flow, then referred experience, then the incentive.

- ## Success Criteria

+ ## References

- - 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)

+ Load the reference that matches the task — keep this file lean and pull detail on demand:

- ---

+ - **[references/loop-and-incentives.md](references/loop-and-incentives.md)** — Referral vs Affiliate decision table, the full 4-stage loop with per-stage tables, incentive design (single/double-sided, reward types, tiers, economics), and trigger moment architecture. Read when designing the core program.

+ - **[references/share-and-experience.md](references/share-and-experience.md)** — share channel priority, share message templates, referral landing page layout, attribution rules, and the program copy set (in-app prompt, dashboard, post-activation email). Read when building the sharing flow and referred-user experience.

+ - **[references/modeling-and-affiliate.md](references/modeling-and-affiliate.md)** — K-factor calculation and improvement levers, plus the full affiliate framework (commission structure, tier system, toolkit, recruitment). Read when modeling growth math or designing an affiliate program.

+ - **[references/optimization-and-operations.md](references/optimization-and-operations.md)** — optimization playbook, key metrics and benchmarks, revenue impact model, output artifacts, full tool reference, troubleshooting table, success criteria, and anti-patterns. Read when diagnosing a stalled program or operating the scripts.

## 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

- | Anti-pattern | Failure mode | Fix |

- |--------------|--------------|-----|

- | Asking at signup instead of after the aha moment | Referrer has no value experience to share; share rates under 2% | Fire the trigger after activation or milestone — never before value is delivered |

- | "Refer a friend" link buried in the account menu | Discovery rate near zero; program appears to "not work" | Surface at trigger moments in-product (modal, banner, post-action), not in settings |

- | Single-sided reward where only the referrer benefits | Referred users feel exploited; conversion on referral landing page drops | Use double-sided rewards — both sides get value, aligned with program positioning |

- | Reward sized larger than first-payment margin | Program grows but unit economics invert; CAC exceeds LTV | Cap reward at 30% of first payment (or <1 payback period); model before launch with referral_economics_calculator.py |

- | Manual reward fulfillment | Delay between referral and reward kills the loop; referrer disengages | Automate reward delivery with in-app notification; trigger within 24 hours of referred user's qualifying event |

- | Confusing affiliate program with customer referral | Wrong activation (customers don't behave like affiliates); wrong attribution (affiliates don't behave like advocates) | Decide the program type first using the Referral vs Affiliate Decision table; don't merge |

- | Ignoring K-factor, optimizing only for share count | Shares grow but referred conversions don't; false sense of progress | Track K = shares × conversion rate; optimize the weakest stage, not the most visible one |

- | Generic monthly "invite friends" email with no trigger | Becomes inbox noise; unsubscribe lift with no conversion lift | Event-triggered emails only — milestone, renewal, support-win, team-growth |

- ---

- ## 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.