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minhnv0807/ai-business-skills/skills/en/18-referral-program-global/SKILL.md

18-referral-program-global

Use when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and attribution, fraud controls, and anti-spam compliance by region: TCPA for US, GDPR for EU, PDPA for SEA, LGPD for LATAM. Trigger on 'referral program', 'refer a friend', 'word of mouth', 'viral loop', 'how do I get customers to bring friends', 'affiliate rewards'. Also use when the user has happy customers and no system to use them

Source repository stars
553
Declared platforms
0
Static risk flags
0
Last source update
2026-08-17
Source checked
2026-08-25

Decision brief

What it does: where it fits

Word of mouth is the highest-LTV acquisition channel in every region. But the LEGAL framework around how you contact referred prospects differs HUGELY: TCPA (US, SMS), GDPR (EU, all channels), PDPA (SEA), LGPD (LATAM). Pick the right region variant or get fined.

Best for

  • Use when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and attribution, fraud controls, and anti-spam complianc…

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/minhnv0807/ai-business-skills --skill "skills/en/18-referral-program-global"
Safe inspection promptEditorial

Inspect the Agent Skill "18-referral-program-global" from https://github.com/minhnv0807/ai-business-skills/blob/958bd43b03afbc5afc42dfdb3fb2c087b23309e4/skills/en/18-referral-program-global/SKILL.md at commit 958bd43b03afbc5afc42dfdb3fb2c087b23309e4. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Workflow

    Review the “Workflow” section in the pinned source before continuing.

    Review and apply the “Workflow” source section.
  2. 02

    Step 0: Check global context

    Check .agents/product-marketing-context-global.md: - Yes - Read product, customer, region. Do NOT re-ask. - No - Suggest running product-marketing-context-global first.

    Yes - Read product, customer, region. Do NOT re-ask.No - Suggest running product-marketing-context-global first.Check .agents/product-marketing-context-global.md: - Yes - Read product, customer, region. Do NOT re-ask. - No - Suggest running product-marketing-context-global first.
  3. 03

    Step 1: Pick region variant

    Ask: "Which is your PRIMARY region: US, EU, SEA, or LATAM?"

    Ask: "Which is your PRIMARY region: US, EU, SEA, or LATAM?"
  4. 04

    Step 2: Prerequisites — does referral make sense?

    1. Product type? (DTC / SaaS / Service / Subscription) 2. Average AOV and LTV? 3. Existing happy customer count? 4. Goal: more new customers, lower CAC, or higher engagement?

    NPS = 40 (customers actively like you)Customer has natural reason to share (visible result, social currency, peer-relevant)AOV high enough to fund meaningful incentive (USD 50+ ideal)
  5. 05

    Step 3: Referral models

    When: Premium product where referee will buy regardless of incentive Examples: - Tesla referral program (referrer gets credit, new buyer pays full price) - Robinhood (referrer gets free stock; referee just signs up)

    Tesla referral program (referrer gets credit, new buyer pays full price)Robinhood (referrer gets free stock; referee just signs up)Airbnb (both get USD 25-50 credit)

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score95/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars553SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
minhnv0807/ai-business-skills
Skill path
skills/en/18-referral-program-global/SKILL.md
Commit
958bd43b03afbc5afc42dfdb3fb2c087b23309e4
License
MIT
Collected
2026-08-25
Default branch
master
View the original SKILL.md

Referral Program (Global)

Word of mouth is the highest-LTV acquisition channel in every region. But the LEGAL framework around how you contact referred prospects differs HUGELY: TCPA (US, SMS), GDPR (EU, all channels), PDPA (SEA), LGPD (LATAM). Pick the right region variant or get fined.


For newbies

Who is this skill for?

AudienceConcrete example
DTC brand wanting cheaper acquisitionAlready at USD 30 CAC; want USD 10 CAC via referral
SaaS adding viral loopExisting PMF; want negative CAC growth
Service business (coaching, agency)High-LTV; want client referrals
Subscription brandHigh retention; turn customers into ambassadors
E-commerce wanting AOV growthRefer a friend = both get discount

Who is this NOT for?

  • Vietnam-only referral -> Use 18-referral-program (VN skill) — Zalo / Messenger optimized
  • B2B enterprise sales -> ABM / partnership programs are different motion (not covered here)
  • Brand ambassador / affiliate -> Use 27-personal-brand-monetize-global for influencer-affiliate (when available)

30-second pre-read

This skill produces ONE referral program design with 6 components: model selection (1-way / 2-way / multi-tier), incentive math (% of LTV), tracking infrastructure, anti-fraud measures, launch sequence, and KPIs (K-factor, viral coefficient). Pick 1 of 4 region variants — the variant tunes the LEGAL rules for contacting referred prospects (especially via SMS/email).

3 common errors

  1. SMS-based referral in US without TCPA consent -> Up to USD 1,500 per text fines + class actions
  2. Email-blast referred contacts in EU -> GDPR violation; referral programs touching EU prospects need explicit consent from the prospect, NOT just the referrer
  3. Cash incentives that violate FTC endorsement rules -> "Refer a friend, get USD 100" requires disclosed material connection if referrer posts publicly

Why do you need this skill?

Without proper referral design:

  • US: Risk TCPA class action (USD 500-1,500 per message)
  • EU: GDPR violation if you store referred-prospect data without their consent
  • SEA: PDPA Singapore strict — most referral programs need both-side consent
  • LATAM: Brazil LGPD treats referred contacts as data subjects requiring consent
  • Universal: incentive math wrong -> losing money instead of growing
  • Universal: no anti-fraud -> 30-50% of "referrals" are self-referrals or bots

Plan the legal foundation correctly, get the incentive math right, ship a working viral loop.


Workflow

Step 0: Check global context file
    |-- exists -> read product / customer / region
    |-- missing -> suggest user run product-marketing-context-global first
Step 1: Pick region variant (US / EU / SEA / LATAM)
Step 2: Confirm prerequisites (NPS, AOV, LTV, customer base)
Step 3: Choose model (1-way / 2-way / multi-tier affiliate)
Step 4: Calculate incentive (15-25% of LTV)
Step 5: Set up tracking + anti-fraud
Step 6: Design referral flow (7 steps)
Step 7: Launch sequence (30-day plan)
Step 8: Measure K-factor / viral coefficient

Step 0: Check global context

Check .agents/product-marketing-context-global.md:

  • Yes -> Read product, customer, region. Do NOT re-ask.
  • No -> Suggest running product-marketing-context-global first.

Step 1: Pick region variant

Ask: "Which is your PRIMARY region: US, EU, SEA, or LATAM?"

Where do most of your customers (and their referrals) live?
    |-- US / Canada       --> 01-us.md    (TCPA SMS rules; CAN-SPAM email; CCPA data)
    |-- EU / EEA / UK     --> 02-eu.md    (GDPR consent for ALL channels)
    |-- Southeast Asia    --> 03-sea.md   (PDPA per country; mostly opt-in)
    |-- Latin America     --> 04-latam.md (LGPD Brazil; LFPDPPP Mexico)
    |-- Vietnam only      --> Use `18-referral-program` (VN skill)

Step 2: Prerequisites — does referral make sense?

When referral works

  • NPS >= 40 (customers actively like you)
  • Customer has natural reason to share (visible result, social currency, peer-relevant)
  • AOV high enough to fund meaningful incentive (USD 50+ ideal)
  • LTV high enough to justify CAC investment
  • Existing base of 100+ happy customers to seed

When referral does NOT work (skip this skill)

  • NPS < 20 (customers don't like you yet — fix retention first)
  • Sensitive product category (financial advice, intimate health) — referrals feel weird
  • Very low AOV (< USD 10) — incentive economics don't work
  • Pre-launch or no customer base — no one to refer

Ask the user

  1. Product type? (DTC / SaaS / Service / Subscription)
  2. Average AOV and LTV?
  3. Existing happy customer count?
  4. Goal: more new customers, lower CAC, or higher engagement?

Step 3: Referral models

Model 1: One-way (referrer gets reward, referee gets nothing)

When: Premium product where referee will buy regardless of incentive Examples:

  • Tesla referral program (referrer gets credit, new buyer pays full price)
  • Robinhood (referrer gets free stock; referee just signs up)

Pros: Lower cost Cons: Lower conversion (referee has no extra reason to buy now)

Model 2: Two-way (BOTH referrer and referee get rewards) — DEFAULT CHOICE

When: 80% of cases; psychological "win-win" feels generous to referrer Examples:

  • Airbnb (both get USD 25-50 credit)
  • Uber (both get USD 5-15 credit)
  • Dropbox (both get +500MB)

Pros: Higher conversion; referrer feels good giving "gift" Cons: Higher cost per acquisition

Standard 2-way structure:

Referrer gets: Discount / credit / free product / cash / reward
Referee gets: Discount / free trial / bonus on first order

Model 3: Multi-tier affiliate (% commission on revenue)

When: SaaS, high-ticket courses, premium DTC; want power-users / influencers Examples:

  • ConvertKit / Kit (30% recurring affiliate)
  • Shopify (200% of monthly fee per signup)
  • AWeber, Teachable, Coursera (10-50% per sale)

Pros: Attracts professional affiliates / influencers; scalable Cons: Requires legal disclosures (FTC US), tracking infrastructure (Rewardful, FirstPromoter), tax forms (W-9 in US, equivalents elsewhere)

Standard tier structure:

  • Tier 1: 10-30% commission on first purchase
  • Tier 2: 5-15% on recurring (next 90 days or lifetime)
  • Top tier: 30-50% for super-affiliates (negotiated)

Step 4: Incentive math (CRITICAL)

The formula

Total incentive (both sides combined) <= 15-25% of customer LTV

Worked example (Saas)

Product: Project management SaaS
Pricing: USD 49/month
Average tenure: 18 months
LTV: USD 882 (49 x 18)

Incentive cap: 15-25% of LTV = USD 130-220 total

Two-way structure:
  Referrer: 1 month free (USD 49 value) + USD 30 credit = USD 79 cost
  Referee: 50% off first 2 months = USD 49 cost
  Total: USD 128 (within cap)

Or simpler:
  Both get 1 month free = USD 98 total cost
  ROI: USD 882 LTV - USD 98 incentive = USD 784 net per successful referral

Worked example (DTC)

Product: Skincare subscription
AOV: USD 50 / box
Average orders: 10
LTV: USD 500

Incentive cap: USD 75-125 total

Two-way structure:
  Referrer: USD 30 credit (next box)
  Referee: USD 20 off first box
  Total: USD 50 (well within cap)

Reward formats — pros and cons

FormatProsConsBest for
CashHighest motivationHighest cost (out of pocket)Affiliate, B2B
Account creditKeeps customerUseless if customer leavesSubscription, marketplace
Discount on next purchasePay-on-purchaseCustomer may not returnE-commerce
Free product / serviceHigher perceived valueLogistics complexityService, beauty, F&B
Physical giftTangible delightOperational burdenPremium DTC
Points / rewardsHabit-formingRequires loyalty systemRetailers, airlines

Step 5: Tracking + anti-fraud

Tracking tools (region-agnostic)

ToolBest forPricing
ReferralCandyShopify DTCUSD 49+/mo
RewardfulSaaS affiliateUSD 49+/mo
FirstPromoterSaaS affiliateUSD 49+/mo
FriendbuyMid-market DTCUSD 249+/mo
Mention MePremium DTCEnterprise
PartnerStackB2B SaaS partnershipsUSD 500+/mo
TalkableEnterprise DTCEnterprise
Build in-houseFull controlCustom

Anti-fraud measures

RiskDefense
Self-referral via second accountMatch phone, address, payment, IP, device fingerprint
Public posting on coupon sitesLimit 3-5 redemptions per code; require minimum AOV
Bot / script signupsCaptcha; rate limit; manual review for large batches
Cancel-after-reward30-day reward holding period (after return window)
Influencer abuseCap individual referrer rewards monthly; flag outliers
Referee buys then refundsHold rewards until past return window; partial reward if partial refund

Step 6: 7-step referral flow

Step 1: Customer has good experience
   |--> Trigger when NPS >= 7 OR after 2nd purchase OR completion of service

Step 2: Customer sees referral CTA
   |--> Email after delivery, dashboard widget, post-purchase page, account menu

Step 3: Customer gets unique code/link
   |--> Personalized: "JANE25" or unique link with UTM tracking

Step 4: Customer shares (multiple channels)
   |--> Built-in share: WhatsApp, Email, SMS, Copy link, Twitter/X
   |--> Pre-filled message in customer's voice

Step 5: Friend clicks / enters code
   |--> Landing page tailored to referral (not generic homepage)
   |--> Reward visible upfront ("Get USD 20 off")

Step 6: Friend converts (purchase)
   |--> Tracking pixel fires; both parties receive notification
   |--> Reward delivered automatically (or held for 30 days)

Step 7: Cycle continues
   |--> Friend now eligible to refer; nudge after first delivery
   |--> Top referrers get bonus tiers ("3 referrals = VIP")

Step 7: 30-day launch sequence

Week 1: Setup

  • Choose model (1-way / 2-way / multi-tier)
  • Finalize incentive math (LTV calculation, reward structure)
  • Pick tool (ReferralCandy / Rewardful / build)
  • Create landing page for referee
  • Set up email/SMS automation flows
  • Set up tracking + attribution
  • Legal review (per region variant)

Week 2: Soft launch (seed)

  • Email top 50-100 happiest customers (NPS 9-10)
  • Track first referrals; fix bugs
  • Iterate on copy / friction points
  • Verify reward delivery automation

Week 3: Public launch

  • Email full customer base
  • Add referral CTA to:
    • Order confirmation page
    • Post-delivery email
    • Account dashboard
    • Receipt PDF / packaging insert (offline)
  • Social posts on owned channels
  • Optional: paid promotion to existing customers ("Tell friends, both save")

Week 4: Optimize

  • Identify top sharers (top 10%)
  • Bonus push: "You're in top 10 — extra reward this month"
  • A/B test:
    • Landing page (referral vs. cold)
    • Reward amount (USD 20 vs USD 30)
    • Channel emphasis (email vs. SMS vs. WhatsApp)

Step 8: KPIs and viral coefficient

Key metrics

MetricFormulaBenchmark
Share rateReferrers / total customers10% basic, 20% good, 30%+ excellent
Conversion rateSuccessful redemptions / shares15% basic, 25% good, 40%+ excellent
Average referrals per sharerSuccessful refs / sharers1.2 basic, 2+ good, 3+ excellent
K-factor (viral coefficient)Share rate x Conversion rate x Avg refs0.3-0.5 typical, 1.0+ true viral
CAC via referralReward cost / referred customers30-50% of paid CAC
Referred customer LTVAvg LTV of referred customersOften 1.2x non-referred

K-factor interpretation

K = 0.3 -> 100 customers bring 30 new -> sub-viral, supplements other channels
K = 0.7 -> 100 customers bring 70 new -> strong supplement
K = 1.0 -> 100 customers bring 100 new -> equilibrium (each customer replaces one)
K > 1.0 -> Viral loop! Exponential growth (rare but transformative)

Most healthy referral programs target K = 0.4-0.7. K > 1 is rare and usually requires unique product mechanics (Dropbox, WhatsApp, Calendly).


Output template

# Referral Program - [Brand]
Region: [US/EU/SEA/LATAM]
Date: [YYYY-MM-DD]

## 1. Goal
[New customers / Lower CAC / Higher LTV / Multiple]

## 2. Prerequisites confirmed
- NPS: [X]
- AOV: [USD/EUR/etc.]
- LTV: [calculated]
- Customer base: [N]

## 3. Model
[1-way / 2-way / multi-tier]

## 4. Incentive structure
- Referrer gets: [reward + cost]
- Referee gets: [reward + cost]
- Total cost: [USD X, ~Y% of LTV]

## 5. Tracking tool
[ReferralCandy / Rewardful / etc.]

## 6. Anti-fraud measures
[List all 5-7 measures applied]

## 7. Referral flow (7 steps)
[Description per step]

## 8. Launch sequence (30 days)
[Week 1-4 plan]

## 9. KPIs
[Share rate, Conversion rate, K-factor target]

## 10. Legal compliance
[Per region variant — see specific variant file]

Quality checklist

  • Region variant chosen (US/EU/SEA/LATAM)
  • NPS >= 40 confirmed (have happy customers)
  • Total incentive cost <= 25% of LTV
  • 2-way model unless strong reason for 1-way
  • Tracking tool integrated and tested
  • Anti-fraud measures live (5+)
  • Reward delivery automated within 24h
  • Legal compliance per region (TCPA / GDPR / PDPA / LGPD)
  • Landing page for referees built
  • K-factor target documented; measure at 30 / 60 / 90 days

Related skills

  • product-marketing-context-global — foundation
  • 14-email-marketing-global — email-driven referral mechanics
  • 27-personal-brand-monetize-global — affiliate / creator program (when available)
  • references/global-legal-compliance — deep legal reference

Global Skill 18 (Referral Program) | Over Powers Agency | v1.0.0

Frequently asked questions

What to verify before installation and use

What does the 18-referral-program-global source document cover?

Word of mouth is the highest-LTV acquisition channel in every region. But the LEGAL framework around how you contact referred prospects differs HUGELY: TCPA (US, SMS), GDPR (EU, all channels), PDPA (SEA), LGPD (LATAM). Pick the right region variant or get fined.

How do I install 18-referral-program-global?

The source record exposes this install command: npx skills add https://github.com/minhnv0807/ai-business-skills --skill "skills/en/18-referral-program-global". Inspect the command and pinned source before running it.

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