18-referral-program-global

name	18-referral-program-global
description	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. Not for — running your own community space, see `28-community-building-global`; posting inside third-party communities, see `38-community-seeding-global`; email flow mechanics, see `14-email-marketing-global`.
metadata	{"version":"1.0.1","category":"operations"}
license	MIT
triggers	["referral program","refer a friend","word of mouth","viral loop","referral marketing"]
related	["product-marketing-context-global","14-email-marketing-global","27-personal-brand-monetize-global","references/global-legal-compliance"]

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.

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For newbies

Who is this skill for?

Audience	Concrete example
DTC brand wanting cheaper acquisition	Already at USD 30 CAC; want USD 10 CAC via referral
SaaS adding viral loop	Existing PMF; want negative CAC growth
Service business (coaching, agency)	High-LTV; want client referrals
Subscription brand	High retention; turn customers into ambassadors
E-commerce wanting AOV growth	Refer 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

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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.

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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

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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.

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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)

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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?

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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)

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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

Format	Pros	Cons	Best for
Cash	Highest motivation	Highest cost (out of pocket)	Affiliate, B2B
Account credit	Keeps customer	Useless if customer leaves	Subscription, marketplace
Discount on next purchase	Pay-on-purchase	Customer may not return	E-commerce
Free product / service	Higher perceived value	Logistics complexity	Service, beauty, F&B
Physical gift	Tangible delight	Operational burden	Premium DTC
Points / rewards	Habit-forming	Requires loyalty system	Retailers, airlines

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Step 5: Tracking + anti-fraud

Tracking tools (region-agnostic)

Tool	Best for	Pricing
ReferralCandy	Shopify DTC	USD 49+/mo
Rewardful	SaaS affiliate	USD 49+/mo
FirstPromoter	SaaS affiliate	USD 49+/mo
Friendbuy	Mid-market DTC	USD 249+/mo
Mention Me	Premium DTC	Enterprise
PartnerStack	B2B SaaS partnerships	USD 500+/mo
Talkable	Enterprise DTC	Enterprise
Build in-house	Full control	Custom

Anti-fraud measures

Risk	Defense
Self-referral via second account	Match phone, address, payment, IP, device fingerprint
Public posting on coupon sites	Limit 3-5 redemptions per code; require minimum AOV
Bot / script signups	Captcha; rate limit; manual review for large batches
Cancel-after-reward	30-day reward holding period (after return window)
Influencer abuse	Cap individual referrer rewards monthly; flag outliers
Referee buys then refunds	Hold rewards until past return window; partial reward if partial refund

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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")

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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)

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Step 8: KPIs and viral coefficient

Key metrics

Metric	Formula	Benchmark
Share rate	Referrers / total customers	10% basic, 20% good, 30%+ excellent
Conversion rate	Successful redemptions / shares	15% basic, 25% good, 40%+ excellent
Average referrals per sharer	Successful refs / sharers	1.2 basic, 2+ good, 3+ excellent
K-factor (viral coefficient)	Share rate x Conversion rate x Avg refs	0.3-0.5 typical, 1.0+ true viral
CAC via referral	Reward cost / referred customers	30-50% of paid CAC
Referred customer LTV	Avg LTV of referred customers	Often 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).

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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]

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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

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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

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