18 referral program global skill
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.
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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?
| 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-globalfor 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
- SMS-based referral in US without TCPA consent -> Up to USD 1,500 per text fines + class actions
- Email-blast referred contacts in EU -> GDPR violation; referral programs touching EU prospects need explicit consent from the prospect, NOT just the referrer
- 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-globalfirst.
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
- Product type? (DTC / SaaS / Service / Subscription)
- Average AOV and LTV?
- Existing happy customer count?
- 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
| 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 |
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 |
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
| 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).
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— foundation14-email-marketing-global— email-driven referral mechanics27-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
| 1 | |
| 2 | name 18-referral-program-global |
| 3 | 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`." |
| 4 | metadata |
| 5 | version 1.0.1 |
| 6 | category operations |
| 7 | license MIT |
| 8 | triggers |
| 9 | - "referral program" |
| 10 | - "refer a friend" |
| 11 | - "word of mouth" |
| 12 | - "viral loop" |
| 13 | - "referral marketing" |
| 14 | related |
| 15 | - product-marketing-context-global |
| 16 | - 14-email-marketing-global |
| 17 | - 27-personal-brand-monetize-global |
| 18 | - references/global-legal-compliance |
| 19 | |
| 20 | |
| 21 | # Referral Program (Global) |
| 22 | |
| 23 | > 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. |
| 24 | |
| 25 | |
| 26 | |
| 27 | ## For newbies |
| 28 | |
| 29 | ### Who is this skill for? |
| 30 | |
| 31 | | Audience | Concrete example | |
| 32 | |----------|------------------| |
| 33 | | DTC brand wanting cheaper acquisition | Already at USD 30 CAC; want USD 10 CAC via referral | |
| 34 | | SaaS adding viral loop | Existing PMF; want negative CAC growth | |
| 35 | | Service business (coaching, agency) | High-LTV; want client referrals | |
| 36 | | Subscription brand | High retention; turn customers into ambassadors | |
| 37 | | E-commerce wanting AOV growth | Refer a friend = both get discount | |
| 38 | |
| 39 | ### Who is this NOT for? |
| 40 | |
| 41 | **Vietnam-only referral** -> Use `18-referral-program` (VN skill) — Zalo / Messenger optimized |
| 42 | **B2B enterprise sales** -> ABM / partnership programs are different motion (not covered here) |
| 43 | **Brand ambassador / affiliate** -> Use `27-personal-brand-monetize-global` for influencer-affiliate (when available) |
| 44 | |
| 45 | ### 30-second pre-read |
| 46 | |
| 47 | 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). |
| 48 | |
| 49 | ### 3 common errors |
| 50 | |
| 51 | **SMS-based referral in US without TCPA consent** -> Up to USD 1,500 per text fines + class actions |
| 52 | **Email-blast referred contacts in EU** -> GDPR violation; referral programs touching EU prospects need explicit consent from the prospect, NOT just the referrer |
| 53 | **Cash incentives that violate FTC endorsement rules** -> "Refer a friend, get USD 100" requires disclosed material connection if referrer posts publicly |
| 54 | |
| 55 | |
| 56 | |
| 57 | ## Why do you need this skill? |
| 58 | |
| 59 | Without proper referral design: |
| 60 | US: Risk TCPA class action (USD 500-1,500 per message) |
| 61 | EU: GDPR violation if you store referred-prospect data without their consent |
| 62 | SEA: PDPA Singapore strict — most referral programs need both-side consent |
| 63 | LATAM: Brazil LGPD treats referred contacts as data subjects requiring consent |
| 64 | Universal: incentive math wrong -> losing money instead of growing |
| 65 | Universal: no anti-fraud -> 30-50% of "referrals" are self-referrals or bots |
| 66 | |
| 67 | Plan the legal foundation correctly, get the incentive math right, ship a working viral loop. |
| 68 | |
| 69 | |
| 70 | |
| 71 | ## Workflow |
| 72 | |
| 73 | |
| 74 | Step 0: Check global context file |
| 75 | |-- exists -> read product / customer / region |
| 76 | |-- missing -> suggest user run product-marketing-context-global first |
| 77 | Step 1: Pick region variant (US / EU / SEA / LATAM) |
| 78 | Step 2: Confirm prerequisites (NPS, AOV, LTV, customer base) |
| 79 | Step 3: Choose model (1-way / 2-way / multi-tier affiliate) |
| 80 | Step 4: Calculate incentive (15-25% of LTV) |
| 81 | Step 5: Set up tracking + anti-fraud |
| 82 | Step 6: Design referral flow (7 steps) |
| 83 | Step 7: Launch sequence (30-day plan) |
| 84 | Step 8: Measure K-factor / viral coefficient |
| 85 | |
| 86 | |
| 87 | |
| 88 | |
| 89 | ## Step 0: Check global context |
| 90 | |
| 91 | Check `.agents/product-marketing-context-global.md`: |
| 92 | **Yes** -> Read product, customer, region. Do NOT re-ask. |
| 93 | **No** -> Suggest running `product-marketing-context-global` first. |
| 94 | |
| 95 | |
| 96 | |
| 97 | ## Step 1: Pick region variant |
| 98 | |
| 99 | Ask: **"Which is your PRIMARY region: US, EU, SEA, or LATAM?"** |
| 100 | |
| 101 | |
| 102 | Where do most of your customers (and their referrals) live? |
| 103 | |-- US / Canada --> 01-us.md (TCPA SMS rules; CAN-SPAM email; CCPA data) |
| 104 | |-- EU / EEA / UK --> 02-eu.md (GDPR consent for ALL channels) |
| 105 | |-- Southeast Asia --> 03-sea.md (PDPA per country; mostly opt-in) |
| 106 | |-- Latin America --> 04-latam.md (LGPD Brazil; LFPDPPP Mexico) |
| 107 | |-- Vietnam only --> Use `18-referral-program` (VN skill) |
| 108 | |
| 109 | |
| 110 | |
| 111 | |
| 112 | ## Step 2: Prerequisites — does referral make sense? |
| 113 | |
| 114 | ### When referral works |
| 115 | |
| 116 | NPS >= 40 (customers actively like you) |
| 117 | Customer has natural reason to share (visible result, social currency, peer-relevant) |
| 118 | AOV high enough to fund meaningful incentive (USD 50+ ideal) |
| 119 | LTV high enough to justify CAC investment |
| 120 | Existing base of 100+ happy customers to seed |
| 121 | |
| 122 | ### When referral does NOT work (skip this skill) |
| 123 | |
| 124 | NPS < 20 (customers don't like you yet — fix retention first) |
| 125 | Sensitive product category (financial advice, intimate health) — referrals feel weird |
| 126 | Very low AOV (< USD 10) — incentive economics don't work |
| 127 | Pre-launch or no customer base — no one to refer |
| 128 | |
| 129 | ### Ask the user |
| 130 | |
| 131 | Product type? (DTC / SaaS / Service / Subscription) |
| 132 | Average AOV and LTV? |
| 133 | Existing happy customer count? |
| 134 | Goal: more new customers, lower CAC, or higher engagement? |
| 135 | |
| 136 | |
| 137 | |
| 138 | ## Step 3: Referral models |
| 139 | |
| 140 | ### Model 1: One-way (referrer gets reward, referee gets nothing) |
| 141 | |
| 142 | **When:** Premium product where referee will buy regardless of incentive |
| 143 | **Examples:** |
| 144 | Tesla referral program (referrer gets credit, new buyer pays full price) |
| 145 | Robinhood (referrer gets free stock; referee just signs up) |
| 146 | |
| 147 | **Pros:** Lower cost |
| 148 | **Cons:** Lower conversion (referee has no extra reason to buy now) |
| 149 | |
| 150 | ### Model 2: Two-way (BOTH referrer and referee get rewards) — DEFAULT CHOICE |
| 151 | |
| 152 | **When:** 80% of cases; psychological "win-win" feels generous to referrer |
| 153 | **Examples:** |
| 154 | Airbnb (both get USD 25-50 credit) |
| 155 | Uber (both get USD 5-15 credit) |
| 156 | Dropbox (both get +500MB) |
| 157 | |
| 158 | **Pros:** Higher conversion; referrer feels good giving "gift" |
| 159 | **Cons:** Higher cost per acquisition |
| 160 | |
| 161 | **Standard 2-way structure:** |
| 162 | |
| 163 | |
| 164 | Referrer gets: Discount / credit / free product / cash / reward |
| 165 | Referee gets: Discount / free trial / bonus on first order |
| 166 | |
| 167 | |
| 168 | ### Model 3: Multi-tier affiliate (% commission on revenue) |
| 169 | |
| 170 | **When:** SaaS, high-ticket courses, premium DTC; want power-users / influencers |
| 171 | **Examples:** |
| 172 | ConvertKit / Kit (30% recurring affiliate) |
| 173 | Shopify (200% of monthly fee per signup) |
| 174 | AWeber, Teachable, Coursera (10-50% per sale) |
| 175 | |
| 176 | **Pros:** Attracts professional affiliates / influencers; scalable |
| 177 | **Cons:** Requires legal disclosures (FTC US), tracking infrastructure (Rewardful, FirstPromoter), tax forms (W-9 in US, equivalents elsewhere) |
| 178 | |
| 179 | **Standard tier structure:** |
| 180 | Tier 1: 10-30% commission on first purchase |
| 181 | Tier 2: 5-15% on recurring (next 90 days or lifetime) |
| 182 | Top tier: 30-50% for super-affiliates (negotiated) |
| 183 | |
| 184 | |
| 185 | |
| 186 | ## Step 4: Incentive math (CRITICAL) |
| 187 | |
| 188 | ### The formula |
| 189 | |
| 190 | |
| 191 | Total incentive (both sides combined) <= 15-25% of customer LTV |
| 192 | |
| 193 | |
| 194 | ### Worked example (Saas) |
| 195 | |
| 196 | |
| 197 | Product: Project management SaaS |
| 198 | Pricing: USD 49/month |
| 199 | Average tenure: 18 months |
| 200 | LTV: USD 882 (49 x 18) |
| 201 | |
| 202 | Incentive cap: 15-25% of LTV = USD 130-220 total |
| 203 | |
| 204 | Two-way structure: |
| 205 | Referrer: 1 month free (USD 49 value) + USD 30 credit = USD 79 cost |
| 206 | Referee: 50% off first 2 months = USD 49 cost |
| 207 | Total: USD 128 (within cap) |
| 208 | |
| 209 | Or simpler: |
| 210 | Both get 1 month free = USD 98 total cost |
| 211 | ROI: USD 882 LTV - USD 98 incentive = USD 784 net per successful referral |
| 212 | |
| 213 | |
| 214 | ### Worked example (DTC) |
| 215 | |
| 216 | |
| 217 | Product: Skincare subscription |
| 218 | AOV: USD 50 / box |
| 219 | Average orders: 10 |
| 220 | LTV: USD 500 |
| 221 | |
| 222 | Incentive cap: USD 75-125 total |
| 223 | |
| 224 | Two-way structure: |
| 225 | Referrer: USD 30 credit (next box) |
| 226 | Referee: USD 20 off first box |
| 227 | Total: USD 50 (well within cap) |
| 228 | |
| 229 | |
| 230 | ### Reward formats — pros and cons |
| 231 | |
| 232 | | Format | Pros | Cons | Best for | |
| 233 | |--------|------|------|---------| |
| 234 | | Cash | Highest motivation | Highest cost (out of pocket) | Affiliate, B2B | |
| 235 | | Account credit | Keeps customer | Useless if customer leaves | Subscription, marketplace | |
| 236 | | Discount on next purchase | Pay-on-purchase | Customer may not return | E-commerce | |
| 237 | | Free product / service | Higher perceived value | Logistics complexity | Service, beauty, F&B | |
| 238 | | Physical gift | Tangible delight | Operational burden | Premium DTC | |
| 239 | | Points / rewards | Habit-forming | Requires loyalty system | Retailers, airlines | |
| 240 | |
| 241 | |
| 242 | |
| 243 | ## Step 5: Tracking + anti-fraud |
| 244 | |
| 245 | ### Tracking tools (region-agnostic) |
| 246 | |
| 247 | | Tool | Best for | Pricing | |
| 248 | |------|---------|--------| |
| 249 | | **ReferralCandy** | Shopify DTC | USD 49+/mo | |
| 250 | | **Rewardful** | SaaS affiliate | USD 49+/mo | |
| 251 | | **FirstPromoter** | SaaS affiliate | USD 49+/mo | |
| 252 | | **Friendbuy** | Mid-market DTC | USD 249+/mo | |
| 253 | | **Mention Me** | Premium DTC | Enterprise | |
| 254 | | **PartnerStack** | B2B SaaS partnerships | USD 500+/mo | |
| 255 | | **Talkable** | Enterprise DTC | Enterprise | |
| 256 | | Build in-house | Full control | Custom | |
| 257 | |
| 258 | ### Anti-fraud measures |
| 259 | |
| 260 | | Risk | Defense | |
| 261 | |------|---------| |
| 262 | | Self-referral via second account | Match phone, address, payment, IP, device fingerprint | |
| 263 | | Public posting on coupon sites | Limit 3-5 redemptions per code; require minimum AOV | |
| 264 | | Bot / script signups | Captcha; rate limit; manual review for large batches | |
| 265 | | Cancel-after-reward | 30-day reward holding period (after return window) | |
| 266 | | Influencer abuse | Cap individual referrer rewards monthly; flag outliers | |
| 267 | | Referee buys then refunds | Hold rewards until past return window; partial reward if partial refund | |
| 268 | |
| 269 | |
| 270 | |
| 271 | ## Step 6: 7-step referral flow |
| 272 | |
| 273 | |
| 274 | Step 1: Customer has good experience |
| 275 | |--> Trigger when NPS >= 7 OR after 2nd purchase OR completion of service |
| 276 | |
| 277 | Step 2: Customer sees referral CTA |
| 278 | |--> Email after delivery, dashboard widget, post-purchase page, account menu |
| 279 | |
| 280 | Step 3: Customer gets unique code/link |
| 281 | |--> Personalized: "JANE25" or unique link with UTM tracking |
| 282 | |
| 283 | Step 4: Customer shares (multiple channels) |
| 284 | |--> Built-in share: WhatsApp, Email, SMS, Copy link, Twitter/X |
| 285 | |--> Pre-filled message in customer's voice |
| 286 | |
| 287 | Step 5: Friend clicks / enters code |
| 288 | |--> Landing page tailored to referral (not generic homepage) |
| 289 | |--> Reward visible upfront ("Get USD 20 off") |
| 290 | |
| 291 | Step 6: Friend converts (purchase) |
| 292 | |--> Tracking pixel fires; both parties receive notification |
| 293 | |--> Reward delivered automatically (or held for 30 days) |
| 294 | |
| 295 | Step 7: Cycle continues |
| 296 | |--> Friend now eligible to refer; nudge after first delivery |
| 297 | |--> Top referrers get bonus tiers ("3 referrals = VIP") |
| 298 | |
| 299 | |
| 300 | |
| 301 | |
| 302 | ## Step 7: 30-day launch sequence |
| 303 | |
| 304 | ### Week 1: Setup |
| 305 | |
| 306 | Choose model (1-way / 2-way / multi-tier) |
| 307 | Finalize incentive math (LTV calculation, reward structure) |
| 308 | Pick tool (ReferralCandy / Rewardful / build) |
| 309 | Create landing page for referee |
| 310 | Set up email/SMS automation flows |
| 311 | Set up tracking + attribution |
| 312 | Legal review (per region variant) |
| 313 | |
| 314 | ### Week 2: Soft launch (seed) |
| 315 | |
| 316 | Email top 50-100 happiest customers (NPS 9-10) |
| 317 | Track first referrals; fix bugs |
| 318 | Iterate on copy / friction points |
| 319 | Verify reward delivery automation |
| 320 | |
| 321 | ### Week 3: Public launch |
| 322 | |
| 323 | Email full customer base |
| 324 | Add referral CTA to: |
| 325 | Order confirmation page |
| 326 | Post-delivery email |
| 327 | Account dashboard |
| 328 | Receipt PDF / packaging insert (offline) |
| 329 | Social posts on owned channels |
| 330 | Optional: paid promotion to existing customers ("Tell friends, both save") |
| 331 | |
| 332 | ### Week 4: Optimize |
| 333 | |
| 334 | Identify top sharers (top 10%) |
| 335 | Bonus push: "You're in top 10 — extra reward this month" |
| 336 | A/B test: |
| 337 | Landing page (referral vs. cold) |
| 338 | Reward amount (USD 20 vs USD 30) |
| 339 | Channel emphasis (email vs. SMS vs. WhatsApp) |
| 340 | |
| 341 | |
| 342 | |
| 343 | ## Step 8: KPIs and viral coefficient |
| 344 | |
| 345 | ### Key metrics |
| 346 | |
| 347 | | Metric | Formula | Benchmark | |
| 348 | |--------|---------|-----------| |
| 349 | | Share rate | Referrers / total customers | 10% basic, 20% good, 30%+ excellent | |
| 350 | | Conversion rate | Successful redemptions / shares | 15% basic, 25% good, 40%+ excellent | |
| 351 | | Average referrals per sharer | Successful refs / sharers | 1.2 basic, 2+ good, 3+ excellent | |
| 352 | | K-factor (viral coefficient) | Share rate x Conversion rate x Avg refs | 0.3-0.5 typical, 1.0+ true viral | |
| 353 | | CAC via referral | Reward cost / referred customers | 30-50% of paid CAC | |
| 354 | | Referred customer LTV | Avg LTV of referred customers | Often 1.2x non-referred | |
| 355 | |
| 356 | ### K-factor interpretation |
| 357 | |
| 358 | |
| 359 | K = 0.3 -> 100 customers bring 30 new -> sub-viral, supplements other channels |
| 360 | K = 0.7 -> 100 customers bring 70 new -> strong supplement |
| 361 | K = 1.0 -> 100 customers bring 100 new -> equilibrium (each customer replaces one) |
| 362 | K > 1.0 -> Viral loop! Exponential growth (rare but transformative) |
| 363 | |
| 364 | |
| 365 | Most healthy referral programs target K = 0.4-0.7. K > 1 is rare and usually requires unique product mechanics (Dropbox, WhatsApp, Calendly). |
| 366 | |
| 367 | |
| 368 | |
| 369 | ## Output template |
| 370 | |
| 371 | |
| 372 | # Referral Program - [Brand] |
| 373 | Region: [US/EU/SEA/LATAM] |
| 374 | Date: [YYYY-MM-DD] |
| 375 | |
| 376 | ## 1. Goal |
| 377 | [New customers / Lower CAC / Higher LTV / Multiple] |
| 378 | |
| 379 | ## 2. Prerequisites confirmed |
| 380 | - NPS: [X] |
| 381 | - AOV: [USD/EUR/etc.] |
| 382 | - LTV: [calculated] |
| 383 | - Customer base: [N] |
| 384 | |
| 385 | ## 3. Model |
| 386 | [1-way / 2-way / multi-tier] |
| 387 | |
| 388 | ## 4. Incentive structure |
| 389 | - Referrer gets: [reward + cost] |
| 390 | - Referee gets: [reward + cost] |
| 391 | - Total cost: [USD X, ~Y% of LTV] |
| 392 | |
| 393 | ## 5. Tracking tool |
| 394 | [ReferralCandy / Rewardful / etc.] |
| 395 | |
| 396 | ## 6. Anti-fraud measures |
| 397 | [List all 5-7 measures applied] |
| 398 | |
| 399 | ## 7. Referral flow (7 steps) |
| 400 | [Description per step] |
| 401 | |
| 402 | ## 8. Launch sequence (30 days) |
| 403 | [Week 1-4 plan] |
| 404 | |
| 405 | ## 9. KPIs |
| 406 | [Share rate, Conversion rate, K-factor target] |
| 407 | |
| 408 | ## 10. Legal compliance |
| 409 | [Per region variant — see specific variant file] |
| 410 | |
| 411 | |
| 412 | |
| 413 | |
| 414 | ## Quality checklist |
| 415 | |
| 416 | [ ] Region variant chosen (US/EU/SEA/LATAM) |
| 417 | [ ] NPS >= 40 confirmed (have happy customers) |
| 418 | [ ] Total incentive cost <= 25% of LTV |
| 419 | [ ] 2-way model unless strong reason for 1-way |
| 420 | [ ] Tracking tool integrated and tested |
| 421 | [ ] Anti-fraud measures live (5+) |
| 422 | [ ] Reward delivery automated within 24h |
| 423 | [ ] Legal compliance per region (TCPA / GDPR / PDPA / LGPD) |
| 424 | [ ] Landing page for referees built |
| 425 | [ ] K-factor target documented; measure at 30 / 60 / 90 days |
| 426 | |
| 427 | |
| 428 | |
| 429 | ## Related skills |
| 430 | |
| 431 | `product-marketing-context-global` — foundation |
| 432 | `14-email-marketing-global` — email-driven referral mechanics |
| 433 | `27-personal-brand-monetize-global` — affiliate / creator program (when available) |
| 434 | `references/global-legal-compliance` — deep legal reference |
| 435 | |
| 436 | |
| 437 | |
| 438 | *Global Skill 18 (Referral Program) | Over Powers Agency | v1.0.0* |
| 439 |
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