--- 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. --- ## 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 --- ## 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 | 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 ```markdown # 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*