# 18 Referral Program Global > Referral program design for global businesses — 1-way vs 2-way, incentive structure, anti-fraud, attribution. Has 4 region variants for anti-spam compliance (TCPA US, GDPR EU, PDPA SEA, LGPD LATAM). Trigger: 'referral program', 'refer a friend', 'word of mouth', 'viral loop', 'referral marketing'. - **Type:** Skill - **Install:** `agentstack add skill-minhnv0807-ai-business-skills-18-referral-program-global` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [minhnv0807](https://agentstack.voostack.com/s/minhnv0807) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [minhnv0807](https://github.com/minhnv0807) - **Source:** https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-global - **Website:** https://opa.business ## Install ```sh agentstack add skill-minhnv0807-ai-business-skills-18-referral-program-global ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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 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* ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [minhnv0807](https://github.com/minhnv0807) - **Source:** [minhnv0807/ai-business-skills](https://github.com/minhnv0807/ai-business-skills) - **License:** MIT - **Homepage:** https://opa.business Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-minhnv0807-ai-business-skills-18-referral-program-global - Seller: https://agentstack.voostack.com/s/minhnv0807 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.