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a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a...\n\nTags: latest:1.1.0\n\nVersion history:\n\nv1.1.0 | 2026-06-05T14:42:25.064Z | user\n\n- Major upgrade: Expanded documentation and guidance for launching effective customer referral programs.\n- Added four detailed resources: incentive-calculator.md, fraud-prevention-rules.md, output-template.md, and quality-checklist.md to support reward design, fraud safeguards, and implementation quality.\n- Removed outdated skill-card.md for streamlined documentation.\n- SKILL.md now includes quick reference tables, step-by-step workflow, stronger benchmarks, and actionable best practices.\n- Provides more examples, practical launch tips, and expanded fraud prevention strategies for users.\n\nv1.0.0 | 2026-04-20T01:29:18.439Z | user\n\nInitial release.\n\nArchive index:\n\nArchive v1.1.0: 7 files, 12564 bytes\n\nFiles: fraud-prevention-rules.md (4095b), incentive-calculator.md (2898b), output-template.md (1960b), quality-checklist.md (2326b), skill-card.md (2371b), SKILL.md (11164b), _meta.json (134b)\n\nFile v1.1.0:SKILL.md\n\n---\nname: Referral Engine\ndescription: Design a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel.\n---\n\n# Referral Engine\n\nDesign a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel. Referral is consistently the highest-converting acquisition source for ecommerce — referred customers convert at 3–5× the rate of cold traffic and have 16–25% higher LTV — but most referral programs fail because the incentive is wrong, the timing is off, or the mechanics are too complex for buyers to act on.\n\n## Quick Reference\n\n| Decision | Strong | Acceptable | Weak |\n|---|---|---|---|\n| Incentive type | Double-sided reward (both referrer and referee get value) | One-sided reward for referrer only | Discount-only incentive with no novelty |\n| Trigger timing | First positive experience moment (post-delivery Day 7–14) | Post-purchase confirmation page | Sent only to all customers at once via blast email |\n| Reward value | 10–20% of AOV or product credit with real perceived value | Flat $5–$10 credit | $1–$2 credit that feels insulting |\n| Sharing mechanics | 1-click share with pre-filled message to WhatsApp, SMS, email | Copy-paste link only | Manual \"tell a friend\" with no tracking |\n| Fraud prevention | Email domain checks, IP/device deduplication, minimum order before payout | Basic duplicate email check | No fraud protection |\n| Program measurement | Track referral CAC vs. other channels; CLV of referred cohort | Track total referrals sent | Count referral codes shared only |\n| Program visibility | Persistent account page link + post-purchase flow + triggered email | Only in one email | Hidden in footer |\n\n## Solves\n\n- High customer acquisition cost from paid channels with no organic growth loop\n- Strong product-market fit but weak word-of-mouth spread\n- Loyal customers who would refer but have no easy mechanism to do so\n- New store or brand with low ad budget needing cost-efficient first customers\n- Existing customers who don't re-engage after their first purchase\n- Discount dependency cycle — needing to offer promos to drive repeat business\n- No measurable advocacy metric tied to customer satisfaction\n\n## Workflow\n\n### Step 1 — Define Program Economics\n\nBefore designing the referral experience, validate the economics work for your margins.\n\n**Unit economics check:**\n\n| Metric | Your number | Target range |\n|---|---|---|\n| Average Order Value (AOV) | | |\n| Gross margin % | | |\n| Current CAC (paid channels) | | |\n| Target referral CAC | | <50% of paid CAC |\n| Maximum reward budget | | <25% of gross margin on referred order |\n\n**Reward type options by margin profile:**\n\n| Margin | Best reward type | Why |\n|---|---|---|\n| >50% GM | Product credit or free item | High perceived value, low real cost |\n| 30–50% GM | Discount code (15–20% off) | Sustainable; still feels meaningful |\n| <30% GM | Cash reward on second order | Defer cost to proven repeat buyer |\n| Any | Tiered rewards (more referrals = better reward) | Gamification without upfront cost |\n\n**Double-sided reward benchmark:**\n- Referrer gets: $15–20 credit or 15% off next order\n- Referee gets: 10–15% off their first order\n- Both rewards activate only when the referred order ships (not at sign-up)\n\n### Step 2 — Choose Program Structure\n\n**Standard referral (recommended for most stores):**\nEvery customer gets a unique referral link after purchase. Referrer rewards activate on friend's first order.\n\n**Loyalty-gated referral:**\nReferral program unlocked after Nth purchase or reaching a spend threshold. Keeps program exclusive and rewards your best customers.\n\n**Influencer / ambassador tier:**\nSeparate track for customers with large networks. Higher reward rates (20–30%) in exchange for content creation or social posts. Requires manual vetting.\n\n**Group referral / squad mechanic:**\nReferrer gets progressive rewards for multiple friends referred (1 friend = $10, 3 friends = $40, 5 friends = $100). Drives high-effort sharing from motivated advocates.\n\n### Step 3 — Design the Sharing Experience\n\nThe referral share moment must be frictionless. Each additional step cuts conversion by ~40%.\n\n**Required elements:**\n1. Unique shareable link (auto-generated per customer)\n2. Pre-written share message (editable, but pre-filled — never blank)\n3. One-click share buttons: WhatsApp (highest conversion), SMS, Email, Copy link\n4. Visual referral card with the offer clearly stated\n\n**Pre-written message template:**\n> \"Hey! I've been buying from [Brand] and genuinely love [product/brand]. Here's 15% off your first order: [link]. I get a credit too when you order — thought I'd share!\"\n\nPersonal tone, names the brand benefit, explains the mechanic briefly.\n\n**Where to surface the share moment:**\n- Order confirmation page (highest intent moment)\n- Day 7–14 post-delivery email (\"How's your order?\")\n- My Account → Referrals page (persistent, always accessible)\n- Reorder email for consumables\n\n### Step 4 — Set Up Tracking and Attribution\n\n**Minimum viable tracking setup:**\n\n| Tool tier | Option | Tracks |\n|---|---|---|\n| Built-in (Shopify) | Shopify Referrals or ReferralCandy | Basic referral links, discount attribution |\n| Mid-tier | Yotpo Loyalty, Smile.io, Friendbuy | Full referral + loyalty, email flows |\n| Enterprise | Impact.com, PartnerStack | Multi-channel affiliate + referral |\n\n**UTM parameters for custom implementations:**\n`?utm_source=referral&utm_medium=friend&utm_campaign=referral-program&utm_content=[customer_id]`\n\n**Metrics to track from Day 1:**\n- Referral share rate: % of eligible customers who share a link\n- Referral conversion rate: referred visits → first order\n- Referral CAC: total reward cost ÷ referred new customers\n- Referred customer CLV: compare to non-referred cohort at 6 months\n\n### Step 5 — Build Fraud Prevention\n\nReferral fraud is common. Implement at minimum:\n\n**Basic controls (must-have):**\n- Reward activates only on completed, shipped order (never on sign-up)\n- Self-referral prevention: same email domain as referrer = flagged\n- IP address deduplication: multiple orders from same IP in same session = flagged\n- Minimum order threshold before reward activates ($25–$50)\n\n**Intermediate controls:**\n- Delay reward payout by return window (e.g., 30 days after delivery before credit issued)\n- Device fingerprinting to catch same-device referral loops\n- Email domain block list (temporary email services: mailinator, guerrilla mail, etc.)\n- Manual review queue for orders that trigger 2+ fraud signals\n\n**Signs of fraud to watch:**\n- Same IP generating 5+ referrals in one day\n- Referral codes used by email addresses sharing domain patterns\n- Referred customers who never return after redeeming referral discount\n\n### Step 6 — Launch and Promote\n\n**Launch sequence:**\n1. Soft launch to your top 200 customers (high LTV, repeat buyers) — test mechanics, confirm reward delivery\n2. Full launch to entire customer base via email campaign\n3. Add referral CTA to post-purchase email series (Day 7 trigger)\n4. Add referral to My Account navigation permanently\n\n**Announcement email subject lines (A/B test):**\n- \"Give $15, get $15 — share [Brand] with a friend\"\n- \"You've been asking how to share [Brand]. Here's how.\"\n- \"[First name], your friends get 15% off. Here's why.\"\n\n### Step 7 — Optimize and Scale\n\n**Monthly review:**\n- Share rate below 5%? The incentive is too small or the sharing UX has too much friction\n- Conversion rate below 20%? The referee offer isn't compelling enough; test higher discount\n- Fraud rate above 10%? Tighten controls in Step 5\n\n**Growth levers:**\n- Seasonal multipliers: 2× rewards during holiday or brand anniversary\n- Category-specific programs: higher rewards for premium products with high social currency\n- Ambassador upgrade path: top referrers (5+ conversions) get invited to ambassador program with better economics\n\n## Examples\n\n### Example 1 — Coffee Subscription Brand (Shopify + Klaviyo)\n\n**Setup:**\n- AOV: $38; GM: 62%; Current paid CAC: $41\n- Target referral CAC: <$20\n- Reward: Referrer gets $15 store credit; referee gets 20% off first order\n- Trigger: Day 10 post-delivery email (\"How's your first bag?\") with Smile.io link embedded\n\n**Share message:**\n> \"Honestly one of the best coffees I've tried. Use my link for 20% off your first bag: [link]\"\n\n**90-day results:**\n- 847 shares sent\n- 12.4% referral conversion rate → 105 new customers\n- Referral CAC: $14.29 (vs. $41 paid CAC — 65% cheaper)\n- Referred customer 6-month retention: 54% vs. 38% non-referred\n\n---\n\n### Example 2 — Skincare Brand (WooCommerce + ReferralHero)\n\n**Setup:**\n- AOV: $62; GM: 55%; no prior referral program\n- Reward structure: Double-sided — referrer gets free travel-size product ($14 value); referee gets 15% off\n- Fraud control: 30-day payout delay; self-referral email domain check; $40 minimum order\n\n**Insight:** Product credit (free travel size) outperformed $10 cash credit in A/B test by 34% on share rate because recipients perceived it as a gift, not a transaction.\n\n**Result:** 6.8% of customers shared within 30 days; 22% referee conversion rate; referral program accounted for 18% of new customer acquisition by Month 3.\n\n## Common Mistakes\n\n1. **One-sided incentive only** — If only the referrer benefits, the share feels selfish. Double-sided rewards outperform single-sided by 30–50% on conversion.\n\n2. **Launching before product-market fit** — Referral amplifies your existing word-of-mouth signal. If customers aren't naturally recommending you, a referral program won't create that impulse.\n\n3. **Too-small incentive** — A $2 credit isn't motivating. The referrer is doing you a favor; the reward should feel meaningful. Match 15–20% of AOV as a rule of thumb.\n\n4. **No fraud prevention** — Without basic controls, self-referral loops and bulk fake account creation can drain your reward budget quickly.\n\n5. **Burying the program** — If the only access point is a single email sent at sign-up, most customers will never remember or find the program again.\n\n6. **Complicated reward mechanics** — If explaining how to earn rewards takes more than two sentences, customers won't participate. Simplicity converts.\n\n7. **No post-share nurture** — Referred visitors who don't convert on first visit need a follow-up sequence. Capture email at minimum; retarget if budget allows.\n\n8. **Treating all customers equally** — Your top 10% of customers by LTV are 5–10× more likely to refer effectively. Target them first with higher incentives.\n\n## Resources\n\n- [Output Template](references/output-template.md) — Referral program design brief\n- [Incentive Calculator](references/incentive-calculator.md) — Unit economics worksheet\n- [Fraud Prevention Rules](references/fraud-prevention-rules.md) — Controls checklist and detection patterns\n- [Quality Checklist](assets/quality-checklist.md) — Pre-launch review checklist\n\nFile v1.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn70fv0ehp50emedet9tx3fekd82pw3b\",\n  \"slug\": \"referral-engine\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1780670545064\n}\n\nFile v1.1.0:fraud-prevention-rules.md\n\n# Referral Fraud Prevention Rules\n\n## Fraud Risk by Program Type\n\n| Program type | Fraud risk | Primary attack vectors |\n|---|---|---|\n| Open referral (any customer) | Medium | Self-referral, fake account creation |\n| Post-purchase gated | Low-Medium | Family/household self-referral |\n| High-value rewards ($50+) | High | Organized fraud rings, fake account farms |\n| Free product reward | High | Bulk account creation to claim free items |\n\n## Tier 1 Controls — Implement for Every Program\n\n### 1. Reward on shipped order, never on sign-up or add-to-cart\n- Fraud attack: Create fake account → use referral code → claim reward without buying\n- Control: Reward credit is issued only after referred order ships and return window opens\n\n### 2. Self-referral email domain detection\n- Fraud attack: Customer uses their own code with a second email address\n- Control: If referrer email domain matches referee email domain → flag for review\n- Also flag: obvious pattern variations (john@gmail.com refers johnny.smith@gmail.com)\n\n### 3. Minimum order value threshold\n- Set minimum: $25–$50 before referred order qualifies\n- Prevents: Creating an account, ordering the cheapest item to unlock a $15 credit\n\n### 4. Duplicate IP address detection\n- If 3+ referral code uses originate from the same IP in 24 hours → auto-flag\n- Legitimate use case: family member also orders (allow 2 per IP; flag at 3+)\n\n## Tier 2 Controls — Add for High-Value Programs\n\n### 5. Payout delay = return window\n- If your return policy is 30 days, don't issue reward until Day 31\n- Prevents: Order → claim referral discount → immediately return the order\n\n### 6. Device fingerprinting\n- Most referral platforms (Friendbuy, Impact.com) offer this as a paid feature\n- Detects same physical device using multiple accounts\n- Useful for free product programs where creating 10 fake accounts is worth effort\n\n### 7. Temporary email domain blocklist\nCommon disposable email domains to block from receiving referee rewards:\n- mailinator.com, guerrillamail.com, tempmail.com, throwam.com, yopmail.com, dispostable.com\n\n### 8. Phone number verification for high-value rewards\n- Require SMS verification before reward activates\n- One phone number = one referee account\n- Significantly reduces fake account creation\n\n## Tier 3 Controls — For Enterprise / High-Fraud-Risk Programs\n\n### 9. Manual review queue\n- Auto-flag any order that triggers 2+ fraud signals\n- Human review before reward issuance\n- Target: review within 24 hours\n\n### 10. Velocity rules\n- Max referral rewards per customer per month: 5–10 (above this = abnormal)\n- If referrer earns 5+ rewards in 30 days → pause account pending review\n- Legitimate super-referrers are rare; high volume almost always indicates fraud\n\n## Red Flags Requiring Investigation\n\n| Signal | What it likely means |\n|---|---|\n| 10+ referral uses from same IP in one day | Fraud ring or organized abuse |\n| Referee email addresses follow a pattern (user1@, user2@, user3@) | Bulk fake account creation |\n| Referrer redeems reward immediately after referee order ships | Possible family/self-referral collusion |\n| High referral code usage rate but low referee repeat purchases | Discount hunters, not real customers |\n| Multiple referral accounts with same shipping address | Same household; review for self-referral |\n\n## Acceptable Fraud Rate\n\nIndustry benchmark: 3–8% of referral transactions contain some fraud signal.\n\n- Below 3%: Controls may be too aggressive — check if legitimate referrals are being blocked\n- 8–15%: Tighten Tier 1 controls; add Tier 2\n- Above 15%: Program mechanics are exploitable; consider structural redesign\n\n## Fraud Investigation Workflow\n\n1. Flag triggered → automatic reward hold (don't cancel — investigate first)\n2. Customer service review within 48 hours\n3. If legitimate: release reward and whitelist account\n4. If confirmed fraud: cancel reward, flag account, review all associated accounts\n5. If unclear: request order verification (ID match, phone verification)\n6. Document pattern and update blocklist/rules accordingly\n\nFile v1.1.0:incentive-calculator.md\n\n# Referral Incentive Calculator\n\n## Unit Economics Worksheet\n\n| Input | Your value |\n|---|---|\n| Average Order Value (AOV) | $ |\n| Gross Margin % | % |\n| Gross Margin per order | $ (AOV × GM%) |\n| Current CAC (paid channels) | $ |\n| Target referral CAC | $ (aim for 40–60% of paid CAC) |\n| Maximum reward budget per acquisition | $ (GM per order − target referral CAC) |\n\n## Reward Value by Margin Tier\n\n### High-margin brands (>55% GM)\n- Referrer: $15–20 store credit or free product ($12–18 cost)\n- Referee: 15–20% off first order\n- Total reward cost per acquisition: ~$20–35\n- Viable if: AOV > $60 and referral CAC < $35\n\n### Mid-margin brands (35–55% GM)\n- Referrer: $10–15 store credit or 15% off next order\n- Referee: 10–15% off first order\n- Total reward cost per acquisition: ~$15–25\n- Viable if: AOV > $45 and referral CAC < $25\n\n### Lower-margin brands (<35% GM)\n- Referrer: 10% off next order (cost deferred to second purchase)\n- Referee: 10% off first order\n- Alternative: Unlock reward only after referred customer makes second purchase\n- Note: Straightforward cash rewards may not be viable at this margin level\n\n## Reward Type Comparison\n\n| Reward type | Perceived value | Actual cost | Share rate impact | Best for |\n|---|---|---|---|---|\n| Free product (travel/sample size) | High | Low (COGS) | +30–40% vs. cash | Brands with low-COGS samples |\n| Store credit | High | Medium (discounts future revenue) | Neutral | Subscription or repeat-purchase brands |\n| % discount off next order | Medium | Medium | Neutral | Mid-margin brands |\n| Flat $ cash discount | Medium | Medium | Baseline | Any brand |\n| Exclusive early access | High | Very low | +15–25% | Brands with strong product pipeline |\n| Charity donation on their behalf | Low-medium | Low | Below baseline | Only for brand-values-driven audiences |\n\n## Double-Sided vs. Single-Sided\n\nResearch consistently shows double-sided referral programs (both referrer and referee get rewards) outperform single-sided by 30–50% in:\n- Share rate (referrer more willing to share when friend also benefits)\n- Referee conversion rate (new customer more likely to act on offer)\n- Referral program NPS (customers view program as generous, not transactional)\n\n**Minimum viable double-sided offer:**\n- Referrer: 10% off next order or $10 credit\n- Referee: 10% off first order\n- Combined cost at 40% GM on $50 AOV: ~$10 per acquisition (vs. paid CAC of $35+)\n\n## Break-Even Referral Rate Calculator\n\nHow many referrals must convert to justify program costs?\n\n| Fixed program cost (software) | $99/month (e.g., Smile.io Pro) |\n|---|---|\n| Variable cost per referred acquisition | $18 average reward cost |\n| Break-even referrals to cover software | 99 ÷ 18 = 6 referral conversions to cover tool cost |\n\nAt 6+ referral acquisitions per month, even basic referral tools pay for themselves before any CAC comparison.\n\nFile v1.1.0:output-template.md\n\n# Referral Program Design Brief\n\n## Brand Overview\n- **Brand name:** \n- **AOV:** $\n- **Gross margin %:** %\n- **Current paid CAC:** $\n- **Target referral CAC:** $\n\n## Program Economics\n- **Referrer reward:** \n- **Referee reward:** \n- **Minimum order to activate reward:** $\n- **Reward payout delay:** ___ days after delivery\n- **Estimated reward cost per acquisition:** $\n\n## Program Structure\n- **Type:** Standard / Loyalty-gated / Ambassador / Group mechanic\n- **Eligibility:** All customers / Repeat buyers / Loyalty tier members\n\n## Share Experience\n- **Referral link format:** \n- **Pre-written share message:**\n\n> [Draft message here — 2–3 sentences, first person, names the offer]\n\n- **Share channels enabled:** WhatsApp / SMS / Email / Copy link / Facebook / Instagram\n- **Visual referral card:** Yes / No (design needed)\n\n## Integration\n- **Platform:** Shopify / WooCommerce / Custom\n- **Referral tool:** Smile.io / ReferralCandy / Yotpo / Friendbuy / Custom\n- **Email trigger:** Post-purchase Day ___ / Order confirmation / My Account\n\n## Fraud Prevention\n- [ ] Reward activates on shipped order only\n- [ ] Self-referral email domain check enabled\n- [ ] IP deduplication enabled\n- [ ] Minimum order threshold: $___\n- [ ] Payout delay: ___ days\n- [ ] Device fingerprinting: Yes / No\n- [ ] Temp email domain blocklist: Yes / No\n\n## Sharing Touchpoints\n- [ ] Order confirmation page\n- [ ] Day 7–14 post-delivery email\n- [ ] My Account → Referrals page (persistent)\n- [ ] Packaging insert / unboxing card\n\n## Launch Plan\n- **Soft launch cohort:** ___ top customers (target: 100–200)\n- **Soft launch date:** \n- **Full launch date:** \n- **Announcement email subject A:** \n- **Announcement email subject B:** \n\n## Success Metrics\n| Metric | 30-day target | 90-day target |\n|---|---|---|\n| Share rate | % | % |\n| Referee conversion rate | % | % |\n| New customers via referral | | |\n| Referral CAC | $ | $ |\n| Referral % of new customer mix | % | % |\n\nFile v1.1.0:quality-checklist.md\n\n# Referral Engine Quality Checklist\n\n## Program Economics\n- [ ] AOV and gross margin % confirmed before setting reward values\n- [ ] Reward cost per acquisition calculated and below 25% of GM per order\n- [ ] Target referral CAC is at least 40% lower than current paid CAC\n- [ ] Double-sided reward structure designed (both referrer and referee benefit)\n- [ ] Reward value feels meaningful relative to AOV (not insulting)\n\n## Sharing Experience\n- [ ] Unique referral links generated automatically per customer\n- [ ] Pre-written share message drafted (not left blank for customer to write)\n- [ ] WhatsApp and SMS share buttons enabled (highest conversion channels)\n- [ ] Share message names the specific offer the friend receives\n- [ ] Visual referral card created showing the offer clearly\n- [ ] 1-click sharing from multiple entry points\n\n## Program Visibility\n- [ ] Referral CTA on order confirmation page\n- [ ] Day 7–14 post-delivery email trigger configured\n- [ ] My Account → Referrals page exists and is in navigation\n- [ ] Announcement email drafted for launch\n\n## Technical Setup\n- [ ] Referral tracking tool integrated with store\n- [ ] UTM parameters configured for referral traffic\n- [ ] Reward issuance automated (no manual steps after referred order ships)\n- [ ] Test referral completed end-to-end (link → order → reward confirmation)\n- [ ] Email notification sent to referrer when friend orders\n\n## Fraud Prevention\n- [ ] Reward activates on shipped order only (not sign-up or cart)\n- [ ] Self-referral email domain check enabled\n- [ ] IP deduplication active\n- [ ] Minimum order threshold set ($25–$50)\n- [ ] Payout delay matches return window\n- [ ] Disposable email domain blocklist added\n\n## Measurement\n- [ ] Share rate baseline target set (aim for >5% of eligible customers sharing)\n- [ ] Referee conversion rate target set (aim for >15%)\n- [ ] Referral CAC tracking configured\n- [ ] Referred customer cohort tagged for CLV comparison\n- [ ] Monthly review cadence scheduled\n\n## Launch Readiness\n- [ ] Soft launch to 100–200 top customers completed and mechanics validated\n- [ ] Reward delivery confirmed (credit arrives in account as expected)\n- [ ] Customer support briefed on program mechanics and FAQ\n- [ ] Fraud controls tested (self-referral attempt blocked)\n- [ ] Full launch email scheduled\n\nFile v1.1.0:skill-card.md\n\n## Description:\n\nDesign a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[leooooooow](https://clawhub.ai/user/leooooooow)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal ecommerce operators and growth marketers use this skill to design referral program economics, customer sharing flows, fraud controls, launch plans, and measurement checklists.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Referral tracking, device fingerprinting, SMS or ID verification, reward holds, and account flags can create privacy, disclosure, or customer fairness concerns.\n\nMitigation: Disclose customer tracking and verification practices where required, align them with privacy law and store policy, and use human review for edge cases before denying rewards.\n\nRisk: Fraud-prevention automation can incorrectly flag legitimate customers or households.\n\nMitigation: Use reward holds and manual review queues before cancellation, document review decisions, and whitelist legitimate cases after investigation.\n\n## Reference(s):\n\n- [Referral Engine Skill Source](artifact/SKILL.md)\n- [Referral Program Design Brief](artifact/output-template.md)\n- [Referral Incentive Calculator](artifact/incentive-calculator.md)\n- [Referral Fraud Prevention Rules](artifact/fraud-prevention-rules.md)\n- [Referral Engine Quality Checklist](artifact/quality-checklist.md)\n- [ClawHub Skill Page](https://clawhub.ai/leooooooow/skills/referral-engine)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, configuration, guidance]\n\n**Output Format:** [Markdown guidance, worksheets, checklists, and implementation planning text]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Markdown-only planning skill; no executable code or external tool calls are included in the release artifact.]\n\n## Skill Version(s):\n\n1.1.0 (source: server release evidence, created 2026-06-05)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.0: 3 files, 3425 bytes\n\nFiles: skill-card.md (2053b), SKILL.md (4338b), _meta.json (134b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: referral-engine\ndescription: Design a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel.\n---\n\n# Referral Engine\n\nDesign a complete customer referral program that transforms your existing buyer base into a reliable, low-cost acquisition channel. This skill walks you through building incentive structures, defining sharing mechanics across platforms, setting up fraud prevention guardrails, and establishing tracking infrastructure so every referral dollar is accountable and every new customer is attributed correctly.\n\n## Use when\n\n- You want to launch a refer-a-friend program for your Shopify, TikTok Shop, or Amazon storefront and need a structured plan covering incentives, rules, and tracking\n- A founder or growth manager says \"we need our customers to bring us more customers\" and you need to design the full referral loop from scratch\n- You are evaluating whether to offer cash-back, store credit, percentage discounts, or free products as referral rewards and need a framework to decide\n- Your existing referral program has low participation or high fraud rates and you need to redesign the incentive structure and add abuse prevention rules\n\n## What this skill does\n\nThis skill analyzes your product type, average order value, customer lifetime value, and existing marketing channels to design a referral program tailored to your business. It determines the optimal reward type and amount for both the referrer and the referred friend, maps out the sharing flow across email, SMS, social media, and unique referral links, defines fraud prevention rules such as self-referral blocks, IP deduplication, minimum purchase requirements, and velocity limits, and produces a tracking plan covering attribution windows, conversion events, and reporting dashboards. The output is a ready-to-implement blueprint that balances generosity with profitability.\n\n## Inputs required\n\n- **Product category and AOV** (required): What you sell and the average order value, so reward sizing is proportional to margin — e.g., \"skincare, AOV $45\"\n- **Estimated customer LTV** (required): Rough lifetime value per customer so the skill can set a reward ceiling that keeps CAC below LTV — e.g., \"$120 over 12 months\"\n- **Sales channels** (required): Where you sell (Shopify storefront, TikTok Shop, Amazon, retail) so sharing mechanics and tracking are channel-appropriate\n- **Current referral setup** (optional): Describe any existing program or past attempts so the skill can diagnose issues rather than starting from zero\n- **Tech stack** (optional): Tools you use (Klaviyo, ReferralCandy, Smile.io, custom code) so recommendations are compatible with your infrastructure\n\n## Output format\n\nThe output is a structured referral program blueprint divided into six sections. First, a Program Summary with the reward model, referral flow diagram, and projected economics. Second, an Incentive Design section specifying exact reward types, amounts, and conditions for both referrer and referee, including tiered bonuses for power referrers. Third, a Sharing Mechanics section detailing channel-specific sharing flows for email, SMS, WhatsApp, Instagram, and unique referral links with sample copy for each. Fourth, a Fraud Prevention section listing specific rules — self-referral blocking, IP and device fingerprint checks, minimum purchase thresholds, velocity caps, and manual review triggers. Fifth, a Tracking and Attribution section covering UTM parameters, cookie windows, conversion pixels, and dashboard KPIs. Sixth, a Launch Checklist with phased rollout steps from soft launch to full promotion.\n\n## Scope\n\n- Designed for: ecommerce operators, DTC brand teams, growth managers\n- Platform context: Shopify, TikTok Shop, Amazon, WooCommerce, platform-agnostic\n- Language: English\n\n## Limitations\n\n- Does not integrate directly with referral software APIs — outputs a plan you implement in your chosen tool\n- Reward economics are estimates based on inputs you provide; actual results depend on customer behavior and market conditions\n- Does not provide legal advice on referral program compliance with local regulations; consult a lawyer for sweepstakes or cash-reward legality in your jurisdiction\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn70fv0ehp50emedet9tx3fekd82pw3b\",\n  \"slug\": \"referral-engine\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776648558439\n}\n\nFile v1.0.0:skill-card.md\n\n## Description: <br>\nDesign a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[leooooooow](https://clawhub.ai/user/leooooooow) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nExternal ecommerce operators, DTC brand teams, and growth managers use this skill to design referral programs with incentive models, sharing flows, fraud controls, tracking plans, and a launch checklist. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Referral economics, tracking setup, or fraud-prevention recommendations may be unsuitable for a specific business or platform. <br>\nMitigation: Review the proposed economics, tracking setup, and fraud controls before implementation. <br>\nRisk: Users may include customer PII, secrets, or live platform credentials while asking for referral-program guidance. <br>\nMitigation: Avoid sharing customer PII, secrets, or live platform credentials in prompts. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/leooooooow/referral-engine) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, guidance, configuration] <br>\n**Output Format:** [Markdown referral program blueprint with sections for incentives, sharing mechanics, fraud prevention, tracking, and launch checklist] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Planning-only output; no executable code, install hooks, credential use, or direct API integration.] <br>\n\n## Skill Version(s): <br>\n1.0.0 (source: server release evidence) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>","readmeExcerpt":"Skill: Referral Engine Owner: leooooooow Summary: Design a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a... Tags: latest:1.1.0 Version history: v1.1.0 | 2026-06-05T14:42:25.064Z | user - Major upgrade: Expanded documentation and guidance for launching effective customer referral programs. - Added four detailed resou","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: Referral Engine\ndescription: Design a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel.\n---\n\n# Referral Engine\n\nDesign a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel. Referral is consistently the highest-converting acquisition source for ecommerce — referred customers convert at 3–5× the rate of cold traffic and have 16–25% higher LTV — but most referral programs fail because the incentive is wrong, the timing is off, or the mechanics are too complex for buyers to act on.\n\n## Quick Reference\n\n| Decision | Strong | Acceptable | Weak |\n|---|---|---|---|\n| Incentive type | Double-sided reward (both referrer and referee get value) | One-sided reward for referrer only | Discount-only incentive with no novelty |\n| Trigger timing | First positive experience moment (post-delivery Day 7–14) | Post-purchase confirmation page | Sent only to all customers at once via blast email |\n| Reward value | 10–20% of AOV or product credit with real perceived value | Flat $5–$10 credit | $1–$2 credit that feels insulting |\n| Sharing mechanics | 1-click share with pre-filled message to WhatsApp, SMS, email | Copy-paste link only | Manual \"tell a friend\" with no tracking |\n| Fraud prevention | Email domain checks, IP/device deduplication, minimum order before payout | Basic duplicate email check | No fraud protection |\n| Program measurement | Track referral CAC vs. other channels; CLV of referred cohort | Track total referrals sent | Count referral codes shared only |\n| Program visibility | Persistent account page link + post-purchase flow + triggered email | Only in one email | Hidden in footer |\n\n## Solves\n\n- High customer acquisition cost from paid channels with no organic growth loop\n- Strong product-market fit but weak word-of-mouth spread\n- Loyal customers who would refer but have no easy mechanism to do so\n- New store or brand with low ad budget needing cost-efficient first customers\n- Existing customers who don't re-engage after their first purchase\n- Discount dependency cycle — needing to offer promos to drive repeat business\n- No measurable advocacy metric tied to customer satisfaction\n\n## Workflow\n\n### Step 1 — Define Program Economics\n\nBefore designing the referral experience, validate the economics work for your margins.\n\n**Unit economics check:**\n\n| Metric | Your number | Target range |\n|---|---|---|\n| Average Order Value (AOV) | | |\n| Gross margin % | | |\n| Current CAC (paid channels) | | |\n| Target referral CAC | | <50% of paid CAC |\n| Maximum reward budget | | <25% of gross margin on referred order |\n\n**Reward type options by margin profile:**\n\n| Margin | Best reward type | Why |\n|---|---|---|\n| >50% GM | Product credit or free item | High perceived value, low real cost |\n| 30–50% GM | D"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn70fv0ehp50emedet9tx3fekd82pw3b\",\n  \"slug\": \"referral-engine\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1780670545064\n}"},{"path":"fraud-prevention-rules.md","content":"# Referral Fraud Prevention Rules\n\n## Fraud Risk by Program Type\n\n| Program type | Fraud risk | Primary attack vectors |\n|---|---|---|\n| Open referral (any customer) | Medium | Self-referral, fake account creation |\n| Post-purchase gated | Low-Medium | Family/household self-referral |\n| High-value rewards ($50+) | High | Organized fraud rings, fake account farms |\n| Free product reward | High | Bulk account creation to claim free items |\n\n## Tier 1 Controls — Implement for Every Program\n\n### 1. Reward on shipped order, never on sign-up or add-to-cart\n- Fraud attack: Create fake account → use referral code → claim reward without buying\n- Control: Reward credit is issued only after referred order ships and return window opens\n\n### 2. Self-referral email domain detection\n- Fraud attack: Customer uses their own code with a second email address\n- Control: If referrer email domain matches referee email domain → flag for review\n- Also flag: obvious pattern variations (john@gmail.com refers johnny.smith@gmail.com)\n\n### 3. Minimum order value threshold\n- Set minimum: $25–$50 before referred order qualifies\n- Prevents: Creating an account, ordering the cheapest item to unlock a $15 credit\n\n### 4. Duplicate IP address detection\n- If 3+ referral code uses originate from the same IP in 24 hours → auto-flag\n- Legitimate use case: family member also orders (allow 2 per IP; flag at 3+)\n\n## Tier 2 Controls — Add for High-Value Programs\n\n### 5. Payout delay = return window\n- If your return policy is 30 days, don't issue reward until Day 31\n- Prevents: Order → claim referral discount → immediately return the order\n\n### 6. Device fingerprinting\n- Most referral platforms (Friendbuy, Impact.com) offer this as a paid feature\n- Detects same physical device using multiple accounts\n- Useful for free product programs where creating 10 fake accounts is worth effort\n\n### 7. Temporary email domain blocklist\nCommon disposable email domains to block from receiving referee rewards:\n- mailinator.com, guerrillamail.com, tempmail.com, throwam.com, yopmail.com, dispostable.com\n\n### 8. Phone number verification for high-value rewards\n- Require SMS verification before reward activates\n- One phone number = one referee account\n- Significantly reduces fake account creation\n\n## Tier 3 Controls — For Enterprise / High-Fraud-Risk Programs\n\n### 9. Manual review queue\n- Auto-flag any order that triggers 2+ fraud signals\n- Human review before reward issuance\n- Target: review within 24 hours\n\n### 10. Velocity rules\n- Max referral rewards per customer per month: 5–10 (above this = abnormal)\n- If referrer earns 5+ rewards in 30 days → pause account pending review\n- Legitimate super-referrers are rare; high volume almost always indicates fraud\n\n## Red Flags Requiring Investigation\n\n| Signal | What it likely means |\n|---|---|\n| 10+ referral uses from same IP in one day | Fraud ring or organized abuse |\n| Referee email addresses follow a pattern (user1@, user2@, user3@) | Bulk fake account creation "},{"path":"incentive-calculator.md","content":"# Referral Incentive Calculator\n\n## Unit Economics Worksheet\n\n| Input | Your value |\n|---|---|\n| Average Order Value (AOV) | $ |\n| Gross Margin % | % |\n| Gross Margin per order | $ (AOV × GM%) |\n| Current CAC (paid channels) | $ |\n| Target referral CAC | $ (aim for 40–60% of paid CAC) |\n| Maximum reward budget per acquisition | $ (GM per order − target referral CAC) |\n\n## Reward Value by Margin Tier\n\n### High-margin brands (>55% GM)\n- Referrer: $15–20 store credit or free product ($12–18 cost)\n- Referee: 15–20% off first order\n- Total reward cost per acquisition: ~$20–35\n- Viable if: AOV > $60 and referral CAC < $35\n\n### Mid-margin brands (35–55% GM)\n- Referrer: $10–15 store credit or 15% off next order\n- Referee: 10–15% off first order\n- Total reward cost per acquisition: ~$15–25\n- Viable if: AOV > $45 and referral CAC < $25\n\n### Lower-margin brands (<35% GM)\n- Referrer: 10% off next order (cost deferred to second purchase)\n- Referee: 10% off first order\n- Alternative: Unlock reward only after referred customer makes second purchase\n- Note: Straightforward cash rewards may not be viable at this margin level\n\n## Reward Type Comparison\n\n| Reward type | Perceived value | Actual cost | Share rate impact | Best for |\n|---|---|---|---|---|\n| Free product (travel/sample size) | High | Low (COGS) | +30–40% vs. cash | Brands with low-COGS samples |\n| Store credit | High | Medium (discounts future revenue) | Neutral | Subscription or repeat-purchase brands |\n| % discount off next order | Medium | Medium | Neutral | Mid-margin brands |\n| Flat $ cash discount | Medium | Medium | Baseline | Any brand |\n| Exclusive early access | High | Very low | +15–25% | Brands with strong product pipeline |\n| Charity donation on their behalf | Low-medium | Low | Below baseline | Only for brand-values-driven audiences |\n\n## Double-Sided vs. Single-Sided\n\nResearch consistently shows double-sided referral programs (both referrer and referee get rewards) outperform single-sided by 30–50% in:\n- Share rate (referrer more willing to share when friend also benefits)\n- Referee conversion rate (new customer more likely to act on offer)\n- Referral program NPS (customers view program as generous, not transactional)\n\n**Minimum viable double-sided offer:**\n- Referrer: 10% off next order or $10 credit\n- Referee: 10% off first order\n- Combined cost at 40% GM on $50 AOV: ~$10 per acquisition (vs. paid CAC of $35+)\n\n## Break-Even Referral Rate Calculator\n\nHow many referrals must convert to justify program costs?\n\n| Fixed program cost (software) | $99/month (e.g., Smile.io Pro) |\n|---|---|\n| Variable cost per referred acquisition | $18 average reward cost |\n| Break-even referrals to cover software | 99 ÷ 18 = 6 referral conversions to cover tool cost |\n\nAt 6+ referral acquisitions per month, even basic referral tools pay for themselves before any CAC comparison."},{"path":"output-template.md","content":"# Referral Program Design Brief\n\n## Brand Overview\n- **Brand name:** \n- **AOV:** $\n- **Gross margin %:** %\n- **Current paid CAC:** $\n- **Target referral CAC:** $\n\n## Program Economics\n- **Referrer reward:** \n- **Referee reward:** \n- **Minimum order to activate reward:** $\n- **Reward payout delay:** ___ days after delivery\n- **Estimated reward cost per acquisition:** $\n\n## Program Structure\n- **Type:** Standard / Loyalty-gated / Ambassador / Group mechanic\n- **Eligibility:** All customers / Repeat buyers / Loyalty tier members\n\n## Share Experience\n- **Referral link format:** \n- **Pre-written share message:**\n\n> [Draft message here — 2–3 sentences, first person, names the offer]\n\n- **Share channels enabled:** WhatsApp / SMS / Email / Copy link / Facebook / Instagram\n- **Visual referral card:** Yes / No (design needed)\n\n## Integration\n- **Platform:** Shopify / WooCommerce / Custom\n- **Referral tool:** Smile.io / ReferralCandy / Yotpo / Friendbuy / Custom\n- **Email trigger:** Post-purchase Day ___ / Order confirmation / My Account\n\n## Fraud Prevention\n- [ ] Reward activates on shipped order only\n- [ ] Self-referral email domain check enabled\n- [ ] IP deduplication enabled\n- [ ] Minimum order threshold: $___\n- [ ] Payout delay: ___ days\n- [ ] Device fingerprinting: Yes / No\n- [ ] Temp email domain blocklist: Yes / No\n\n## Sharing Touchpoints\n- [ ] Order confirmation page\n- [ ] Day 7–14 post-delivery email\n- [ ] My Account → Referrals page (persistent)\n- [ ] Packaging insert / unboxing card\n\n## Launch Plan\n- **Soft launch cohort:** ___ top customers (target: 100–200)\n- **Soft launch date:** \n- **Full launch date:** \n- **Announcement email subject A:** \n- **Announcement email subject B:** \n\n## Success Metrics\n| Metric | 30-day target | 90-day target |\n|---|---|---|\n| Share rate | % | % |\n| Referee conversion rate | % | % |\n| New customers via referral | | |\n| Referral CAC | $ | $ |\n| Referral % of new customer mix | % | % |"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":null,"editorialQuality":{"score":100,"threshold":65,"status":"thin","wordCount":1912,"uniquenessScore":44,"reasons":["uniqueness-below-45"]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-11T18:09:07.189Z","emptyReason":"No screenshots, media assets, or demo links are 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