irismaker/referral-program
skills/cro/growth/referral/SKILL.md
When the user wants to plan, implement, or optimize referral program strategy. Also use when the user mentions "referral program," "referral marketing," "user referral," "refer-a-friend," "word-of-mouth growth," "referral rewards," "referral tracking," "referral code," "referral incentives," or "viral loop."
$ install --global
skillsauthnpx skillsauth add irismaker/ai-agent-skills-hub referral-programInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 23, 2026, 4:12 PM319.0s1 file scanned
SKILL.md
- name:
- referral-program
- description:
- When the user wants to plan, implement, or optimize referral program strategy. Also use when the user mentions "referral program," "referral marketing," "user referral," "refer-a-friend," "word-of-mouth growth," "referral rewards," "referral tracking," "referral code," "referral incentives," or "viral loop.
- version:
- 1.0.0
Channels: Referral
Guides referral program strategy for AI/SaaS products. Leverage existing users to drive growth; 3%-5% conversion vs 1%-2% for ads; CAC 50%-70% lower; referred users LTV 30%-50% higher, retention 20%-30% higher. Referral is necessity in overseas markets, not alternative.
When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Initial Assessment
Check for product marketing context first: If .claude/product-marketing-context.md or .cursor/product-marketing-context.md exists, read it for product, audience, and value proposition.
Identify:
- Product type: SaaS, AI tool, subscription
- User base: Size, engagement, retention
- Goal: Signups, purchases, or both
Referral vs. Affiliate vs. Influencer
| Dimension | Referral | Affiliate | Influencer | |-----------|----------|-----------|------------| | Who | Existing users | Professional promoters | KOLs | | Incentive | Discounts, credits | Commission | Fees, product | | Barrier | Low (all users) | Medium | High | | Conversion | 3%-5% | Varies | Varies |
Referral vs affiliate: Referral needs no landing page or application; integrated in dashboard. Affiliate requires landing page and approval.
Reward Models
| Model | Use | |-------|-----| | Two-way | Both referrer and referee get rewards; highest participation | | One-way | Only referrer rewarded; cost control | | Tiered | Rewards increase with referral count (e.g. $10 for 1-5, $15 for 6-10, $20 for 11+); incentivizes volume |
Benchmark: Rewards typically 10%-30% of product price; ~11% off or ~$21 value; weak incentives = low participation. Triggers: signup, purchase, activation, or sustained use.
Mechanism Types
| Type | Use | |------|-----| | Link-based | Unique referral link; easy to implement; accurate tracking; share via email, social, SMS; works for web and app | | Code-based | Referral code (e.g. FRIEND20); memorable; offline events; mobile-friendly input | | Social referral | Share buttons (Facebook, X, LinkedIn); viral spread; friend trust; young users |
Tracking & Attribution
| Method | Use | |-------|-----| | Cookie | Web apps; 30-90 day window | | URL params | All platforms; persistent in link | | Referral code | Mobile, offline; manual entry | | Account association | Long-term tracking; subscription products |
Attribution window: 30-90 days typical; 180 days for subscription. First-touch attribution to avoid double-counting.
Fraud Prevention
| Risk | Action | |------|--------| | Self-referral | Detect same device, payment, IP | | Fake accounts | Validate email, payment; monitor patterns | | Bulk/automation | Rate limits; anomaly detection | | Per-user cap | e.g. Max 10 referrals per user |
Use tool anti-fraud features; audit referrals regularly.
Design Framework
- Reward structure: Type (cash, discount, credits, free service); amount (10%-30% of price); trigger; cap
- Tracking: Choose method; set attribution window; first-touch rule
- UX: One-click share; clear rules; dashboard with referral data; notify on success
- Fraud prevention: See above
- Monitor & optimize: Referral rate, conversion, CAC, LTV; A/B test rewards and flow
Best Practices
- Run multiple programs: Target different audiences, stages, goals
- Tiered rewards: Motivate top performers; progressive incentives
- Friction-free sharing: Mobile-friendly; one-click share
- Time-boxed incentives: "Refer this week for $15 off" creates urgency
- Placement: Web, email, app, in-product touchpoints; dashboard integration primary
Implementation
| Approach | Use | |----------|-----| | Self-build | Full control; low cost; URL params or cookie + reward logic + fraud checks; open-source (e.g. RefRef) for faster start | | Third-party | Fast launch; Cello, Viral Loops, ReferralCandy (e-commerce), Impact (enterprise); monthly fee |
Placement: Most programs integrate in product dashboard; no landing page or application needed. Optional landing page for value prop, rewards, and case studies.
Startup cost: Typically hundreds for tools + dev.
Tools
| Tool | Use | |------|-----| | Cello | SaaS; AI-driven automation | | Viral Loops | Referral + waitlist + contests | | ReferralCandy | Shopify, e-commerce | | Impact | Enterprise; unified platform | | RefRef | Open-source; self-hosted |
KPIs
Referral rate, conversion, CAC, LTV of referred users, referred-user retention.
Output Format
- Reward model and mechanism type (link/code/social)
- Tracking approach and attribution window
- Placement (dashboard vs landing page)
- Fraud prevention measures
- Tool selection (self-build vs third-party)
- KPI framework
Related Skills
- discount-marketing-strategy: Referral rewards (discounts, credits); 10–30% benchmark; campaign design
- affiliate-marketing: Different audience; can run both
- influencer-marketing: Brand building vs. user-driven growth
- directory-submission: Directory submission for discovery; referral for user-driven growth
- analytics-tracking: Referral link tracking, UTM
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