referral-program
Plan, implement, and optimize referral marketing programs to boost user growth and retention.
Install
mkdir -p .claude/skills/referral-program-kostja94 && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/17107" && unzip -o skill.zip -d .claude/skills/referral-program-kostja94 && rm skill.zipInstalls to .claude/skills/referral-program-kostja94
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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." For referral landing copy, use landing-page-generator.Key capabilities
- →Guide referral program strategy for AI/SaaS products
- →Identify product type, user base, and goal for a referral program
- →Compare referral, affiliate, and influencer marketing models
- →Recommend reward models like two-way, one-way, or tiered
- →Suggest mechanism types such as link-based, code-based, or social referral
- →Outline fraud prevention actions for referral programs
How it works
This skill guides referral program strategy by assessing project context, comparing marketing models, and outlining reward and mechanism types.
Inputs & outputs
When to use referral-program
- →Design referral program
- →Optimize referral conversion
- →Setup referral incentives
About this skill
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 project context first: If .claude/project-context.md or .cursor/project-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
When not to use it
- →When the user needs a landing page for affiliate marketing
- →When the user needs to build brand awareness through influencer marketing
- →When the user needs to submit a directory for discovery
Limitations
- →This skill focuses on referral programs for AI/SaaS products
- →This skill does not generate referral landing page copy
- →This skill does not implement the referral program directly
How it compares
This workflow provides a structured framework for designing and optimizing referral programs, unlike manually piecing together strategies.
Compared to similar skills
referral-program side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| referral-program (this skill) | 0 | 4mo | No flags | Intermediate |
| marketing-campaign-management | 0 | 3mo | No flags | Intermediate |
| podcast-marketing | 0 | 4mo | No flags | Beginner |
| scriptwriting | 17 | 9mo | No flags | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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