Referral Program
Plan, implement, or optimize a referral program for AI or SaaS with clearer economics than paid acquisition alone.
Overview
Referral Program is an agent skill most often used in Grow (also Validate pricing) that plans and optimizes user referral strategy for AI and SaaS products.
Install
npx skills add https://github.com/kostja94/marketing-skills --skill referral-programWhat is this skill?
- Structured referral vs affiliate vs influencer comparison
- Initial assessment: product type, user base size, signup vs purchase goals
- Benchmarks cited: 3–5% conversion vs ads, lower CAC, higher LTV for referred users
- Pairs with landing-page-generator for referral landing copy
- Reads .claude or .cursor project-context.md when present
- Document cites ~3%–5% referral conversion vs ~1%–2% for ads
- Document cites CAC ~50%–70% lower and referred-user LTV ~30%–50% higher
Adoption & trust: 743 installs on skills.sh; 586 GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
Paid channels are expensive and referred users convert better, but you lack a structured referral vs affiliate plan tied to your product and user base.
Who is it for?
SaaS or AI tools with an engaged user base exploring refer-a-friend loops before scaling ads.
Skip if: One-off influencer campaigns or affiliate-only programs without existing users to refer.
When should I use this skill?
User mentions referral program, referral marketing, refer-a-friend, referral code, referral rewards, viral loop, or related phrases in frontmatter triggers.
What do I get? / Deliverables
You leave with a referral strategy framed by goals, channel contrasts, and implementation considerations—ready to wire tracking and landing experiences.
- Referral program strategy assessment and channel recommendations
- Referral vs affiliate vs influencer decision framing
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Grow lifecycle is the canonical shelf because referral loops monetize existing users after product-market fit, though scope work in Validate informs reward design. Lifecycle covers retention-linked incentives, tracking, and viral loops—not one-off launch posts.
Where it fits
Define double-sided rewards and tracking before enabling refer-a-friend in your app.
Check whether reward cost fits margin before promising lifetime discounts for referrals.
How it compares
Strategic referral playbook—use landing-page-generator for referral landing copy instead of mixing both in one pass.
Common Questions / FAQ
Who is referral-program for?
Indie founders and small teams selling SaaS or AI subscriptions who want referral-led growth with clearer unit economics than display or search ads alone.
When should I use referral-program?
Use it in Grow when designing lifecycle incentives and tracking; in Validate when pricing and reward tradeoffs affect whether referrals can fund acquisition.
Is referral-program safe to install?
Review the Security Audits panel on this Prism page; the skill is advisory markdown—no built-in payment or network actions beyond reading local project context if you allow it.
Workflow Chain
Then invoke: landing page generator
SKILL.md
READMESKILL.md - Referral Program
# 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: 1. **Product type**: SaaS, AI tool, subscription 2. **User base**: Size, engagement, retention 3. **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 1. **Reward structure**: Type (cash, discount, credits, free service); amount (10%-30% of price); trigger; cap 2. **Tracking**: Choose method; set attribution window; first-touch rule 3. **UX**: One-click share; clear rules; dashboard with referral data; notify on success 4. **Fraud prevention**: See above 5. **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