referral-program
# ClaudeWave Entry: referral-program The referral-program skill guides strategy for referral initiatives in AI and SaaS products, covering reward structures, mechanism types like link-based and code-based systems, and tracking methods. Use this skill when users ask about implementing or optimizing referral programs, refer-a-friend campaigns, referral incentives, or leveraging existing users for growth, particularly in overseas markets where referral programs are essential rather than optional.
git clone --depth 1 https://github.com/kostja94/marketing-skills /tmp/referral-program && cp -r /tmp/referral-program/skills/channels/partnerships/referral-program ~/.claude/skills/referral-programSKILL.md
# 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; 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 the user wants to analyze Google Search Console data, use the GSC API, or interpret search performance. Also use when the user mentions "GSC," "Search Console," "indexing report," "Core Web Vitals," "Enhancements," "Insights report," "search performance," "search queries," "search performance report," "URL inspection," "impressions," "CTR," "average position," "index coverage," "GSC data analysis," "Search Console API," or "searchanalytics.query." When the user wants to rewrite title tags (not only report on them), use title-tag. For meta description rewrites, use meta-description.
When the user wants to build an SEO data analysis system, monitor indexing/traffic/keywords/backlinks, or set up benchmarks. Also use when the user mentions "SEO data analysis," "SEO monitoring," "article database," "traffic benchmark," "penalty recovery," "SEO work document," "SEO dashboard," "keyword tracking," "ranking monitoring," "indexing report," or "backlink monitoring." For GSC API, use google-search-console.
When the user wants to track AI search traffic in GA4 or GSC. Also use when the user mentions "AI traffic," "ChatGPT referral," "Perplexity traffic," "AI Overviews," "GA4 AI sources," "AI search analytics," "track AI referrals," "AI search traffic," "Claude traffic," or "how to track AI traffic." For AI SEO strategy, use generative-engine-optimization.
When the user wants to analyze website traffic sources, attribution, or dark traffic. Also use when the user mentions "traffic sources," "dark traffic," "direct traffic," "UTM parameters," "traffic attribution," "channel attribution," "attribution optimization," "channel analysis," "traffic analysis," "traffic diversification," "natural traffic benchmark," or "organic vs paid traffic." For GA4 setup, use analytics-tracking.
When the user wants to set up, audit, or optimize analytics tracking (GA4, events, conversions). Also use when the user mentions "Google Analytics," "GA4," "event tracking," "conversions," "attribution model," "gtag," "data layer," "GA4 setup," "conversion tracking," "event setup," "User ID tracking," or "CTA attribution." For traffic insights, use traffic-analysis.
When the user wants to promote via forums, communities, or invite users to join a community. Also use when the user mentions "forum promotion," "Indie Hacker," "Hacker News," "community growth," "Discord promotion," "vertical community," "brand encyclopedia," "Wikipedia," "Quora," "Reddit community," "community building," "forum marketing," or "community invite." For Reddit copy, use reddit-posts. For strategy, use integrated-marketing.
When the user wants to submit a product or app to directories, curated lists, launch platforms, or app stores—and needs ready-to-paste copy per platform. Reads project-context.md when present. Also use when the user mentions "directory submission," "get listed," "app store listing," "submit to directories," "curated list," "best tools list," "Taaft," "Product Hunt," "directory ads," "newsletter feature," "directory campaign," "tailor description per platform," "Shopify App Store," "Chrome Web Store," "navigation site," or "product directory." For Product Hunt launch day tactics (hunter, first comment, timing), use product-hunt-launch. For full 0→1 channel planning, use cold-start-strategy.
When the user wants to launch on Product Hunt, prepare a PH submission, or plan launch day (hunter, first comment, timing, upvotes). Also use when the user mentions "Product Hunt," "launch on Product Hunt," "PH launch," "Product Hunt submission," "hunter," "Product of the Day," "upvotes," or "Product Hunt first comment." For multi-platform directory listings and paste-ready copy beyond PH, use directory-submission.