suede-referrals
Suede-owned referral and affiliate program discipline. Use when designing refer-a-friend mechanics, ambassador or partner incentives, fraud controls, attribution, payout logic, or viral-loop measurement. NOT FOR: executing payouts or changing billing, launch-wide packaging (use s
Install
npx skills add https://github.com/JasonColapietro/suede-creator-skills/tree/main/skills/suede-referrals
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install jasoncolapietro-suede-creator-skills@llmmart
git clone https://github.com/JasonColapietro/suede-creator-skills.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole jasoncolapietro/suede-creator-skills collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Suede Referral & Affiliate Programs
Suede Referrals designs measurable customer, affiliate, and partner loops from incentive economics through attribution and fraud controls. It separates modeled loop performance from observed results and keeps activation and payouts behind approval.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
1. Program Type
- Customer referral program, affiliate program, or both?
- B2B or B2C?
- What's the average customer LTV?
- What's your current CAC from other channels?
2. Current State
- Existing referral/affiliate program?
- Current referral rate (% who refer)?
- What incentives have you tried?
3. Product Fit
- Is your product shareable?
- Does it have network effects?
- Do customers naturally talk about it?
4. Resources
- Tools/platforms you use or consider?
- Budget for referral incentives?
Referral vs. Affiliate
Customer Referral Programs
Best for:
- Existing customers recommending to their network
- Products with natural word-of-mouth
- Lower-ticket or self-serve products
Characteristics:
- Referrer is an existing customer
- One-time or limited rewards
- Higher trust, lower volume
Affiliate Programs
Best for:
- Reaching audiences you don't have access to
- Content creators, influencers, bloggers
- Higher-ticket products that justify commissions
Characteristics:
- Affiliates may not be customers
- Ongoing commission relationship
- Higher volume, variable trust
Referral Program Design
The Referral Loop
Trigger Moment → Share Action → Convert Referred → Reward → (Loop)
Step 1: Identify Trigger Moments
High-intent moments:
- Right after first "aha" moment
- After achieving a milestone
- After exceptional support
- After renewing or upgrading
Prompt cadence for customers who have not referred: day 7, day 30, day 60,
and after any milestone. The timing is this skill's call; the message copy is
not — hand that to suede-emails.
Step 2: Design Share Mechanism
Ranked by effectiveness:
- In-product sharing (highest conversion)
- Personalized link
- Email invitation
- Social sharing
- Referral code (works offline)
Step 3: Choose Incentive Structure
Single-sided rewards (referrer only): Simpler, works for high-value products
Double-sided rewards (both parties): Higher conversion, win-win framing
Tiered rewards: Gamifies referral process, increases engagement
For examples and incentive sizing: See references/program-examples.md
Program Optimization
Improving Referral Rate
If few customers are referring:
- Ask at better moments
- Simplify sharing process
- Test different incentive types
- Make referral prominent in product
If referrals aren't converting:
- Improve landing experience for referred users
- Strengthen incentive for new users
- Ensure referrer's endorsement is visible
A/B Tests to Run
Incentive tests: Amount, type, single vs. double-sided, timing
Messaging tests: Program description, CTA copy, landing page copy
Placement tests: Where and when the referral prompt appears
Common Problems & Fixes
| Problem | Fix |
|---|---|
| Low awareness | Add prominent in-app prompts |
| Low share rate | Simplify to one click |
| Low conversion | Optimize referred user experience |
| Fraud/abuse | Apply the fraud controls below |
| One-time referrers | Add tiered/gamified rewards |
Fraud Controls
Read references/affiliate-programs.md §Fraud Prevention before designing rewards or writing program terms — it carries the technical, policy, and structural control set (delayed payout after activation, device and IP signals, clawback on refunds, per-period caps, manual review of suspicious patterns). Every program in this skill uses it, customer referral programs included, not only affiliate programs.
Set these thresholds explicitly, because the reference leaves them open:
- Name the qualifying downstream event that releases a reward (a paid conversion or day-N retention, N stated), never signup alone.
- Hold payouts until the refund/chargeback window has closed, and state that window in days.
- State the dollar amount above which a payout goes to manual review before it is released.
Measuring Success
Key Metrics
Program health:
- Active referrers (referred someone in last 30 days)
- Referral conversion rate
- Rewards earned/paid
Business impact:
- % of new customers from referrals
- CAC via referral vs. other channels
- LTV of referred customers
- Referral program ROI
Typical Findings
Industry-reported ranges, published by referral-platform vendors and not measured on this product. Use them to calibrate a recommendation; never assert them as a result this program will produce or has produced.
- Referred customers have 16-25% higher LTV
- Referred customers have 18-37% lower churn
- Referred customers refer others at 2-3x rate
Launch Checklist
Before Launch
- Define program goals and success metrics
- Design incentive structure
- Build or configure referral tool
- Create referral landing page
- Set up tracking and attribution
- Define fraud prevention rules
- Create terms and conditions
- Test complete referral flow
Launch
- Announce to existing customers
- Add in-app referral prompts
- Update website with program details
- Brief support team
Post-Launch (First 30 Days)
- Review conversion funnel
- Identify top referrers
- Gather feedback
- Fix friction points
- Send reminder emails to non-referrers
Affiliate Programs
For affiliate program design, commission structures, recruitment, fraud prevention, and tools: See references/affiliate-programs.md
Tool Integrations
These are evaluation examples, not guaranteed integrations. Verify current vendor documentation, pricing, account access, attribution behavior, payout controls, tax support, and data-export terms before recommending a platform.
| Tool | Best For | Verify Before Use |
|---|---|---|
| Rewardful / Tolt | SaaS affiliate programs | Billing integration, attribution, payouts |
| Mention Me | Enterprise referral programs | Identity, fraud, and reporting controls |
| Dub.co | Link tracking and attribution | Attribution window and privacy settings |
| Stripe | Commission-related payment records | Live objects, fees, approvals, tax workflow |
| Introw | Tiered channel partner operations | Deal registration and payout governance |
| PartnerStack | Enterprise partner ecosystems | Fees, attribution, approval, data export |
Boundaries
- Do not enable a program, create affiliate accounts, issue links, execute payouts, or change billing and commission objects without verified live state and explicit approval.
- Do not invent attribution, conversion, fraud, or viral-coefficient results; distinguish modeled economics from observed data.
- Do not decide tax, labor, privacy, contest, endorsement, or incentive compliance. Surface the jurisdiction-specific review needed before launch.
Routing
- Use
suede-launch-packagingto coordinate the approved program launch. - Use
suede-emailsfor referral invitation and nurture sequences. - Use
suede-marketing-psychologyto test incentive framing. - Use
suede-ab-testingto design and evaluate the incentive, messaging, and placement tests above. - Use
suede-analyticsto define and read referral attribution.
Files (suede-creator-skills)
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agents
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openai.yaml 581 B
interface: display_name: "Suede Referrals" short_description: "Suede referral, affiliate, and partner loop design" default_prompt: "Use $suede-referrals on [target]. The user wants existing users or partners bringing in new customers, or is designing a referral or affiliate program. Work through referral mechanics, incentive design that does not attract fraud, affiliate structure, and viral loop math, ground every recommendation in evidence the user can check, and return the decisions, the reasoning, and what to measure next." policy: allow_implicit_invocation: true
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evals
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evals.json 6.3 KB
{ "skill_name": "suede-referrals", "evals": [ { "id": 1, "prompt": "Help me design a referral program for our SaaS product. We're a $49/month project management tool with about 1,000 customers. We want to encourage word-of-mouth growth.", "expected_output": "Should check for product-marketing.md first. Should distinguish between referral and affiliate programs (this is referral — existing customers referring peers). Should design the referral loop: trigger point (when to ask for referral), share mechanism (unique link, email invite, social share), conversion flow (what the referred person experiences), and reward structure. Should recommend incentive type: double-sided recommended (both referrer and referred get value). Should suggest specific incentives appropriate for $49/month SaaS (e.g., free month for both). Should include the launch checklist. Should recommend tool integrations (Rewardful, Tolt, etc.).", "assertions": [ "Checks for product-marketing.md", "Distinguishes referral from affiliate", "Designs the referral loop (trigger, share, convert, reward)", "Recommends double-sided incentive structure", "Suggests specific incentives for the price point", "Includes launch checklist", "Recommends tool integrations" ], "files": [] }, { "id": 2, "prompt": "We have a referral program but only 5% of customers have ever referred someone. How do we increase participation?", "expected_output": "Should apply the program optimization guidance. Should diagnose low participation: are customers aware of the program? Is the trigger point well-timed? Is the incentive compelling enough? Is sharing easy? Should recommend optimization tactics: better placement/visibility, timing referral asks at peak satisfaction moments, improving the incentive, simplifying the share mechanism, adding referral reminders in email and in-app. Should provide specific experiment ideas to test improvements.", "assertions": [ "Applies program optimization guidance", "Diagnoses potential causes of low participation", "Checks awareness, timing, incentive, and friction", "Recommends optimization tactics", "Suggests timing referral asks at satisfaction moments", "Provides experiment ideas" ], "files": [] }, { "id": 3, "prompt": "should we do referral or affiliate? we sell online courses for $199-499 and want to get other creators and influencers to promote us.", "expected_output": "Should trigger on casual phrasing. Should apply the referral vs affiliate distinction clearly. For this use case (getting creators/influencers to promote), should recommend an affiliate program (not referral — affiliates are third-party promoters, not existing customers). Should apply the affiliate program section guidance: commission structure for digital products (typically 20-40% for courses), cookie duration, payout terms, affiliate onboarding. Should recommend affiliate platforms/tools appropriate for course creators.", "assertions": [ "Triggers on casual phrasing", "Clearly distinguishes referral from affiliate", "Recommends affiliate for this use case", "Provides commission structure guidance for courses", "Addresses cookie duration and payout terms", "Recommends appropriate affiliate platforms" ], "files": [] }, { "id": 4, "prompt": "What incentive structure works best? We've been offering $10 off for referrers but it's not working. Our product is $29/month.", "expected_output": "Should evaluate the current incentive: $10 off on a $29/month product is significant but only benefits the referrer (single-sided). Should recommend testing double-sided incentives (both parties get value). Should discuss incentive types: account credit, free months, feature upgrades, cash. Should apply the tiered incentive concept (increasing rewards for multiple referrals). Should provide specific alternative incentive structures to test. Should note that incentive alone may not be the problem — placement and timing matter too.", "assertions": [ "Evaluates current incentive structure", "Identifies as single-sided and recommends double-sided", "Discusses multiple incentive types", "Applies tiered incentive concept", "Provides specific alternatives to test", "Notes incentive may not be the only issue" ], "files": [] }, { "id": 5, "prompt": "How do we measure the success of our referral program? What metrics should we track?", "expected_output": "Should apply the measuring success framework. Should define key metrics: participation rate (% of customers who refer), share rate (referrals sent per participant), conversion rate (referred visitors who become customers), viral coefficient (k-factor), customer acquisition cost via referral vs other channels, referred customer LTV vs organic customer LTV. Should recommend tracking tools and dashboards. Should provide benchmark ranges for each metric.", "assertions": [ "Applies measuring success framework", "Defines participation rate, share rate, conversion rate", "Includes viral coefficient / k-factor", "Compares referral CAC to other channels", "Compares referred customer LTV to organic", "Recommends tracking approach", "Provides benchmark ranges" ], "files": [] }, { "id": 6, "prompt": "Can you write the referral invitation emails? I need the email that goes out when someone shares their referral link.", "expected_output": "Should recognize this overlaps with email writing. Should apply the referral email sequence section for referral-specific emails. However, for detailed multi-email nurture design, should cross-reference `suede-emails`. Should provide the referral invitation email but note that broader email sequence work is handled by `suede-emails`.", "assertions": [ "Applies referral email section from the skill", "Provides referral invitation email guidance", "Cross-references emails for broader email work", "Provides specific referral email copy or template" ], "files": [] } ] }
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references
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affiliate-programs.md 4.3 KB
# Affiliate Program Design Detailed guidance for building and managing affiliate programs. ## Contents - Commission Structures - Cookie Duration - Affiliate Recruitment - Affiliate Enablement - Tools & Platforms (Referral Program Tools, Affiliate Program Tools, Choosing a Tool) - Fraud Prevention (Common Referral Fraud, Prevention Measures) ## Commission Structures **Percentage of sale:** - Standard: 10-30% of first sale or first year - Works for: E-commerce, SaaS with clear pricing - Example: "Earn 25% of every sale you refer" **Flat fee per action:** - Standard: $5-500 depending on value - Works for: Lead gen, trials, freemium - Example: "$50 for every qualified demo" **Recurring commission:** - Standard: 10-25% of recurring revenue - Works for: Subscription products - Example: "20% of subscription for 12 months" **Tiered commission:** - Works for: Motivating high performers - Example: "20% for 1-10 sales, 25% for 11-25, 30% for 26+" --- ## Cookie Duration How long after click does affiliate get credit? | Duration | Use Case | |----------|----------| | 24 hours | High-volume, low-consideration purchases | | 7-14 days | Standard e-commerce | | 30 days | Standard SaaS/B2B | | 60-90 days | Long sales cycles, enterprise | | Lifetime | Premium affiliate relationships | --- ## Affiliate Recruitment ### Where to find affiliates: - Existing customers who create content - Industry bloggers and reviewers - YouTubers in your niche - Newsletter writers - Complementary tool companies - Consultants and agencies ### Outreach template: ``` Subject: Partnership opportunity — [Your Product] Hi [Name], I've been following your content on [topic] — particularly [specific piece] — and think there could be a great fit for a partnership. [Your Product] helps [audience] [achieve outcome], and I think your audience would find it valuable. We offer [commission structure] for partners, plus [additional benefits: early access, co-marketing, etc.]. Would you be open to learning more? [Your name] ``` --- ## Affiliate Enablement Provide affiliates with: - [ ] Unique tracking links/codes - [ ] Product overview and key benefits - [ ] Target audience description - [ ] Comparison to competitors - [ ] Creative assets (logos, banners, images) - [ ] Sample copy and talking points - [ ] Case studies and testimonials - [ ] Demo access or free account - [ ] FAQ and objection handling - [ ] Payment terms and schedule --- ## Tools & Platforms ### Referral Program Tools **Full-featured platforms:** - ReferralCandy — E-commerce focused - Ambassador — Enterprise referral programs - Friendbuy — E-commerce and subscription - GrowSurf — SaaS and tech companies - Mention Me — AI-powered referral marketing - Viral Loops — Template-based campaigns **Built-in options:** - Stripe (basic referral tracking) - HubSpot (CRM-integrated) - Segment (tracking and analytics) ### Affiliate Program Tools **Affiliate networks:** - ShareASale — Large merchant network - Impact — Enterprise partnerships - PartnerStack — SaaS focused - Tapfiliate — Simple SaaS affiliate tracking - FirstPromoter — SaaS affiliate management **Partner Relationship Management (PRM):** - Introw — Full PRM with deal registration, commissions, tiers, QBRs, and partner engagement tracking. Verify current vendor documentation, account access, pricing, and payout controls before use. **Self-hosted:** - Rewardful — Stripe-integrated affiliates - Refersion — E-commerce affiliates ### Choosing a Tool Consider: - Integration with your payment system - Fraud detection capabilities - Payout management - Reporting and analytics - Customization options - Price vs. program scale --- ## Fraud Prevention ### Common Referral Fraud - Self-referrals (creating fake accounts) - Referral rings (groups referring each other) - Coupon sites posting referral codes - Fake email addresses - VPN/device spoofing ### Prevention Measures **Technical:** - Email verification required - Device fingerprinting - IP address monitoring - Delayed reward payout (after activation) - Minimum activity threshold **Policy:** - Clear terms of service - Maximum referrals per period - Reward clawback for refunds/chargebacks - Manual review for suspicious patterns **Structural:** - Require referred user to take meaningful action - Cap lifetime rewards - Pay rewards in product credit (less attractive to fraudsters) -
program-examples.md 3.2 KB
# Referral Program Examples Real-world examples of successful referral programs. ## Contents - Dropbox (Classic) - Uber/Lyft - Morning Brew - Notion - Incentive Types Comparison - Incentive Sizing Framework - Viral Coefficient & Metrics (Key Metrics, Calculating Referral Program ROI) ## Dropbox (Classic) **Program:** Give 500MB storage, get 500MB storage **Why it worked:** - Reward directly tied to product value - Low friction (just an email) - Both parties benefit equally - Gamified with progress tracking --- ## Uber/Lyft **Program:** Give $10 ride credit, get $10 when they ride **Why it worked:** - Immediate, clear value - Double-sided incentive - Easy to share (code/link) - Triggered at natural moments --- ## Morning Brew **Program:** Tiered rewards for subscriber referrals - 3 referrals: Newsletter stickers - 5 referrals: T-shirt - 10 referrals: Mug - 25 referrals: Hoodie **Why it worked:** - Gamification drives ongoing engagement - Physical rewards are shareable (more referrals) - Low cost relative to subscriber value - Built status/identity --- ## Notion **Program:** $10 credit per referral (education) **Why it worked:** - Targeted high-sharing audience (students) - Product naturally spreads in teams - Credit keeps users engaged --- ## Incentive Types Comparison | Type | Pros | Cons | Best For | |------|------|------|----------| | Cash/credit | Universally valued | Feels transactional | Marketplaces, fintech | | Product credit | Drives usage | Only valuable if they'll use it | SaaS, subscriptions | | Free months | Clear value | May attract freebie-seekers | Subscription products | | Feature unlock | Low cost to you | Only works for gated features | Freemium products | | Swag/gifts | Memorable, shareable | Logistics complexity | Brand-focused companies | | Charity donation | Feel-good | Lower personal motivation | Mission-driven brands | --- ## Incentive Sizing Framework **Calculate your maximum incentive:** ``` Max Referral Reward = (Customer LTV × Gross Margin) - Target CAC ``` **Example:** - LTV: $1,200 - Gross margin: 70% - Target CAC: $200 - Max reward: ($1,200 × 0.70) - $200 = $640 **Typical referral rewards:** - B2C: $10-50 or 10-25% of first purchase - B2B SaaS: $50-500 or 1-3 months free - Enterprise: Higher, often custom --- ## Viral Coefficient & Metrics ### Key Metrics **Viral coefficient (K-factor):** ``` K = Invitations × Conversion Rate K > 1 = Viral growth (each user brings more than 1 new user) K < 1 = Amplified growth (referrals supplement other acquisition) ``` **Example:** - Average customer sends 3 invitations - 15% of invitations convert - K = 3 × 0.15 = 0.45 **Referral rate:** ``` Referral Rate = (Customers who refer) / (Total customers) ``` Benchmarks: - Good: 10-25% of customers refer - Great: 25-50% - Exceptional: 50%+ **Referrals per referrer:** Benchmarks: - Average: 1-2 referrals per referrer - Good: 2-5 - Exceptional: 5+ ### Calculating Referral Program ROI ``` Referral Program ROI = (Revenue from referred customers - Program costs) / Program costs Program costs = Rewards paid + Tool costs + Management time ``` **Track separately:** - Cost per referred customer (CAC via referral) - LTV of referred customers (often higher than average) - Payback period for referral rewards
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CARD.md 4.2 KB
# Skill Card — Suede Referral & Affiliate Programs <!-- Generated by scripts/build-skill-cards.mjs — do not hand-edit. --> <!-- Regenerate with: npm run build:cards --> Release record for the `suede-referrals` skill, following the NVIDIA skill-card template (<https://docs.nvidia.com/skills/skill-cards>). It tells a reviewer what the skill does, who owns it, what it needs, what could go wrong, and what evidence backs the release — without requiring them to open the source first. ## Description Suede-owned referral and affiliate program discipline. Status: production. Ships in the `suede-skills` plugin (the full pack) at release 0.19.0; loads as a Claude Code / Codex agent skill from this directory's [SKILL.md](./SKILL.md). ## Owner Jason Colapietro, Suede Labs AI (<https://github.com/JasonColapietro>). Security contact: `info@suedeai.ai` per [SECURITY.md](../../SECURITY.md). ## License / Terms of Use MIT ([LICENSE](../../LICENSE)). The pack's combined license expression is `MIT AND BSD-3-Clause`; this skill bundles no third-party licensed material of its own. ## Use Case Target users: developers and creators running the skill inside a Claude Code or Codex CLI session. Use when designing refer-a-friend mechanics, ambassador or partner incentives, fraud controls, attribution, payout logic, or viral-loop measurement. Out of scope — executing payouts or changing billing, launch-wide packaging (use suede-launch-packaging), lifecycle messaging (use suede-emails), or reporting unverified referral lift. ## Deployment Geography Global. The skill is a prompt-and-script package that runs locally inside the invoking agent session; it pins no region-specific service of its own. ## Requirements / Dependencies - A Claude Code or Codex CLI session with the `suede-skills` plugin installed (install options: <https://skills.suedeai.ai/>). - Bundled files loaded relative to this directory: `agents/` (1 file), `references/` (2 files). - Credentials: none are bundled or required by the skill files. Any tool or API credentials come from the host session; never paste credentials into skill files, prompts, or outputs. ## Known Risks and Mitigations - Risk: an agent treats a quality gate as autonomous authority. Mitigation: every gate in the pack is advisory — it changes what is reported, never what the user decided; only extreme-risk findings (data loss, credential exposure, legal/rights violations, payment mistakes, irreversible public damage) pause for the user's explicit choice. - Risk: a skill instruction is used to act outside its mandate. Mitigation: the hard limits in the skill body's "Boundaries" section, quoted below. From "Boundaries": - Do not enable a program, create affiliate accounts, issue links, execute payouts, or change billing and commission objects without verified live state and explicit approval. - Do not invent attribution, conversion, fraud, or viral-coefficient results; distinguish modeled economics from observed data. - Do not decide tax, labor, privacy, contest, endorsement, or incentive compliance. Surface the jurisdiction-specific review needed before launch. ## References - Skill source: [`skills/suede-referrals/SKILL.md`](./SKILL.md) - Rendered reference page: <https://skills.suedeai.ai/skills/suede-referrals.html> - Security policy and reviewed scanner exceptions: [SECURITY.md](../../SECURITY.md) and [`.plugin-scanner.toml`](../../.plugin-scanner.toml) at the repo root ## Skill Output Markdown analysis and recommendations returned in the agent's response. The skill publishes, posts, and sends nothing without the user's explicit authorization; delivery decisions stay with the user. ## Skill Version 0.19.0 — the pack is single-versioned, so every skill releases together; see [VERSION](../../VERSION) and [CITATION.cff](../../CITATION.cff) for the release identifier this card describes. ## Ethical Considerations - The skill produces recommendations for a human decision-maker. Publishing, sending, payment, and rights decisions stay with the user. - Its gates require verifiable claims and honest reporting; do not use the skill to fabricate claims, evidence, metrics, or attribution. - Report suspected misuse or a security concern privately per [SECURITY.md](../../SECURITY.md); do not open a public issue for it. -
SKILL.md 8.3 KB
--- name: suede-referrals description: "Suede-owned referral and affiliate program discipline. Use when designing refer-a-friend mechanics, ambassador or partner incentives, fraud controls, attribution, payout logic, or viral-loop measurement. NOT FOR: executing payouts or changing billing, launch-wide packaging (use suede-launch-packaging), lifecycle messaging (use suede-emails), or reporting unverified referral lift." metadata: version: 2.0.0 --- # Suede Referral & Affiliate Programs Suede Referrals designs measurable customer, affiliate, and partner loops from incentive economics through attribution and fraud controls. It separates modeled loop performance from observed results and keeps activation and payouts behind approval. ## Before Starting **Check for product marketing context first:** If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task. Gather this context (ask if not provided): ### 1. Program Type - Customer referral program, affiliate program, or both? - B2B or B2C? - What's the average customer LTV? - What's your current CAC from other channels? ### 2. Current State - Existing referral/affiliate program? - Current referral rate (% who refer)? - What incentives have you tried? ### 3. Product Fit - Is your product shareable? - Does it have network effects? - Do customers naturally talk about it? ### 4. Resources - Tools/platforms you use or consider? - Budget for referral incentives? --- ## Referral vs. Affiliate ### Customer Referral Programs **Best for:** - Existing customers recommending to their network - Products with natural word-of-mouth - Lower-ticket or self-serve products **Characteristics:** - Referrer is an existing customer - One-time or limited rewards - Higher trust, lower volume ### Affiliate Programs **Best for:** - Reaching audiences you don't have access to - Content creators, influencers, bloggers - Higher-ticket products that justify commissions **Characteristics:** - Affiliates may not be customers - Ongoing commission relationship - Higher volume, variable trust --- ## Referral Program Design ### The Referral Loop ``` Trigger Moment → Share Action → Convert Referred → Reward → (Loop) ``` ### Step 1: Identify Trigger Moments **High-intent moments:** - Right after first "aha" moment - After achieving a milestone - After exceptional support - After renewing or upgrading **Prompt cadence for customers who have not referred:** day 7, day 30, day 60, and after any milestone. The timing is this skill's call; the message copy is not — hand that to `suede-emails`. ### Step 2: Design Share Mechanism **Ranked by effectiveness:** 1. In-product sharing (highest conversion) 2. Personalized link 3. Email invitation 4. Social sharing 5. Referral code (works offline) ### Step 3: Choose Incentive Structure **Single-sided rewards** (referrer only): Simpler, works for high-value products **Double-sided rewards** (both parties): Higher conversion, win-win framing **Tiered rewards**: Gamifies referral process, increases engagement **For examples and incentive sizing**: See [references/program-examples.md](references/program-examples.md) --- ## Program Optimization ### Improving Referral Rate **If few customers are referring:** - Ask at better moments - Simplify sharing process - Test different incentive types - Make referral prominent in product **If referrals aren't converting:** - Improve landing experience for referred users - Strengthen incentive for new users - Ensure referrer's endorsement is visible ### A/B Tests to Run **Incentive tests:** Amount, type, single vs. double-sided, timing **Messaging tests:** Program description, CTA copy, landing page copy **Placement tests:** Where and when the referral prompt appears ### Common Problems & Fixes | Problem | Fix | |---------|-----| | Low awareness | Add prominent in-app prompts | | Low share rate | Simplify to one click | | Low conversion | Optimize referred user experience | | Fraud/abuse | Apply the fraud controls below | | One-time referrers | Add tiered/gamified rewards | ### Fraud Controls Read [references/affiliate-programs.md](references/affiliate-programs.md) §Fraud Prevention before designing rewards or writing program terms — it carries the technical, policy, and structural control set (delayed payout after activation, device and IP signals, clawback on refunds, per-period caps, manual review of suspicious patterns). Every program in this skill uses it, customer referral programs included, not only affiliate programs. Set these thresholds explicitly, because the reference leaves them open: - Name the qualifying downstream event that releases a reward (a paid conversion or day-N retention, N stated), never signup alone. - Hold payouts until the refund/chargeback window has closed, and state that window in days. - State the dollar amount above which a payout goes to manual review before it is released. --- ## Measuring Success ### Key Metrics **Program health:** - Active referrers (referred someone in last 30 days) - Referral conversion rate - Rewards earned/paid **Business impact:** - % of new customers from referrals - CAC via referral vs. other channels - LTV of referred customers - Referral program ROI ### Typical Findings Industry-reported ranges, published by referral-platform vendors and not measured on this product. Use them to calibrate a recommendation; never assert them as a result this program will produce or has produced. - Referred customers have 16-25% higher LTV - Referred customers have 18-37% lower churn - Referred customers refer others at 2-3x rate --- ## Launch Checklist ### Before Launch - [ ] Define program goals and success metrics - [ ] Design incentive structure - [ ] Build or configure referral tool - [ ] Create referral landing page - [ ] Set up tracking and attribution - [ ] Define fraud prevention rules - [ ] Create terms and conditions - [ ] Test complete referral flow ### Launch - [ ] Announce to existing customers - [ ] Add in-app referral prompts - [ ] Update website with program details - [ ] Brief support team ### Post-Launch (First 30 Days) - [ ] Review conversion funnel - [ ] Identify top referrers - [ ] Gather feedback - [ ] Fix friction points - [ ] Send reminder emails to non-referrers --- ## Affiliate Programs **For affiliate program design, commission structures, recruitment, fraud prevention, and tools**: See [references/affiliate-programs.md](references/affiliate-programs.md) --- ## Tool Integrations These are evaluation examples, not guaranteed integrations. Verify current vendor documentation, pricing, account access, attribution behavior, payout controls, tax support, and data-export terms before recommending a platform. | Tool | Best For | Verify Before Use | |------|----------|-------------------| | **Rewardful / Tolt** | SaaS affiliate programs | Billing integration, attribution, payouts | | **Mention Me** | Enterprise referral programs | Identity, fraud, and reporting controls | | **Dub.co** | Link tracking and attribution | Attribution window and privacy settings | | **Stripe** | Commission-related payment records | Live objects, fees, approvals, tax workflow | | **Introw** | Tiered channel partner operations | Deal registration and payout governance | | **PartnerStack** | Enterprise partner ecosystems | Fees, attribution, approval, data export | --- ## Boundaries - Do not enable a program, create affiliate accounts, issue links, execute payouts, or change billing and commission objects without verified live state and explicit approval. - Do not invent attribution, conversion, fraud, or viral-coefficient results; distinguish modeled economics from observed data. - Do not decide tax, labor, privacy, contest, endorsement, or incentive compliance. Surface the jurisdiction-specific review needed before launch. ## Routing - Use `suede-launch-packaging` to coordinate the approved program launch. - Use `suede-emails` for referral invitation and nurture sequences. - Use `suede-marketing-psychology` to test incentive framing. - Use `suede-ab-testing` to design and evaluate the incentive, messaging, and placement tests above. - Use `suede-analytics` to define and read referral attribution.
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