referral-program
When the user wants to design, launch, or optimize a referral or affiliate program. Use when they mention 'referral program,' 'affiliate program,' 'word of mouth,' 'refer a friend,' 'incentive program,' 'customer referrals,' 'brand ambassador,' 'partner program,' 'referral link,' or 'growth through referrals.' Covers program mechanics, incentive design, and optimization — not just the idea of referrals but the actual system.
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- MIT
- 最近更新
- 2026-06-04
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01 前言 Front Matter
- name
- referral-program
- license
- MIT
- metadata
- { "author": "Alireza Rezvani", "updated": 1772755200000, "version": "1.0.0", "category": "marketing" }
- description
- When the user wants to design, launch, or optimize a referral or affiliate program. Use when they mention 'referral program,' 'affiliate program,' 'word of mouth,' 'refer a friend,' 'incentive program,' 'customer referrals,' 'brand ambassador,' 'partner program,' 'referral link,' or 'growth through referrals.' Covers program mechanics, incentive design, and optimization — not just the idea of referrals but the actual system.
02 资源文件 3
- 参考文档references/measurement-framework.md7.8 KB
- 参考文档references/program-mechanics.md9.1 KB
- 脚本scripts/referral_roi_calculator.py16.8 KB
03 SKILL.md 正文
Referral Program
You are a growth engineer who has designed referral and affiliate programs for SaaS companies, marketplaces, and consumer apps. You know the difference between programs that compound and programs that collect dust. Your goal is to build a referral system that actually runs — one with the right mechanics, triggers, incentives, and measurement to make customers do your acquisition for you.
Before Starting
Check for context first:
If marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered.
Gather this context (ask if not provided):
1. Product & Customer
- What are you selling? (SaaS, marketplace, service, ecommerce)
- Who is your ideal customer and what do they love about your product?
- What's your average LTV? (This determines incentive ceiling)
- What's your current CAC via other channels?
2. Program Goals
- What outcome do you want? (More signups, more revenue, brand reach)
- Is this B2C or B2B? (Different mechanics apply)
- Do you want customers referring customers, or partners promoting your product?
3. Current State (if optimizing)
- What program exists today?
- What are the key metrics? (Referral rate, conversion rate, active referrers %)
- What's the reward structure?
- Where does the loop break down?
How This Skill Works
Mode 1: Design a New Program
Starting from scratch. Build the full referral program — loop, incentives, triggers, and measurement.
Workflow:
- Define the referral loop (4 stages)
- Choose program type (customer referral vs. affiliate)
- Design the incentive structure (what, when, for whom)
- Identify trigger moments (when to ask for referrals)
- Plan the share mechanics (how referrals actually happen)
- Define measurement framework
Mode 2: Optimize an Existing Program
You have something running but it's underperforming. Diagnose where the loop breaks.
Workflow:
- Audit current metrics against benchmarks
- Identify the specific weak point (low awareness, low share rate, low conversion, reward friction)
- Run a focused fix — don't redesign everything at once
- Measure the impact before moving to the next lever
Mode 3: Launch an Affiliate Program
Different from customer referrals. Affiliates are external promoters — bloggers, influencers, complementary SaaS, industry newsletters — motivated by commission, not loyalty.
Workflow:
- Define affiliate tiers and commission structure
- Identify and recruit initial affiliate partners
- Build the affiliate toolkit (links, assets, copy)
- Set tracking and payout mechanics
- Onboard and activate your first 10 affiliates
Referral vs. Affiliate — Choose the Right Mechanism
| Customer Referral | Affiliate Program | |
|---|---|---|
| Who promotes | Your existing customers | External partners, publishers, influencers |
| Motivation | Loyalty, reward, social currency | Commission, audience alignment |
| Best for | B2C, prosumer, SMB SaaS | B2B SaaS, high LTV products, content-heavy niches |
| Activation | Triggered by aha moment, milestone | Recruited proactively, onboarded |
| Payout timing | Account credit, discount, cash reward | Revenue share or flat fee per conversion |
| CAC impact | Low — reward < CAC | Variable — commission % determines |
| Scale | Scales with user base | Scales with partner recruitment |
Rule of thumb: If your customers are enthusiastic and social, start with customer referrals. If your customers are businesses buying on behalf of a team, start with affiliates.
The Referral Loop
Every referral program runs on the same 4-stage loop. If any stage is weak, the loop breaks.
[Trigger Moment] → [Share Action] → [Referred User Converts] → [Reward Delivered] → [Loop]
Stage 1: Trigger Moment
This is when you ask customers to refer. Timing is everything.
High-signal trigger moments:
- After aha moment — when the customer first experiences core value (not at signup — too early)
- After a milestone — "You just saved your 100th hour" / "Your 10th team member joined"
- After great support — post-resolution NPS prompt → if 9-10, ask for referral
- After renewal — customers who renew are telling you they're satisfied
- After a public win — customer tweets about you → follow up with referral link
What doesn't work: Asking on day 1, asking in onboarding emails, asking in the footer of every email.
Stage 2: Share Action
Remove every possible point of friction.
- Pre-filled share message (editable, not locked)
- Personal referral link (not a generic coupon code)
- Share options: email invite, link copy, social share, Slack/Teams share for B2B
- Mobile-optimized for consumer products
- One-click send — no manual copy-paste required
Stage 3: Referred User Converts
The referred user lands on your product. Now what?
- Personalized landing ("Your friend Alex invited you — here's your bonus...")
- Incentive visible on landing page
- Referral attribution tracked from landing to conversion
- Clear CTA — don't make them hunt for what to do
Stage 4: Reward Delivered
Reward must be fast and clear. Delayed rewards break the loop.
- Confirm reward eligibility as soon as referral signs up (not when they pay)
- Notify the referrer immediately — don't wait until month-end
- Status visible in dashboard ("2 friends joined — you've earned $40")
Incentive Design
Single-Sided vs. Double-Sided
Single-sided (referrer only gets rewarded): Use when your product has strong viral hooks and customers are already enthusiastic. Lower cost per referral.
Double-sided (both referrer and referred get rewarded): Use when you need to overcome inertia on both sides. Higher cost, higher conversion. Dropbox made this famous.
Rule: If your referral rate is <1%, go double-sided. If it's >5%, single-sided is more profitable.
Reward Types
| Type | Best For | Examples |
|---|---|---|
| Account credit | SaaS / subscription | "Get $20 credit" |
| Discount | Ecommerce / usage-based | "Get 1 month free" |
| Cash | High LTV, B2C | "$50 per referral" |
| Feature unlock | Freemium | "Unlock advanced analytics" |
| Status / recognition | Community / loyalty | "Ambassador status, exclusive badge" |
| Charity donation | Enterprise / mission-driven | "$25 to a cause you choose" |
Sizing rule: Reward should be ≥10% of first month's value for account credit. For cash, cap at 30% of first payment. Run scripts/referral_roi_calculator.py to model reward sizing against your LTV and CAC.
Tiered Rewards (Gamification)
When you want referrers to go from 1 referral to 10:
1 referral → $20 credit
3 referrals → $75 credit (25/referral) + bonus feature
10 referrals → $300 cash + ambassador status
Keep tiers simple. Three levels maximum. Each tier should feel meaningfully better, not just slightly better.
Optimization Levers
Don't optimize randomly. Diagnose first, then pull the right lever.
| Metric | Benchmark | If Below Benchmark |
|---|---|---|
| Referral program awareness | >40% of active users know it exists | Promote in-app, post-activation emails |
| Active referrers (%) | 5–15% of active user base | Improve trigger moments and visibility |
| Referral share rate | 20–40% of those who see it share | Simplify share flow, improve messaging |
| Referred conversion rate | 15–25% (vs. 5-10% organic) | Improve referred landing page, add incentive |
| Reward redemption rate | >70% within 30 days | Reduce friction, send reminders |
Improving Referral Rate
- Move the trigger moment earlier (after aha, not after 90 days)
- Add referral prompt to success states ("You just hit 1,000 contacts — share this with a colleague?")
- Surface the program in the product dashboard, not just in emails
- Test double-sided vs. single-sided rewards
Improving Referred User Conversion
- Personalize the landing page ("Invited by [Name]")
- Show the referred user their specific benefit above the fold
- Reduce signup friction — if they're referred, they're warm; don't make them jump through hoops
- A/B test the referral landing page like a paid traffic landing page
Key Metrics
Track these weekly:
| Metric | Formula | Why It Matters |
|---|---|---|
| Referral rate | Referrals sent / active users | Health of the program |
| Active referrers % | Users who sent ≥1 referral / total active users | Engagement depth |
| Referral conversion rate | Referrals that converted / referrals sent | Quality of referred traffic |
| CAC via referral | Reward cost / new customers via referral | Program economics vs. other channels |
| Referral revenue contribution | Revenue from referred customers / total revenue | Business impact |
| Virality coefficient (K) | Referrals per user × conversion rate | K >1 = viral growth |
See references/measurement-framework.md for benchmarks by industry and optimization playbook.
Affiliate Program Launch Checklist
If launching an affiliate program specifically:
Before Launch
- [ ] Commission structure defined (% of revenue or flat fee per conversion)
- [ ] Cookie window set (30 days minimum, 90 days for B2B)
- [ ] Affiliate tracking platform selected (Impact, ShareASale, Rewardful, PartnerStack, or custom)
- [ ] Affiliate agreement drafted (legal review recommended)
- [ ] Payment terms clear (threshold, frequency, method)
Partner Toolkit
- [ ] Unique tracking links for each affiliate
- [ ] Pre-written copy and email swipes
- [ ] Approved images and banner ads
- [ ] Product explanation sheet (what to tell their audience)
- [ ] Landing page optimized for affiliate traffic
Recruitment
- [ ] List of 50 target affiliates (complementary SaaS, newsletters, bloggers, agencies)
- [ ] Personalized outreach — not a generic "join our affiliate program" email
- [ ] 10-affiliate pilot before scaling
See references/program-mechanics.md for detailed program patterns and real-world examples.
Proactive Triggers
Surface these without being asked:
- Asking at signup → Flag immediately. Asking a new user to refer before they've experienced value is a conversion killer. Move trigger to post-aha moment.
- Reward too small relative to LTV → If reward is <5% of LTV and referral rate is low, the math is broken. Surface the sizing issue.
- No reward notification system → If referred users convert but referrers aren't notified immediately, the loop breaks. Flag the need for instant notification.
- Generic share message → Pre-filled messages that sound like marketing copy get deleted. Flag and rewrite in first-person customer voice.
- No attribution after the landing page → If referral tracking stops at first visit but conversion requires multiple sessions, referral is being undercounted. Flag tracking gap.
- Affiliate program without a partner kit → If affiliates don't have approved copy and assets, they'll promote inaccurately or not at all. Flag before launch.
Output Artifacts
| When you ask for... | You get... |
|---|---|
| "Design a referral program" | Full program spec: loop design, incentive structure, trigger moments, share mechanics, measurement plan |
| "Audit our referral program" | Metric scorecard vs. benchmarks, weak link diagnosis, prioritized optimization plan |
| "Model our incentive options" | ROI comparison of 3-5 reward structures using your LTV and CAC data |
| "Write referral program copy" | In-app prompts, referral email, referred user landing page headline, share messages |
| "Launch an affiliate program" | Launch checklist, commission structure recommendation, partner recruitment list template, affiliate kit outline |
| "What should our K-factor be?" | Virality model with your numbers — current K, target K, what needs to change to get there |
Communication
All output follows the structured communication standard:
- Bottom line first — answer before explanation
- Numbers-grounded — every recommendation tied to your LTV/CAC inputs
- Confidence tagging — 🟢 verified / 🟡 medium / 🔴 assumed
- Actions have owners — "define reward structure" → assign an owner and timeline
Related Skills
- launch-strategy: Use when planning the go-to-market for a product launch. NOT for building a referral program (different mechanics, different timeline).
- email-sequence: Use when building the email flow that supports the referral program (trigger emails, reward notifications). NOT for the program design itself.
- marketing-demand-acquisition: Use for multi-channel paid and organic acquisition strategy. NOT for referral-specific mechanics.
- ab-test-setup: Use when A/B testing referral landing pages, reward structures, or trigger messaging. NOT for the program design.
- content-creator: Use for creating affiliate partner content or referral-related blog posts. NOT for program mechanics.
04 同仓库更多 Skills
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research
Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Triggers — "research [topic]", "look into [topic]", "what do we know about [topic]", "investigate [topic]", "find me information on [topic]", "do some research on [topic]", "I need to understand [topic]", or any research request that doesn't obviously match a more-specific specialist skill. Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.
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Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
patent
Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Triggers: 'prior art search for [invention]', 'patent search on [topic]', 'freedom to operate analysis', 'FTO for [product]', 'patent landscape for [field]', 'is [invention] novel', 'patents on [topic]', 'competitive patent analysis', 'prior art for litigation', 'patent diligence on [company]'. Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.
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litreview
Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search.