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Referral Program

skill-moizibnyousaf-marketing-cli-referral-program · by MoizIbnYousaf

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Install

$ agentstack add skill-moizibnyousaf-marketing-cli-referral-program

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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About

Referral Program Design

Purpose

Design a referral program that turns existing users into an acquisition channel. Define the incentive model, sharing mechanics, copy, and launch plan. Focus on programs that actually get used — not "refer a friend" links that collect dust.

Reads

  • brand/audience.md — Personas, what they value, their networks
  • brand/positioning.md — Value props, pricing context for incentive sizing
  • brand/voice-profile.md — Brand voice for all referral copy

Brand Integration

  • audience.md — Incentive type depends on audience. B2B audiences prefer account credits. B2C audiences prefer discounts or free months. Developer audiences prefer swag or extended trials.
  • positioning.md — Referral messaging reinforces the brand's positioning angle, not generic 'share with a friend' copy. If positioning is around simplicity, the referral CTA is 'Know someone drowning in complexity?'
  • voice-profile.md — All referral copy (dashboard prompts, share text, emails) should match the brand voice. A casual brand writes "Your friend's gonna love this" while a professional brand writes "Share a professional recommendation."

Workflow

Step 1: Choose Referral Model

Evaluate and recommend the right model:

One-Sided (Referrer only gets reward)

  • Best for: High-value products, enterprise, low-frequency purchases
  • Pros: Simple, lower cost per referral
  • Cons: Less motivation for referee to convert
  • Example: "Give your friend our link, you get $50 credit"

Two-Sided (Both get reward)

  • Best for: SaaS, marketplaces, subscription products
  • Pros: Higher conversion (referee has incentive), feels fair
  • Cons: Higher cost per referral
  • Example: "Give $20, get $20"

Tiered (Rewards increase with referral count)

  • Best for: Products with power users, community-driven products
  • Pros: Creates referral champions, gamification
  • Cons: Complex to communicate, top-heavy rewards
  • Example: "1 referral = 1 month free. 5 = premium forever."

Milestone (Unlock rewards at specific counts)

  • Best for: Products wanting viral moments
  • Pros: Creates sharing bursts, clear goals
  • Cons: Can feel like a grind
  • Example: "3 friends = swag. 10 friends = lifetime access."

Selection criteria:

  • Product price point and margins
  • Customer lifetime value (can you afford the incentive?)
  • Network effects (does the product get better with more users?)
  • Purchase frequency (one-time vs recurring)

Step 2: Define the Incentive

Incentive Types

| Type | Best When | Example | |------|-----------|---------| | Account credit | Recurring subscription | "$20 off next month" | | Extended trial | Freemium model | "+7 days of Pro" | | Feature unlock | Gated features exist | "Unlock advanced analytics" | | Cash/gift card | High LTV, B2B | "$50 Amazon card" | | Discount | E-commerce, one-time | "25% off next purchase" | | Donation | Mission-driven brand | "$10 to charity of choice" | | Exclusive access | Waitlist/beta | "Skip the waitlist" | | Physical swag | Community brand | "Free branded merch" |

Incentive Sizing Rules
  • Referrer reward should be 10-25% of first-year LTV
  • Referee reward should lower the barrier to first purchase
  • Two-sided: referee reward can be smaller than referrer reward
  • Always test: the "right" incentive is discovered, not guessed
  • Stack with existing promotions cautiously — don't train discount hunters

Step 3: Design Sharing Mechanics

Sharing Methods (Offer All, Optimize for Top 2)

Unique referral link (required, baseline)

  • Personal URL: product.com/ref/[username] or product.com/r/[code]
  • One-click copy button
  • Auto-populated share text

Referral code (supplement to link)

  • Short, memorable: 4-6 characters
  • Personalizable: user's name or custom code
  • Easy to share verbally

Email invite (high-intent channel)

  • Import contacts option (Google, Outlook)
  • Or manual email entry
  • Pre-written message, editable by user

Social sharing (volume channel)

  • Twitter/X: Pre-populated tweet with link
  • LinkedIn: Professional framing for B2B
  • WhatsApp/SMS: Direct message with link
  • Platform-native share sheet on mobile

In-product prompts (highest conversion trigger)

  • Post-success moment: "You just [achievement]! Share with a friend?"
  • Milestone celebration: "You've been using [Product] for 30 days!"
  • Team detection: "Looks like [colleague] could use this too"
Friction Reduction Checklist
  • [ ] One-click sharing (no extra steps)
  • [ ] Pre-written share text (editable)
  • [ ] Referral link visible in dashboard at all times
  • [ ] Mobile-optimized sharing flow
  • [ ] No login required for referee to see the offer
  • [ ] Referee landing page explains the incentive immediately

Step 4: Write Referral Copy

Referrer-Facing Copy

Dashboard prompt:

Give [incentive], Get [incentive]

Share [Product] with friends and you both get rewarded.
You've earned [X] so far from [Y] referrals.

[Copy Your Link]  [Invite by Email]

Post-achievement trigger:

Nice — you just [achievement]!

Know someone who'd love this too?
Share your link and you both get [incentive].

[Share Now]  [Maybe Later]

Email nudge:

Subject: You have $[X] in referral rewards waiting

You've been using [Product] for [X] weeks and
[specific achievement]. Know someone who'd benefit?

Share your personal link and you both get [incentive]:
[referral link]

[Share via Email]  [Copy Link]
Referee-Facing Copy

Referral landing page:

[Referrer name] invited you to [Product]

[Referrer] thinks you'd love [Product] — and they're
giving you [incentive] to try it.

[What Product Does — one sentence]

[Claim Your [Incentive]]

[Social proof: X users, rating, testimonial]

Referee welcome email:

Subject: [Referrer] gifted you [incentive] for [Product]

Welcome! [Referrer name] shared [Product] with you
and you've got [incentive] waiting.

Here's how to get started:
1. [Quick start step]
2. [Quick start step]
3. [Quick start step]

[Get Started with [Incentive]]

Step 5: Plan Launch Strategy

Pre-Launch (1 week before)
  • Seed with top 10% most active users first
  • Personal email from founder: "You're getting early access to our referral program"
  • Goal: Get first 50 referrals to validate mechanics
Launch (Week 1)
  • In-app announcement banner for all users
  • Email blast to full user base
  • Social media announcement
  • Add referral link to user dashboard permanently
Sustained Growth (Ongoing)
  • Trigger-based prompts (post-achievement, milestone, NPS 9-10)
  • Monthly referral leaderboard (optional, for tiered/milestone)
  • Seasonal boost: "Double rewards this month"
  • New user onboarding: mention referral program during setup

Viral Coefficient Calculation

Viral Coefficient (K) = i x c

i = invitations sent per user
c = conversion rate of invitations

K > 1 = viral growth (each user brings >1 new user)
K = 0.5 = healthy referral supplement
K 0.3 |
| Referral CAC | Program cost / Referred customers | 5% of active users sharing) and shares per user (target: >2). If below, the prompt timing or incentive needs adjustment.
- **Month 1:** Evaluate invite conversion rate (target: >10%) and K-factor (target: >0.3). If conversion is low, the referee landing page or incentive needs work.
- **Month 3:** Full program review — referral CAC vs. paid CAC, referred customer LTV vs. average LTV, fraud rate. Decide whether to scale, adjust incentives, or restructure the model.

## Output

Each referral program design produces:
- Recommended model with justification
- Incentive structure (type, size, trigger conditions)
- Sharing mechanics spec (channels, copy, UX flow)
- Referrer and referee copy for all touchpoints
- Launch plan (pre-launch, launch, sustained)
- Metrics dashboard spec
- Viral coefficient projection

## Quality Checks

- [ ] Incentive is ROI-positive (reward < LTV margin)
- [ ] Sharing flow is 2 clicks or fewer
- [ ] Copy provided for both referrer and referee touchpoints
- [ ] Anti-fraud measures defined (at minimum: email verification, delayed payout)
- [ ] Referee landing page has clear incentive + product value + CTA
- [ ] Referral link is visible in user dashboard, not buried in settings
- [ ] Copy matches brand voice (if voice-profile.md exists)
- [ ] Launch plan includes measurement milestones with specific targets

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [MoizIbnYousaf](https://github.com/MoizIbnYousaf)
- **Source:** [MoizIbnYousaf/marketing-cli](https://github.com/MoizIbnYousaf/marketing-cli)
- **License:** MIT
- **Homepage:** https://www.marketing-cli.com/

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.