suede-referrals

Suede AI Referral Skills

Compatibility

Uses supplied margin, LTV, and cohort data; current platform and disclosure checks need web access. Program design does not require a live payout or affiliate integration.

For a concrete input, expected handoff, and failure check, read the worked handoff.

Suede AI 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?

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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

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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 program patterns and incentive sizing: See references/program-examples.md

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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.

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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

Peer-reviewed field research (for example "Referral Programs and Customer Value," Journal of Marketing, 2011, a study of bank customers) found referred customers more valuable and less likely to churn than other customers; vendor reports claim a wide range of larger effects with undisclosed methods. Treat any figure as a hypothesis: compare referred and non-referred cohorts on retention, LTV, and onward referral rate in this product's own data before claiming an effect.

Disclosure and incentive rules

• Referrers and affiliates who are paid or rewarded must clearly disclose that connection where they recommend you (FTC Endorsement Guides, revised 2023: FTC guidance).
• Do not reward reviews conditioned on positive sentiment, and do not suppress negative ones (FTC Consumer Reviews and Testimonials Rule, effective October 21, 2024).
• Contests and milestone giveaways can fall under sweepstakes law; payouts can create tax reporting duties. Flag both for the user's counsel and finance owner.

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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

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Affiliate Programs

For affiliate program design, commission structures, recruitment, fraud prevention, and tools: See references/affiliate-programs.md

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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

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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-offers (behavioral-models catalog) to frame incentive tests as evidence-tiered hypotheses.
• Use suede-ab-testing to design and evaluate the incentive, messaging, and placement tests above.
• Use suede-analytics to define and read referral attribution.