---
title: Referral Program
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/referral-program/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/referral-program/
last_updated: 2026-06-20
---

# Referral Program

> Create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy.

A complete playbook for turning happy customers into your cheapest acquisition channel through referral and affiliate programs. It covers program design, incentive sizing, fraud prevention, and viral-loop optimization, backed by a viral-coefficient model that tells you whether your program drives compounding growth or simply supplements other channels. From trigger moment to reward fulfillment, every step is measured and tuned.

## Use cases
- Designing a customer referral or affiliate program from scratch
- Choosing between single-sided, double-sided, and tiered incentive structures
- Sizing the maximum reward you can afford from LTV, margin, and target CAC
- Preventing self-referrals, referral rings, and reward farming
- Picking trigger moments at peak customer satisfaction for higher share rates
- Modeling viral coefficient (k-factor) and optimizing the weakest loop step

## Benefits
- Lowers acquisition cost by activating word of mouth as a tracked channel
- Protects the reward budget with layered fraud detection and qualifying actions
- Maximizes conversion with double-sided incentives and well-timed prompts
- Turns referral performance into a measurable funnel you can A/B test and improve

## What’s included
- Referral vs. affiliate decision guide with B2B/B2C and ticket-size fit
- Incentive-sizing formula plus a comparison of six reward types
- Affiliate commission structures, cookie durations, and recruitment templates
- Fraud prevention across technical, policy, and structural layers
- Launch and 30-day post-launch checklists with dashboard metrics
- Email sequences for program launch, nurture, and past-referrer re-engagement

## Who it’s for
Growth and marketing teams that want to build a referral or affiliate engine that compounds acquisition without bleeding budget to abuse.

## How it runs
Why do most referral programs quietly die? Wrong incentive, wrong moment, no fraud plan. This one starts from LTV math, asks at genuine peak moments, and instruments every leg of the loop.
1. Establishes the economics first: customer LTV, current CAC from other channels, and whether the product is naturally shareable. Then decides referral (customers, small rewards, high trust) versus affiliate (creators, commissions, more management) or a hybrid of both.
2. Identifies the trigger moments where asking actually works: right after the first aha moment, after a milestone, after a glowing support interaction or a high NPS score. A referral prompt in mid-onboarding is rejected as irritation.
3. Sizes the incentive with the formula: maximum reward equals LTV times gross margin minus target CAC. Defaults to double-sided rewards (the Dropbox and Uber pattern) because the new user's incentive is the engine of the loop.
4. Builds fraud prevention into the design, not after launch: rewards pay out only after the referred user completes a qualifying action, device and IP fingerprinting catches self-referrals, daily caps and anomaly flags catch farming.
5. Instruments the full loop and computes the k-factor: invites sent times acceptance rate times activation rate. Each leg gets its own fix: one-click sharing for volume, personal messaging for acceptance, a tailored welcome for activation. The weakest link gets the work, because a 10 percent gain there compounds across the whole loop.
6. Runs the launch checklist (landing page, email templates, tracking, terms, fraud rules, full flow test) and then tracks referred customers as their own cohort, since they typically show higher LTV, lower churn and refer onward at a higher rate.

## FAQ
### Does this fit a B2B service business, or only consumer apps?
The referral-vs-affiliate decision guide explicitly covers B2B/B2C and ticket-size fit, and incentive sizing is computed from your own LTV, margin, and target CAC. The structures change with the model; the method does not.

### How does it know what reward size I can actually afford?
An incentive-sizing formula derives the maximum reward from LTV, margin, and target CAC, then compares six reward types against it. The viral-coefficient (k-factor) model separately tells you whether the program compounds growth or merely supplements other channels.

### Will it run the program for me: tracking links, payouts, dashboards?
No. It is the design playbook: incentive structures, layered fraud prevention, launch checklists, and email sequences. The tracking platform and payout infrastructure are separate tooling you pick and operate.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [AI Google Ads and Meta Ads management](https://forgehouse.ai/guides/ai-google-ads-management/)