Referral Program

Referral Program

Production-grade referral and affiliate program framework covering the 4-stage referral loop, incentive design methodology, trigger moment optimization, share mechanics, viral coefficient modeling, affiliate program architecture, and systematic optimization playbook. Designed to build programs that compound, not collect dust.

Core Capabilities

• Program type & loop design — referral vs affiliate decision, plus the 4-stage loop (trigger → share → convert → reward)
• Incentive design — single- vs double-sided, reward types, tiered gamification, reward economics against LTV/CAC
• Trigger & share mechanics — in-product and email trigger points, share channel priority, first-person share copy
• Referred-user experience — referral landing page, attribution rules, program copy set (prompts, emails, dashboards)
• Growth math — K-factor modeling, revenue impact models, and lever-by-lever K improvement
• Affiliate framework — commission models, tier systems, partner toolkit, recruitment
• Optimization — diagnose-before-optimize playbook, metric benchmarks, troubleshooting, and three Python tools

When to Use

• The user asks to "design a referral program", "launch an affiliate program", or "improve viral growth"
• The decision between customer referral vs affiliate program needs to be made
• An existing referral program has stalled (K-factor <1, low share rate, low referred-user conversion)
• Reward structure needs sizing against CAC, margin, or LTV
• Trigger moments need to be identified (when to ask, which in-product events, which lifecycle emails)
• The user says "word-of-mouth isn't working" or "we want to add a refer-a-friend flow"

Clarify First

Before designing the referral program, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Program type — customer referral vs affiliate (enthusiastic/social customers vs team buyers) (selects the entire framework)
• Trigger moment — the in-product or lifecycle point where you ask (a broken Stage 1 can't be fixed by a bigger reward at Stage 4)
• Reward economics — first-payment value, margin, and CAC (caps the reward at <30% of first payment)
• Current referral rate (if any) — decides single- vs double-sided incentive and which stage to fix first

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the deliverable.

Quick Start

1. Pick the program type — use the Referral vs Affiliate Decision table (enthusiastic/social customers → referral; team buyers → affiliate).
2. Build the loop in order — trigger → share → convert → reward; a broken Stage 1 can't be fixed by a bigger reward at Stage 4.
3. Size the incentive — cap reward at <30% of first payment; go double-sided if referral rate <1%.
4. Model and validate — run the scripts (referral_economics_calculator.py, referral_funnel_analyzer.py, affiliate_commission_modeler.py) to size rewards, find the weakest stage, and model affiliate tiers.