This skill transforms Claude into an e-commerce referral strategist, providing a data-driven framework to design, audit, or relaunch referral and ambassador programs. It goes beyond simple 'give X, get Y' math to analyze viral coefficients, referrer LTV uplift, and fraud resistance, ensuring programs drive incremental customers rather than just discounting organic ones. By leveraging specific data points like cohort LTV and repeat rates, it helps operators optimize reward shapes, eligibility windows, and trigger placements to maximize viral growth and brand loyalty.
主要功能
01Determines optimal reward shapes (store credit, product, or cash) based on margin and brand goals.
02Analyzes cohort LTV to compare referred vs. paid-acquisition customer quality.
033 GitHub stars
04Calculates viral coefficients (K) and referrer LTV uplift metrics.
05Optimizes post-purchase trigger placements to maximize customer participation rates.
06Designs multi-layered eligibility gates and robust fraud-resistance systems.
使用场景
01Designing a new ambassador program for a scaling DTC brand with over 1,000 active buyers.
02Calculating the viral coefficient to determine if a referral program can offset paid acquisition CAC.
03Auditing an existing referral program to reduce fraud and improve unit economics.