referral-program-design

What problem does it solve?

It prevents referral programs from becoming discount-driven customer cannibalization by designing rewards, eligibility, fraud controls, and measurement so referrals remain incremental.

Core Features & Use Cases

• Referral baseline + unit-economics framing: Uses channel attribution, cohort LTV (referred vs non-referred), repeat-rate inputs, and existing referral-flow performance to set an evidence-backed starting point.
• Program structure design: Chooses double-sided vs one-sided reward math, defines reward shape (cash vs credit vs product vs percentage), and sets eligibility windows (order count, time, product usage, and optional NPS gates).
• Fraud-resistant governance: Plans for self-referral, address stuffing, credit stacking/refund clawback, code leakage, and internal abuse, then recommends ongoing quarterly audits and recalibration.
• Operational trigger placement: Recommends when to ask (post-second-order, post-validation window day 7–21, and CS-resolved-positive events) and avoids asking too early.
• Expected outcomes guidance: Estimates participation, expected viral coefficient bands (typical DTC K range), and how to prioritize conversion of referrals over participation and referrals-per-referrer.

Quick Start

Ask the AI to design a double-sided referral program with fraud controls and recommended trigger placements for your store, using your last-12-month referral attribution, referred vs non-referred cohort LTV, and repeat-rate data.