RC Moat Analysis — testable defensibility claims and pricing strategy

Deepen the moat analysis started in the 2026-03-05 competitive landscape rewrite. Key threads to resolve: **Context:** - [[sessions/2026-03-05-0941-competitive-landscape-rewrite]] — replaced aspirational positioning with testable win/lose criteria - [[context/rc-context/competitive.md]] — full competitive landscape (updated 2026-03-05) - [[voice/2026-03-05]] 11:19 — pricing grounding ("$200 might be a better reflection for our target market") - [[projects/moments-team/research/2026-02-25-traditional-loyalty-brief]] — moat question re Smile.io **Open strategic questions:** 1. Should RC move toward flat pricing? At what point does the success fee cost more in churn than it generates? 2. Why no free tier — deliberate or just not built? 3. Win/loss tracking gap — when merchants churn, where do they go? Systematic data would transform competitive strategy. 4. Is the real moat reward type library + AI onboarding + Shopify depth, or something else? **Key insight from rewrite:** "Brand-First Philosophy" isn't a moat — concrete product capabilities are. Need to keep testing this against actual win/loss data.