feature-prioritizationlisted

Prioritize with math, not opinions. RICE scoring forces explicit tradeoffs. The enabler/blocker lens from Linear ensures you're not just building fun things while adoption barriers remain. ## RICE Scoring Score every candidate feature on four dimensions: - **Reach:** How many users/accounts will this affect in a set time period? Use real numbers from analytics, not gut feel. "500 users/quarter" not "a lot." - **Impact:** How much will this move the target metric per user? Score 0.25 (minimal), 0.5 (low), 1 (medium), 2 (high), 3 (massive). Be honest — most features are a 1. - **Confidence:** How sure are you about Reach and Impact? 100% = hard data. 80% = strong evidence. 50% = gut feel. NEVER score 100% without quantitative data. - **Effort:** Person-weeks of work. Include design, engineering, QA, and any cross-team coordination. Round up. **RICE = (Reach x Impact x Confidence) / Effort** Example: SSO — Reach: 500 users/qtr, Impact: 2 (high — unlocks enterprise deals), Confidence: 80%, Effort: 4 person-weeks. RICE = (500 x 2 x 0.8) / 4 = **200**. Tag: Blocker. Rank by score. The math won't be perfect, but it forces you to justify each dimension. ## Enablers vs Blockers (Linear) After RICE scoring, classify each feature: - **Blocker:** Removes a barrier to adoption or retention. Users are churning, stuck, or can't even start because this is missing. Examples: missing SSO for enterprise deals, broken mobile experience, no data export. - **Enabler:** Delights existing u