<!-- Source: Magnotia Master Brief — §9 Success Metrics -->

## 9. Success Metrics

### Business metrics

| Milestone | Target |
|---|---|
| Beta testers recruited | 10–15 |
| Beta feedback: "same complaints repeating" threshold | Signal to stop beta, ship v1.0 |
| Waitlist signups pre-launch | 100+ |
| First paid sale | Within 2 weeks of public launch |
| Revenue target (6 months) | £2K MRR (mix of lifetime + cloud subscriptions) |
| Revenue trigger to evaluate CORBEL roll-up | £500/month sustained |

### Neuro-inclusive product metrics

Standard SaaS metrics like Daily Active Users (DAU) or unbroken streaks must be avoided — they encourage the exact shame spiral Magnotia is designed to prevent. Track these instead:

| Metric | What it measures | Why it matters |
|---|---|---|
| **Time-to-capture** | Seconds from app open to completed brain dump | Measures friction. If this exceeds 10 seconds, the thought is gone. The lower this number, the better Magnotia serves its core purpose. |
| **Grace day recovery rate** | % of users who return and complete a task after 1+ days of inactivity | Proves Magnotia has beaten the abandon-shame cycle. This is the single most important product metric. If users come back after missing days without guilt, the design is working. |
| **Micro-step completion rate** | Completion rate of AI-decomposed tasks vs. manually entered abstract tasks | Validates that micro-stepping actually works. If AI-generated steps have higher completion rates than user-entered tasks, the feature is earning its keep. |
| **Brain dump → task conversion** | % of voice transcription content that converts into actionable tasks | Measures AI quality. Low conversion means the AI isn't parsing well; high conversion means the core loop works. |
| **Return after lapse** | Median days between last session and next session for users who go inactive | Measures stickiness without punishing breaks. A user who returns after 2 weeks is a success, not a failure. |
