89c63891fa
Replace all instances of the legacy product names "Kon" and "Corbie" with "Magnotia" across user-facing copy, code identifiers, package names, bundle ids, file paths, and documentation. Preserves the unrelated "konsole" (KDE terminal) reference and the parent CORBEL company name. - Renames 10 Rust crates (kon-* → magnotia-*) and the tauri binary - Updates package.json, tauri.conf.json (productName + identifier) - Renames CSS classes (kon-rh-* → magnotia-rh-*) and animations - Renames brand and roadmap docs - Regenerates Cargo.lock and package-lock.json Verified: svelte-check passes; pure-rust crates compile under new names.
27 lines
2.0 KiB
Markdown
27 lines
2.0 KiB
Markdown
<!-- Source: Magnotia Master Brief — §9 Success Metrics -->
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## 9. Success Metrics
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### Business metrics
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| Milestone | Target |
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|---|---|
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| Beta testers recruited | 10–15 |
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| Beta feedback: "same complaints repeating" threshold | Signal to stop beta, ship v1.0 |
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| Waitlist signups pre-launch | 100+ |
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| First paid sale | Within 2 weeks of public launch |
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| Revenue target (6 months) | £2K MRR (mix of lifetime + cloud subscriptions) |
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| Revenue trigger to evaluate CORBEL roll-up | £500/month sustained |
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### Neuro-inclusive product metrics
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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:
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| Metric | What it measures | Why it matters |
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|---|---|---|
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| **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. |
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| **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. |
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| **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. |
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| **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. |
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| **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. |
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