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Identifies root causes behind declining user retention with likely causes, supporting evidence, and fix experiments. Use when you observe a retention drop and need to systematically diagnose why users are leaving.
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SOC 職業分類に基づく
Exposes hidden risks by identifying and ranking assumptions across desirability, feasibility, and viability categories. Use when evaluating new products or features to surface the highest-risk assumption to test first.
Designs fast, reliable validation experiments with hypothesis, method, success metric, and decision rules (Ship/Iterate/Kill). Use when you need to validate an assumption or test a product hypothesis before committing resources.
Analyzes why a shipped feature is or isn't being used, covering adoption metrics, barriers, user feedback signals, and recommended actions. Use after launching a feature to diagnose adoption issues.
Translates feature ideas into Jobs-to-Be-Done format with functional and emotional jobs, success criteria, and switching triggers. Use when reframing feature requests to understand the underlying user motivation.
Audits whether a feature or product is truly ready for launch with a structured checklist and readiness status (Ready/At Risk/Not Ready). Use before any product launch to catch critical gaps.
Shifts thinking from feature delivery to measurable user or business outcomes. Use before building a feature, during roadmap planning, or while defining success metrics to ensure work ties to real results.
| name | retention-drop-diagnoser |
| description | Identifies root causes behind declining user retention with likely causes, supporting evidence, and fix experiments. Use when you observe a retention drop and need to systematically diagnose why users are leaving. |
Identify root causes behind declining user retention.
See worked example for a complete scenario.
