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| Criterion | ![]() Engineering Failure Scenario Analysis | ![]() Growth Funnel Analysis | ![]() Product Discovery Fake Door Test | ![]() Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | In critical processes, it is not enough to plan only for the normal case. Failure Scenario Analysis looks at the path into failure and shows which failures, chains, and control gaps cause the most damage. | When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | 1-3 h | 1-3 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 3-8 | 1-5 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop | Async | Async | Async |
Outputdifferent | Failure Scenarios, Risk Notes, Control Gaps, Test and Response Actions | Funnel report, Drop-off analysis, Optimization hypotheses | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary |
Tagsno overlap | FailureResilienceRisk | AnalyticsConversionGrowth | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation |



