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| Criterion |  Operations Change Analysis |  Growth Funnel Analysis |  Operations Fault Tree Analysis |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | For a deviation after a change, the method isolates the influence of the altered condition. It narrows down which differences are relevant and which are just accompanying noise. | 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. | For a critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | 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 | High | High |
Timedifferent | 45-120 min | 1-3 h | 2-6 h | 1-4 Wochen |
Participantsdifferent | 2-6 | 1-5 | 3-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Funnel report, Drop-off analysis, Optimization hypotheses | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Experiment results, Decision log, Learning summary |
Tagsno overlap | ChangeRoot causeTroubleshootingComparison | AnalyticsConversionGrowth | RiskRoot causeSafety | ExperimentsGrowthAnalyticsValidation |