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| Criterion |  Operations Change Analysis |  Growth A/B Testing |  Growth Funnel Analysis |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | High | Medium | Low |
Timedifferent | 45-120 min | 1-4 Wochen | 1-3 h | 30-60 min |
Participantsdifferent | 2-6 | 1-6 | 1-5 | 1-5 |
Formatdifferent | Workshop + async | Async | Async | Workshop + async |
Outputdifferent | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ChangeRoot causeTroubleshootingComparison | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | ExperimentsValidationDiscoveryHypothesis |