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| Criterion |  Growth Funnel Analysis |  Decision Making PERT Estimation |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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. | On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes. | 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 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 | Medium | High | Low |
Timedifferent | 1-3 h | 15-45 min | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-5 | 1-8 | 1-6 | 1-5 |
Formatdifferent | Async | Workshop + async | Async | Workshop + async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | PERT Estimate, Expected Value, Risk Notes | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | AnalyticsConversionGrowth | EstimationUncertaintyRisk | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |