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| Criterion |  Growth Funnel Analysis |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
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. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 | Low | High |
Timedifferent | 1-3 h | 60-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-8 | Nutzertraffic | 1-6 |
Formatdifferent | Async | Workshop | Async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Prioritization Canvas, Hypothesis Backlog | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | ExperimentsPrioritizationDiscoveryHypothesis | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |