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| Criterion |  Growth Funnel Analysis |  Product Discovery Concierge MVP |  Product Discovery Hypothesis Prioritization Canvas |  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 an idea can first fail or grow through genuine hands-on support, it relies on manual work instead of automation. It shows whether user value holds up even under manual execution. | 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 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 | 1-4 Wochen | 60-90 min | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-10 Kunden | 3-8 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Concierge Learnings, Service Blueprint, MVP Risks | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | MVPValidationServiceDiscovery | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |