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| Criterion |  Growth Funnel Analysis |  Product Discovery Concierge MVP |  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. | 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 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 | 1-4 Wochen | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-5 | 3-10 Kunden | 1-6 | 1-5 |
Formatdifferent | Async | Workshop + async | Async | Workshop + async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Concierge Learnings, Service Blueprint, MVP Risks | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | AnalyticsConversionGrowth | MVPValidationServiceDiscovery | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |