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| Criterion |  Growth Funnel Analysis |  Product Strategy Lean Canvas |  Product Discovery Fake Door 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 an early product bet still has too many open points, it brings target group, problem, and assumptions into a single view. It condenses the idea so core risks become nameable. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | 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 | 45-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-5 | 1-6 | Nutzertraffic | 1-6 |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Lean Canvas, Core Assumptions, Experiment Backlog | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | StartupLeanAssumptionsBusiness model | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation |