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| Criterion |  Growth Funnel Analysis |  Product Discovery Smoke Test |  Product Discovery Concierge MVP |  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 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 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. |
Complexitydifferent | Medium | Low | Medium | High |
Timedifferent | 1-3 h | 1-5 Tage | 1-4 Wochen | 1-4 Wochen |
Participantsdifferent | 1-5 | Nutzertraffic | 3-10 Kunden | 1-6 |
Formatdifferent | Async | Async | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note | Concierge Learnings, Service Blueprint, MVP Risks | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth | MVPValidationServiceDiscovery | ExperimentsGrowthAnalyticsValidation |