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| Criterion |  Growth Funnel Analysis |  Product Discovery Smoke Test |  Agile Bucket System |  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 a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates. | 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 | 30-90 min | 1-4 Wochen |
Participantsdifferent | 1-5 | Nutzertraffic | 3-12 | 1-6 |
Formatdifferent | Async | Async | Workshop | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note | Bucketed Backlog, Relative Estimates, Split Candidates | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth | EstimationBacklogRelative sizing | ExperimentsGrowthAnalyticsValidation |