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| Criterion |  Growth Funnel Analysis |  Growth A/B Testing |  Agile Bucket System |  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 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 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 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 | High | Medium | Low |
Timedifferent | 1-3 h | 1-4 Wochen | 30-90 min | 30-60 min |
Participantsdifferent | 1-5 | 1-6 | 3-12 | 1-5 |
Formatdifferent | Async | Async | Workshop | Workshop + async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary | Bucketed Backlog, Relative Estimates, Split Candidates | Completed Experiment Canvas, Success Metric |
Tagsno overlap | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation | EstimationBacklogRelative sizing | ExperimentsValidationDiscoveryHypothesis |