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| Criterion |  Growth A/B Testing |  Growth Funnel Analysis |  Delivery Cost of Delay |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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 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. | Helps clarify scope, order, and delivery flow in concrete terms. It makes work, constraints, and sequence manageable. The result is captured as a Cost of Delay table and prioritization sequence. | 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 | High | Medium | High | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 90-180 min | 30-60 min |
Participantsdifferent | 1-6 | 1-5 | 3-8 | 1-5 |
Formatdifferent | Async | Async | Workshop | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | CoD Table, Prioritization Sequence | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | PrioritizationDeliveryEconomicsDecision | ExperimentsValidationDiscoveryHypothesis |