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| Criterion |  Product Discovery Smoke Test |  Growth Pirate Metrics AARRR |  Product Discovery Hypothesis Prioritization Canvas |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | For products with complex usage paths, overall growth alone is too coarse to reveal bottlenecks. Pirate Metrics breaks the relationship with the product into consecutive stages and shows where the funnel actually leaks. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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 | Low | Medium | Medium | High |
Timedifferent | 1-5 Tage | 1-2 h Setup, laufend | 60-90 min | 1-4 Wochen |
Participantsdifferent | Nutzertraffic | 2-8 | 3-8 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop | Async |
Outputdifferent | Interest Metrics, Conversion Signal, Learning Note | AARRR funnel, Metric baseline, Experiment backlog | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary |
Tags1 shared | ValidationExperimentsDemandGrowth | GrowthMetricsExperiments | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |