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| Criterion |  Product Strategy RICE Scoring |  Product Discovery Smoke Test |  Product Discovery Hypothesis Prioritization Canvas |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When many initiatives compete for the same resources, it brings reach, impact, confidence, and effort into one shared ranking. It makes prioritization economically connectable. | 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 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 | Medium | Low | Medium | High |
Timedifferent | 60-90 min | 1-5 Tage | 60-90 min | 1-4 Wochen |
Participantsdifferent | 2-10 | Nutzertraffic | 3-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | RICE Scores, Ranked List, Assumption Log | Interest Metrics, Conversion Signal, Learning Note | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary |
Tagsno overlap | PrioritizationScoringRoadmapTradeoffs | ValidationExperimentsDemandGrowth | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |