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| Criterion |  Growth A/B Testing |  Product Strategy ICE Scoring |  Product Discovery Concierge MVP |  Product Discovery Hypothesis Prioritization 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 ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | When an idea can first fail or grow through genuine hands-on support, it relies on manual work instead of automation. It shows whether user value holds up even under manual execution. | 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. |
Complexitydifferent | High | Low | Medium | Medium |
Timedifferent | 1-4 Wochen | 30-60 min | 1-4 Wochen | 60-90 min |
Participantsdifferent | 1-6 | 2-8 | 3-10 Kunden | 3-8 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Experiment results, Decision log, Learning summary | ICE Table, Top Idea List | Concierge Learnings, Service Blueprint, MVP Risks | Prioritization Canvas, Hypothesis Backlog |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | PrioritizationScoringGrowthDecision | MVPValidationServiceDiscovery | ExperimentsPrioritizationDiscoveryHypothesis |