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| Criterion | ![]() Product Discovery Concierge MVP | ![]() Growth A/B Testing | ![]() Product Discovery MVP Test Matrix | ![]() Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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 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 several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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 | 1-4 Wochen | 1-4 Wochen | 45-75 min | 30-60 min |
Participantsdifferent | 3-10 Kunden | 1-6 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop + async |
Outputdifferent | Concierge Learnings, Service Blueprint, MVP Risks | Experiment results, Decision log, Learning summary | Test Matrix, Test Plan | Completed Experiment Canvas, Success Metric |
Tags1 shared | MVPValidationServiceDiscovery | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryOptions | ExperimentsValidationDiscoveryHypothesis |



