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| Criterion |  Growth A/B Testing |  Decision Making Constraint Analysis |  Growth Growth Experiment |  Product Discovery Smoke Test |
|---|---|---|---|---|
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 an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | When growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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. |
Complexitydifferent | High | Low | Medium | Low |
Timedifferent | 1-4 Wochen | 30-90 min | 1-2 Wochen | 1-5 Tage |
Participantsdifferent | 1-6 | 2-8 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Experiment card, Result summary, Next bet | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ConstraintsDecisionPlanningOptions | MarketingGrowthExperimentsLearning | ValidationExperimentsDemandGrowth |