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| Criterion |  Decision Making Constraint Analysis |  Product Strategy ICE Scoring |  Growth Growth Experiment |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 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 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 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 | Low | Medium | High |
Timedifferent | 30-90 min | 30-60 min | 1-2 Wochen | 1-4 Wochen |
Participantsdifferent | 2-8 | 2-8 | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | ICE Table, Top Idea List | Experiment card, Result summary, Next bet | Experiment results, Decision log, Learning summary |
Tagsno overlap | ConstraintsDecisionPlanningOptions | PrioritizationScoringGrowthDecision | MarketingGrowthExperimentsLearning | ExperimentsGrowthAnalyticsValidation |