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| Criterion |  Product Discovery Solution Interview |  Product Strategy ICE Scoring |  Decision Making Decision Tree |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When a solution idea is already on the table, it checks how it is understood and evaluated. It looks for resonance, objections, and missing fit early in the conversation. | 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. | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | 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 | 30-60 min je Interview | 30-60 min | 30-90 min | 1-4 Wochen |
Participantsdifferent | 5-10 Interviews | 2-8 | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Solution Feedback, Objection List, Validation Notes | ICE Table, Top Idea List | Decision Tree, Option Map, Assumption List | Experiment results, Decision log, Learning summary |
Tagsno overlap | ValidationInterviewsPrototype | PrioritizationScoringGrowthDecision | DecisionTreeOptions | ExperimentsGrowthAnalyticsValidation |