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| Criterion |  Architecture C4 Model |  Product Discovery Smoke Test |  Product Strategy ICE Scoring |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | The C4 Model makes a system legible across several resolution levels, from context down to code. It suits situations where different audiences need to understand the same architecture from different altitudes. | 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. | 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 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 | Low | High |
Timedifferent | 1-4 h | 1-5 Tage | 30-60 min | 1-4 Wochen |
Participantsdifferent | 1-5 | Nutzertraffic | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Context Diagram, Container Diagram, Component Diagram | Interest Metrics, Conversion Signal, Learning Note | ICE Table, Top Idea List | Experiment results, Decision log, Learning summary |
Tagsno overlap | ArchitectureCommunicationVisualization | ValidationExperimentsDemandGrowth | PrioritizationScoringGrowthDecision | ExperimentsGrowthAnalyticsValidation |