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| Criterion |  Decision Making Pre-Mortem |  Architecture C4 Model |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | In early initiatives with many uncertainties, planning quickly turns too optimistic. A pre-mortem makes the expected failure visible in advance and sharpens the view of causes, gaps, and countermeasures. | 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 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 | 20–45 min | 1-4 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | Small cross-functional group | 1-5 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Risk list, Mitigation plan, Assumption log | Context Diagram, Container Diagram, Component Diagram | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | RiskDecisionFailurePlanning | ArchitectureCommunicationVisualization | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |