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| Criterion |  Systems Thinking Leverage Points |  Knowledge Modeling Shape-First Modeling |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | Leverage Points show exactly where interventions in a system produce a disproportionate effect. It draws relationships, patterns, and feedback loops. The result is captured as a leverage map and action strategy. | Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start. | 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 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 | Medium | High | Low |
Timedifferent | Half day | Halber Tag pro Domain Slice | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-12 | 1-4 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Leverage Map, Action Strategy | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | Systems thinkingChangeStrategy | Knowledge graphValidationSemantic | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |