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| Criterion |  Systems Thinking Leverage Points |  Systems Thinking Future Reality Tree |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
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. | A Future Reality Tree shows how desired actions are meant to lead to a better system state. The method checks whether a path of change holds together logically before time flows into implementation. | 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 | High | High | Low | High |
Timedifferent | Half day | 2-4 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 3-12 | 3-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop | Workshop | Async | Async |
Outputdifferent | Leverage Map, Action Strategy | Future Reality Tree, Negative Branches, Assumption List, Improved Injections | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | Systems thinkingChangeStrategy | Theory of ConstraintsSystems thinkingChange | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |