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| Criterion |  Operations Bottleneck Analysis |  Product Strategy Impact Mapping |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput. | When a goal needs to be connected to several possible paths, it shows chains of impact instead of feature lists. It connects business goal, behavior change, and measures. | 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 | Medium | Medium | High | Low |
Timedifferent | 1-3 h | 45-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-8 | 3-8 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Impact Map, Outcome Hypotheses, Delivery Options | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | FlowMeasurementConstraints | OutcomesStrategyBehaviorPlanning | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |