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| Criterion |  Operations Bottleneck Analysis |  Growth Growth Experiment |  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 growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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 | 1-2 Wochen | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-8 | 1-6 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Experiment card, Result summary, Next bet | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | FlowMeasurementConstraints | MarketingGrowthExperimentsLearning | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |