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| Criterion |  Decision Making Delphi Method |  Growth A/B Testing |  Growth North Star Metric |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | 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 product teams track many metrics, the view of the actual customer outcome easily gets lost. A North Star Metric bundles growth, usage, and value contribution into one signal that makes the system's direction visible. | 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 | High | Medium | Low |
Timedifferent | 1-4 Wochen | 1-4 Wochen | 1-2 h | 1-5 Tage |
Participantsdifferent | 6-30 Experten | 1-6 | 3-8 | Nutzertraffic |
Formatdifferent | Async | Async | Workshop + async | Async |
Outputdifferent | Expert Forecast, Consensus Range, Assumption Notes | Experiment results, Decision log, Learning summary | North Star metric, Input metric tree, Measurement cadence | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ForecastingExpertsDecisionStrategy | ExperimentsGrowthAnalyticsValidation | GrowthMetricsAlignmentRetention | ValidationExperimentsDemandGrowth |