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| Criterion |  Growth A/B Testing |  Decision Making Delphi Method |  Growth North Star Metric |  Product Strategy Counter Metrics |
|---|---|---|---|---|
Purposedifferent | 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 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 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 a headline metric could mask side effects, it adds guardrail signals against unwanted outcomes. It connects customer value, product logic, and decision priorities. The result is captured as a counter-metric list and guardrail definitions. |
Complexitydifferent | High | High | Medium | Low |
Timedifferent | 1-4 Wochen | 1-4 Wochen | 1-2 h | 30-60 min |
Participantsdifferent | 1-6 | 6-30 Experten | 3-8 | 2-6 |
Formatdifferent | Async | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Expert Forecast, Consensus Range, Assumption Notes | North Star metric, Input metric tree, Measurement cadence | Counter Metric List, Guardrail Definitions |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ForecastingExpertsDecisionStrategy | GrowthMetricsAlignmentRetention | MetricsMeasurementStrategyExperiments |