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| Criterion |  Growth A/B Testing |  Decision Making Force Field Analysis |  Product Strategy Counter Metrics |  Product Discovery Smoke Test |
|---|---|---|---|---|
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. | In change initiatives, supporting and restraining forces sit at the table at the same time. Force Field Analysis makes these tensions explicit and shows where change can be pushed forward by strengthening or relieving. | 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. | 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 | Low | Low | Low |
Timedifferent | 1-4 Wochen | 45-90 min | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-6 | 3-12 | 2-6 | Nutzertraffic |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Force Field Map, Change Levers, Risk Notes | Counter Metric List, Guardrail Definitions | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ChangeDecisionStrategy | MetricsMeasurementStrategyExperiments | ValidationExperimentsDemandGrowth |