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| Criterion |  Growth Growth Experiment |  Product Strategy Counter Metrics |  Decision Making Force Field Analysis |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 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. | 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 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. |
Complexitydifferent | Medium | Low | Low | High |
Timedifferent | 1-2 Wochen | 30-60 min | 45-90 min | 1-4 Wochen |
Participantsdifferent | 1-6 | 2-6 | 3-12 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Async |
Outputdifferent | Experiment card, Result summary, Next bet | Counter Metric List, Guardrail Definitions | Force Field Map, Change Levers, Risk Notes | Experiment results, Decision log, Learning summary |
Tagsno overlap | MarketingGrowthExperimentsLearning | MetricsMeasurementStrategyExperiments | ChangeDecisionStrategy | ExperimentsGrowthAnalyticsValidation |