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| Criterion |  Growth A/B Testing |  Product Strategy Counter Metrics |  Growth Funnel Analysis |  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. | 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 many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | 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 | Medium | Low |
Timedifferent | 1-4 Wochen | 30-60 min | 1-3 h | 1-5 Tage |
Participantsdifferent | 1-6 | 2-6 | 1-5 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Counter Metric List, Guardrail Definitions | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | MetricsMeasurementStrategyExperiments | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth |