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| Criterion |  Product Strategy Counter Metrics |  Product Discovery Smoke Test |  Product Discovery Experiment Canvas |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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 | Low | Low | Low | High |
Timedifferent | 30-60 min | 1-5 Tage | 30-60 min | 1-4 Wochen |
Participantsdifferent | 2-6 | Nutzertraffic | 1-5 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Counter Metric List, Guardrail Definitions | Interest Metrics, Conversion Signal, Learning Note | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary |
Tags1 shared | MetricsMeasurementStrategyExperiments | ValidationExperimentsDemandGrowth | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |