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| Criterion |  Growth A/B Testing |  Decision Making Constraint 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. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | 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 | 30-90 min | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-6 | 2-8 | 2-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Counter Metric List, Guardrail Definitions | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ConstraintsDecisionPlanningOptions | MetricsMeasurementStrategyExperiments | ValidationExperimentsDemandGrowth |