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| Criterion |  Product Strategy Counter Metrics |  Domain Modeling Context Map |  Product Discovery Smoke Test |  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 several contexts need to talk to each other, it makes their relationships and dependencies legible. It helps sort integration pressure and responsibilities across system boundaries. | 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 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 | Medium | Low | High |
Timedifferent | 30-60 min | 1-3 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 2-6 | 2-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Counter Metric List, Guardrail Definitions | Context Map, Integration Patterns, Boundary Notes | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | MetricsMeasurementStrategyExperiments | Domain-Driven DesignBoundariesStrategy | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |