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| Criterion |  Agile Ideal Days |  Knowledge Modeling Knowledge Mapping |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats. | Knowledge Mapping makes knowledge, gaps, and transfer paths visible across a field. It fits when expertise should not just exist but also be findable and transferable. | 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 | 15-60 min | 1-3 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 2-9 | 3-12 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Knowledge Map, Critical Knowledge Areas, Transfer Plan | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | EstimationEffortAgile | KnowledgeMappingRisk | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |