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| Criterion |  Agile Ideal Days |  Growth A/B Testing |  Decision Making Delphi Method |  Product Discovery Smoke Test |
|---|---|---|---|---|
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. | 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 knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | 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 | Low | High | High | Low |
Timedifferent | 15-60 min | 1-4 Wochen | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 2-9 | 1-6 | 6-30 Experten | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Experiment results, Decision log, Learning summary | Expert Forecast, Consensus Range, Assumption Notes | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | EstimationEffortAgile | ExperimentsGrowthAnalyticsValidation | ForecastingExpertsDecisionStrategy | ValidationExperimentsDemandGrowth |