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| Criterion |  Growth A/B Testing |  Agile Bucket System |  Decision Making Delphi Method |  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 a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates. | 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 | High | Medium | High | Low |
Timedifferent | 1-4 Wochen | 30-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-6 | 3-12 | 6-30 Experten | Nutzertraffic |
Formatdifferent | Async | Workshop | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Bucketed Backlog, Relative Estimates, Split Candidates | Expert Forecast, Consensus Range, Assumption Notes | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | EstimationBacklogRelative sizing | ForecastingExpertsDecisionStrategy | ValidationExperimentsDemandGrowth |