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| Criterion |  Growth Funnel Analysis |  Agile Affinity Estimation |  Agile Ideal Days |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. | 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. |
Complexitydifferent | Medium | Medium | Low | High |
Timedifferent | 1-3 h | 30-90 min | 15-60 min | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-12 | 2-9 | 1-6 |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Affinity Size Map, Grouped Estimates, Unclear Items | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | EstimationBacklogRelative sizing | EstimationEffortAgile | ExperimentsGrowthAnalyticsValidation |