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| Criterion |  Growth Funnel Analysis |  Growth Flywheel |  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. | Growth often breaks down where a reinforcing mechanism builds up too much friction. A Flywheel shows the cycle of value, repetition, and reinforcement meant to sustain growth. | 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 | 60-120 min | 15-60 min | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-8 | 2-9 | 1-6 |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | GrowthRetentionConversion | EstimationEffortAgile | ExperimentsGrowthAnalyticsValidation |