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| Criterion |  Growth A/B Testing |  Growth Flywheel |  Product Discovery Experiment Canvas |  Growth Funnel Analysis |
|---|---|---|---|---|
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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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. |
Complexitydifferent | High | Medium | Low | Medium |
Timedifferent | 1-4 Wochen | 60-120 min | 30-60 min | 1-3 h |
Participantsdifferent | 1-6 | 3-8 | 1-5 | 1-5 |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog | Completed Experiment Canvas, Success Metric | Funnel report, Drop-off analysis, Optimization hypotheses |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | GrowthRetentionConversion | ExperimentsValidationDiscoveryHypothesis | AnalyticsConversionGrowth |