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| Criterion |  Growth A/B Testing |  Growth Funnel Analysis |  Operations ALPEN Method |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 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. | With an overloaded day full of too many tasks, a realistic picture of the day emerges. The method connects estimating, deciding, and buffer thinking so planning fits available energy and time. | 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. |
Complexitydifferent | High | Medium | Low | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 10-20 min daily | 30-60 min |
Participantsdifferent | 1-6 | 1-5 | 1 | 1-5 |
Formatdifferent | Async | Async | Async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Daily Plan, Time Estimates, Review Notes | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | PlanningTime managementProductivityOperations | ExperimentsValidationDiscoveryHypothesis |