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| Criterion |  Growth A/B Testing |  Growth Funnel Analysis |  Operations ALPEN Method |  Product Discovery Fake Door 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 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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | High | Medium | Low | Medium |
Timedifferent | 1-4 Wochen | 1-3 h | 10-20 min daily | 1-5 Tage |
Participantsdifferent | 1-6 | 1-5 | 1 | Nutzertraffic |
Formatsame | Async | Async | Async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Daily Plan, Time Estimates, Review Notes | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | PlanningTime managementProductivityOperations | ValidationExperimentsDemandDiscovery |