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| Criterion |  Growth Hooked Model |  Product Discovery Hypothesis Prioritization Canvas |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | The Hooked Model helps clarify engagement loops, user behavior, and experiments. It makes repeat behavior measurable and captures the result as a hooked loop, trigger map, and reward design. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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 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 | Medium | Medium | High | Medium |
Timedifferent | Multiple workshops over several weeks | 60-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 2-8 | 3-8 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | Hooked loop, Trigger map, Reward design, Ethics check | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | GrowthBehaviorRetention | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |