View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Growth Hooked Model |  Product Discovery Fake Door Test |  Product Discovery Hypothesis Prioritization Canvas |  Growth A/B Testing |
|---|---|---|---|---|
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 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. | 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. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | Multiple workshops over several weeks | 1-5 Tage | 60-90 min | 1-4 Wochen |
Participantsdifferent | 2-8 | Nutzertraffic | 3-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | Hooked loop, Trigger map, Reward design, Ethics check | Click Data, Interest Signal, Learning Decision | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary |
Tagsno overlap | GrowthBehaviorRetention | ValidationExperimentsDemandDiscovery | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |