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| Criterion |  Product Discovery Kano Model |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Opportunity Solution Tree |
|---|---|---|---|
Purposedifferent | When features need to be distinguished by their effect on satisfaction, it makes expectations and surprises comparable. It separates basic needs, performance contribution, and delight. | 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 discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. |
Complexitysame | Medium | Medium | Medium |
Timedifferent | 1-2 Tage | 60-90 min | 1-2 h Setup, laufend |
Participantsdifferent | 10-50 | 3-8 | 2-6 |
Formatdifferent | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Prioritization Canvas, Hypothesis Backlog | Opportunity Solution Tree, Experiment Backlog, Learning Log |
Tagsno overlap | SatisfactionPrioritizationCustomer | ExperimentsPrioritizationDiscoveryHypothesis | DiscoveryOutcomesExperimentsOpportunity |
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