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| Criterion |  Product Discovery Kano Model |  Product Discovery Problem Interview |  Product Discovery Fake Door Test |  Product Discovery Hypothesis Prioritization Canvas |
|---|---|---|---|---|
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 the picture of the problem still needs to become solid, it asks about real situations and consequences. It separates genuine suffering from mere interest in a solution. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 1-2 Tage | 30-60 min je Interview | 1-5 Tage | 60-90 min |
Participantsdifferent | 10-50 | 5-12 Interviews | Nutzertraffic | 3-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Problem Evidence, Risk Notes, Customer Segments | Click Data, Interest Signal, Learning Decision | Prioritization Canvas, Hypothesis Backlog |
Tagsno overlap | SatisfactionPrioritizationCustomer | DiscoveryInterviewsValidation | ValidationExperimentsDemandDiscovery | ExperimentsPrioritizationDiscoveryHypothesis |