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| Criterion |  Product Discovery Kano Model |  Operations PDCA Cycle |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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. | For an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode. | 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 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 | Low | Medium | Medium |
Timedifferent | 1-2 Tage | 1 h bis mehrere Wochen | 60-90 min | 1-5 Tage |
Participantsdifferent | 10-50 | 1-8 | 3-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Prioritization Canvas, Hypothesis Backlog | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | SatisfactionPrioritizationCustomer | Continuous improvementLeanExperiments | ExperimentsPrioritizationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |