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| Criterion |  Product Discovery Kano Model |  Product Discovery Hypothesis Prioritization Canvas |  Facilitation Dot Voting |  Product Discovery Experiment 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 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 a workshop has produced too many options and the group needs to condense quickly, dot voting makes preferences visible in a short time. It bundles individual votes into a solid signal for the next selection. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | Medium | Low | Low |
Timedifferent | 1-2 Tage | 60-90 min | 5-15 min | 30-60 min |
Participantsdifferent | 10-50 | 3-8 | 3-20 | 1-5 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop + async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Prioritization Canvas, Hypothesis Backlog | Ranked list, Consensus signal, Shortlist | Completed Experiment Canvas, Success Metric |
Tagsno overlap | SatisfactionPrioritizationCustomer | ExperimentsPrioritizationDiscoveryHypothesis | FacilitationVotingConsensusPrioritization | ExperimentsValidationDiscoveryHypothesis |