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| Criterion |  Product Discovery Kano Model |  UX Research Affinity Diagramming |  Product Discovery Hypothesis Prioritization Canvas |  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 research notes, feedback, or observations sit unconnected side by side, affinity diagramming sorts the raw material into solid themes. Many individual points turn into patterns that make decisions and opportunities clearer. | 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 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 | Low | Medium | Low |
Timedifferent | 1-2 Tage | 45–90 min | 60-90 min | 30-60 min |
Participantsdifferent | 10-50 | 3-10 | 3-8 | 1-5 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop + async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Theme clusters, Insight statements, Opportunity areas | Prioritization Canvas, Hypothesis Backlog | Completed Experiment Canvas, Success Metric |
Tagsno overlap | SatisfactionPrioritizationCustomer | SynthesisQualitativeRoot cause | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsValidationDiscoveryHypothesis |