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| Criterion |  Product Discovery Problem Interview |  UX Research Affinity Diagramming |  Product Strategy DIBB |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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 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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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 | Low | Low |
Timedifferent | 30-60 min je Interview | 45–90 min | 1-2 h | 30-60 min |
Participantsdifferent | 5-12 Interviews | 3-10 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Problem Evidence, Risk Notes, Customer Segments | Theme clusters, Insight statements, Opportunity areas | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric |
Tagsno overlap | DiscoveryInterviewsValidation | SynthesisQualitativeRoot cause | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis |