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| Criterion |  Product Discovery Opportunity Interview |  Product Discovery Opportunity Solution Tree |  Knowledge Modeling Event Modeling |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | When opportunities are still hidden behind signals of need, it structures conversations around needs and obstacles. It looks for patterns that lead to new product opportunities. | When discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. | Event Modeling connects business workflows with commands, events, and views into one coherent mental model. It helps design behavior, UI, and technical slices from the same underlying logic. | 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 | Medium | Low |
Timedifferent | 30-60 min je Interview | 1-2 h Setup, laufend | 2-6 h | 30-60 min |
Participantsdifferent | 5-12 Interviews | 2-6 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Interview Notes, Opportunity Themes, Customer Quotes | Opportunity Solution Tree, Experiment Backlog, Learning Log | Event Model, UI Flow, Commands, Read Models | Completed Experiment Canvas, Success Metric |
Tagsno overlap | DiscoveryInterviewsOpportunityCustomer | DiscoveryOutcomesExperimentsOpportunity | EventsBlueprintDomain-Driven DesignBehavior | ExperimentsValidationDiscoveryHypothesis |