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| Criterion |  Delivery Cost of Delay |  Decision Making Decision Tree |  Product Discovery Fake Door Test |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | Helps clarify scope, order, and delivery flow in concrete terms. It makes work, constraints, and sequence manageable. The result is captured as a Cost of Delay table and prioritization sequence. | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | 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. | 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 | High | Medium | Medium | Low |
Timedifferent | 90-180 min | 30-90 min | 1-5 Tage | 30-60 min |
Participantsdifferent | 3-8 | 1-6 | Nutzertraffic | 1-5 |
Formatdifferent | Workshop | Workshop + async | Async | Workshop + async |
Outputdifferent | CoD Table, Prioritization Sequence | Decision Tree, Option Map, Assumption List | Click Data, Interest Signal, Learning Decision | Completed Experiment Canvas, Success Metric |
Tagsno overlap | PrioritizationDeliveryEconomicsDecision | DecisionTreeOptions | ValidationExperimentsDemandDiscovery | ExperimentsValidationDiscoveryHypothesis |