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| Criterion |  Product Discovery Solution Interview |  Decision Making Decision Tree |  Innovation Design Thinking |  Product Discovery Outcome-Driven Innovation |
|---|---|---|---|---|
Purposedifferent | When a solution idea is already on the table, it checks how it is understood and evaluated. It looks for resonance, objections, and missing fit early in the conversation. | 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. | For complex user problems with an uncertain cause, the method connects observation, interpretation, and experiment into a learning cycle. It keeps the focus on real needs and avoids premature solution language. This produces robust decisions for product and service. | Outcome-Driven Innovation clarifies customer problems, solution opportunities, and evidence. It separates jobs, desired outcomes, importance, satisfaction, and opportunity, and captures results as desired outcome statements, an opportunity landscape, and segments by underserved needs. |
Complexitydifferent | Medium | Medium | High | High |
Timedifferent | 30-60 min je Interview | 30-90 min | 1 Tag bis mehrere Wochen | Mehrere Wochen |
Participantsdifferent | 5-10 Interviews | 1-6 | 4-10 | 2-6 researchers plus sample |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Solution Feedback, Objection List, Validation Notes | Decision Tree, Option Map, Assumption List | Problem Statement, Prototype, Test Learnings | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses |
Tagsno overlap | ValidationInterviewsPrototype | DecisionTreeOptions | InnovationPrototypeResearch | OutcomesInnovationResearchQuantitative |