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| Criterion |  Product Discovery Solution Interview |  Decision Making Decision Tree |  Product Discovery Outcome-Driven Innovation |  Innovation Design Thinking |
|---|---|---|---|---|
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. | 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. | 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. |
Complexitydifferent | Medium | Medium | High | High |
Timedifferent | 30-60 min je Interview | 30-90 min | Mehrere Wochen | 1 Tag bis mehrere Wochen |
Participantsdifferent | 5-10 Interviews | 1-6 | 2-6 researchers plus sample | 4-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Solution Feedback, Objection List, Validation Notes | Decision Tree, Option Map, Assumption List | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Problem Statement, Prototype, Test Learnings |
Tagsno overlap | ValidationInterviewsPrototype | DecisionTreeOptions | OutcomesInnovationResearchQuantitative | InnovationPrototypeResearch |