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| Criterion |  Decision Making Decision Tree |  Innovation Design Thinking |  Product Discovery MVP Test Matrix |  Innovation Design Sprint |
|---|---|---|---|---|
Purposedifferent | 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. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. |
Complexitydifferent | Medium | High | Medium | High |
Timedifferent | 30-90 min | 1 Tag bis mehrere Wochen | 45-75 min | 4-5 Tage |
Participantsdifferent | 1-6 | 4-10 | 2-8 | 5-8 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Problem Statement, Prototype, Test Learnings | Test Matrix, Test Plan | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | DecisionTreeOptions | InnovationPrototypeResearch | ExperimentsValidationDiscoveryOptions | Design sprintPrototypeValidationInnovation |