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| Criterion |  Growth A/B Testing |  Innovation Design Thinking |  Operations Root Cause Tree Analysis |  Innovation Design Sprint |
|---|---|---|---|---|
Purposedifferent | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | 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. | For a problem with several suspected causes, the method builds a causal structure instead of a mere list. It makes visible how causes connect and where the strongest points of leverage lie. | 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 | High | High | Medium | High |
Timedifferent | 1-4 Wochen | 1 Tag bis mehrere Wochen | 1-3 h | 4-5 Tage |
Participantsdifferent | 1-6 | 4-10 | 2-8 | 5-8 |
Formatdifferent | Async | Workshop | Workshop | Workshop |
Outputdifferent | Experiment results, Decision log, Learning summary | Problem Statement, Prototype, Test Learnings | Cause Tree, Evidence Notes, Countermeasures | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | InnovationPrototypeResearch | Root causeTreeIncidentQuality | Design sprintPrototypeValidationInnovation |