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Criterion
A hypothetical failure makes concrete risks, signals, and fitting mitigations visible.
Decision Making
Pre-Mortem
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Purposedifferent
In early initiatives with many uncertainties, planning quickly turns too optimistic. A pre-mortem makes the expected failure visible in advance and sharpens the view of causes, gaps, and countermeasures.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 many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses.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.
Complexitydifferent
LowMediumMediumHigh
Timedifferent
20–45 min30-90 min1-3 h1-4 Wochen
Participantsdifferent
Small cross-functional group1-61-51-6
Formatdifferent
WorkshopWorkshop + asyncAsyncAsync
Outputdifferent
Risk list, Mitigation plan, Assumption logDecision Tree, Option Map, Assumption ListFunnel report, Drop-off analysis, Optimization hypothesesExperiment results, Decision log, Learning summary
Tagsno overlap
RiskDecisionFailurePlanning
DecisionTreeOptions
AnalyticsConversionGrowth
ExperimentsGrowthAnalyticsValidation
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