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Criterion
How Might We
Innovation
How Might We
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
Paper illustration for Fake Door Test
Product Discovery
Fake Door Test
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Purposedifferent
With a stuck problem carrying many implicit assumptions, the method translates observations into open, workable questions. It sharpens the problem frame without pre-thinking the solution, turning frustration or fuzziness into a search space with clear direction.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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small.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
30–45 min1-3 h1-5 Tage1-4 Wochen
Participantsdifferent
1-101-5Nutzertraffic1-6
Formatdifferent
Workshop + asyncAsyncAsyncAsync
Outputdifferent
HMW Questions, Challenge Framing, Idea PromptsFunnel report, Drop-off analysis, Optimization hypothesesClick Data, Interest Signal, Learning DecisionExperiment results, Decision log, Learning summary
Tagsno overlap
FramingOpportunityIdeationQuestions
AnalyticsConversionGrowth
ValidationExperimentsDemandDiscovery
ExperimentsGrowthAnalyticsValidation
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