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Criterion
How Might We
Innovation
How Might We
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
Paper illustration for Fake Door Test
Product Discovery
Fake Door Test
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 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.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.
Complexitydifferent
LowHighMediumMedium
Timedifferent
30–45 min1-4 Wochen1-3 h1-5 Tage
Participantsdifferent
1-101-61-5Nutzertraffic
Formatdifferent
Workshop + asyncAsyncAsyncAsync
Outputdifferent
HMW Questions, Challenge Framing, Idea PromptsExperiment results, Decision log, Learning summaryFunnel report, Drop-off analysis, Optimization hypothesesClick Data, Interest Signal, Learning Decision
Tagsno overlap
FramingOpportunityIdeationQuestions
ExperimentsGrowthAnalyticsValidation
AnalyticsConversionGrowth
ValidationExperimentsDemandDiscovery
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