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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration for Flywheel.
Growth
Flywheel
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
When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.Growth often breaks down where a reinforcing mechanism builds up too much friction. A Flywheel shows the cycle of value, repetition, and reinforcement meant to sustain growth.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
HighMediumMediumHigh
Timedifferent
1-4 Wochen60-120 min1-3 h1-4 Wochen
Participantsdifferent
6-30 Experten3-81-51-6
Formatdifferent
AsyncWorkshopAsyncAsync
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesFlywheel Map, Friction Points, Growth Levers, Experiment BacklogFunnel report, Drop-off analysis, Optimization hypothesesExperiment results, Decision log, Learning summary
Tagsno overlap
ForecastingExpertsDecisionStrategy
GrowthRetentionConversion
AnalyticsConversionGrowth
ExperimentsGrowthAnalyticsValidation
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