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Criterion
Paper illustration for Flywheel.
Growth
Flywheel
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Purposedifferent
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 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.When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all.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
MediumHighLowHigh
Timedifferent
60-120 min1-4 Wochen1-5 Tage1-4 Wochen
Participantsdifferent
3-86-30 ExpertenNutzertraffic1-6
Formatdifferent
WorkshopAsyncAsyncAsync
Outputdifferent
Flywheel Map, Friction Points, Growth Levers, Experiment BacklogExpert Forecast, Consensus Range, Assumption NotesInterest Metrics, Conversion Signal, Learning NoteExperiment results, Decision log, Learning summary
Tagsno overlap
GrowthRetentionConversion
ForecastingExpertsDecisionStrategy
ValidationExperimentsDemandGrowth
ExperimentsGrowthAnalyticsValidation
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