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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of RAID Log with a method-specific labelled workspace.
Delivery
RAID Log
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Purposedifferent
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.Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as a RAID Log and a source for status reporting.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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all.
Complexitydifferent
HighLowMediumLow
Timedifferent
1-4 Wochen30 min Setup, dann laufend1-3 h1-5 Tage
Participantsdifferent
1-61-3 maintaining, briefing for everyone1-5Nutzertraffic
Formatsame
AsyncAsyncAsyncAsync
Outputdifferent
Experiment results, Decision log, Learning summaryRAID Log, Status Report SourceFunnel report, Drop-off analysis, Optimization hypothesesInterest Metrics, Conversion Signal, Learning Note
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
RiskTrackingStakeholdersGovernance
AnalyticsConversionGrowth
ValidationExperimentsDemandGrowth
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