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Criterion
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Paper illustration for PDCA Cycle.
Operations
PDCA Cycle
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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all.For an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode.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
LowLowMediumHigh
Timedifferent
1-5 Tage1 h bis mehrere Wochen1-3 h1-4 Wochen
Participantsdifferent
Nutzertraffic1-81-51-6
Formatdifferent
AsyncWorkshop + asyncAsyncAsync
Outputdifferent
Interest Metrics, Conversion Signal, Learning NotePDCA Log, Experiment Plan, Learning Outcome, Standard ChangeFunnel report, Drop-off analysis, Optimization hypothesesExperiment results, Decision log, Learning summary
Tagsno overlap
ValidationExperimentsDemandGrowth
Continuous improvementLeanExperiments
AnalyticsConversionGrowth
ExperimentsGrowthAnalyticsValidation
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