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| Criterion |  Product Discovery Kano Model |  Growth Funnel Analysis |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | When features need to be distinguished by their effect on satisfaction, it makes expectations and surprises comparable. It separates basic needs, performance contribution, and delight. | 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. | 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 | Medium | Medium | High | Low |
Timedifferent | 1-2 Tage | 1-3 h | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 10-50 | 1-5 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | SatisfactionPrioritizationCustomer | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |