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| Criterion |  Product Discovery Kano Model |  Growth Funnel Analysis |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | Medium | High | Low |
Timedifferent | 1-2 Tage | 1-3 h | 1-4 Wochen | 30-60 min |
Participantsdifferent | 10-50 | 1-5 | 1-6 | 1-5 |
Formatdifferent | Workshop + async | Async | Async | Workshop + async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Funnel report, Drop-off analysis, Optimization hypotheses | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | SatisfactionPrioritizationCustomer | AnalyticsConversionGrowth | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |