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| Criterion |  Product Discovery Kano Model |  Growth Funnel Analysis |  Product Strategy ICE Scoring |  Growth A/B Testing |
|---|---|---|---|---|
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 ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | 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 | Medium | Medium | Low | High |
Timedifferent | 1-2 Tage | 1-3 h | 30-60 min | 1-4 Wochen |
Participantsdifferent | 10-50 | 1-5 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Funnel report, Drop-off analysis, Optimization hypotheses | ICE Table, Top Idea List | Experiment results, Decision log, Learning summary |
Tagsno overlap | SatisfactionPrioritizationCustomer | AnalyticsConversionGrowth | PrioritizationScoringGrowthDecision | ExperimentsGrowthAnalyticsValidation |