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| Criterion |  Product Discovery Kano Model |  UX Research Personas |  Operations Root Cause Analysis |  Decision Making Pareto Analysis |
|---|---|---|---|---|
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 target groups stay too vague and teams talk about the same people differently, personas create a shared, solid picture of usage. Condensed profiles make needs, contexts, and differences tangible enough for decisions. | When a problem keeps recurring and the cause stays unclear, the method exposes the underlying mechanism. It separates symptom, guess, and robust explanation from one another. | In many problem situations, effect is distributed unequally across many causes. A Pareto Analysis separates the few drivers with high leverage from the long remainder and focuses attention on what measurably moves the needle. |
Complexitydifferent | Medium | Medium | Medium | Low |
Timedifferent | 1-2 Tage | 1-3 Tage | 1-4 h | 30-60 min |
Participantsdifferent | 10-50 | Internes Team | 3-8 | 1-6 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Persona Profiles, Needs Summary, Scenario Notes | Problem Statement, Cause Hypotheses, Confirmed Causes, Action Plan | Pareto Chart, Top Causes, Focus List |
Tagsno overlap | SatisfactionPrioritizationCustomer | ResearchCustomerSynthesis | Root causeProblem solvingQualityIncident | PrioritizationQualityFocusRoot cause |