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| Criterion |  Product Discovery Kano Model |  Operations Causal Factor Analysis |  Operations Root Cause Tree 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. | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. | For a problem with several suspected causes, the method builds a causal structure instead of a mere list. It makes visible how causes connect and where the strongest points of leverage lie. | 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 | High | Medium | Low |
Timedifferent | 1-2 Tage | 2-6 h | 1-3 h | 30-60 min |
Participantsdifferent | 10-50 | 3-10 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Cause Tree, Evidence Notes, Countermeasures | Pareto Chart, Top Causes, Focus List |
Tagsno overlap | SatisfactionPrioritizationCustomer | CausalityIncidentRoot causeTimeline | Root causeTreeIncidentQuality | PrioritizationQualityFocusRoot cause |