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| Criterion |  Agile Affinity Estimation |  Product Discovery Dual-Track Agile |  Product Discovery Product Kata |  Agile Story Splitting |
|---|---|---|---|---|
Purposedifferent | When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. | When uncertainty and delivery run in parallel, it separates learning work from implementation while keeping both connected. It prevents unvalidated ideas from falling directly into the delivery stream. | Product Kata helps clarify customer problems, solution ideas, and evidence through a repeatable improvement routine. It captures direction, current metric, target metric, experiment notes, and learning. | When a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 30-90 min | Laufend, Wochen bis Monate | 1-2 Wochen je Loop | 30-60 min |
Participantsdifferent | 3-12 | 4-10 | 3-10 | 2-6 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Direction, Current metric, Target metric, Experiment note, Learning report | Smaller Stories, Acceptance Criteria, Split Rationale |
Tagsno overlap | EstimationBacklogRelative sizing | AgileDiscoveryDelivery | OutcomesDiscoveryLearningIterationCoaching | BacklogIterationDelivery |