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| Criterion |  Agile Affinity Estimation |  Agile Bucket System |  Product Discovery Opportunity Solution Tree |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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 a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates. | When discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 30-90 min | 30-90 min | 1-2 h Setup, laufend | 1-5 Tage |
Participantsdifferent | 3-12 | 3-12 | 2-6 | Nutzertraffic |
Formatdifferent | Workshop | Workshop | Workshop + async | Async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Bucketed Backlog, Relative Estimates, Split Candidates | Opportunity Solution Tree, Experiment Backlog, Learning Log | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | EstimationBacklogRelative sizing | EstimationBacklogRelative sizing | DiscoveryOutcomesExperimentsOpportunity | ValidationExperimentsDemandDiscovery |