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| Criterion |  Growth Growth Experiment |  Agile NoEstimates |  Engineering Kanban |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. | 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 | Medium | High |
Timedifferent | 1-2 Wochen | laufend | Ongoing | 1-4 Wochen |
Participantsdifferent | 1-6 | 2-12 | 2-12 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment card, Result summary, Next bet | Throughput Data, Flow Forecast, Slicing Rules | Kanban board, WIP policies, Flow metrics | Experiment results, Decision log, Learning summary |
Tagsno overlap | MarketingGrowthExperimentsLearning | EstimationForecastingFlow | FlowVisual managementDelivery | ExperimentsGrowthAnalyticsValidation |