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| Criterion | ![]() Growth Hooked Model | ![]() Facilitation Dot Estimation | ![]() Agile NoEstimates | ![]() Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | The Hooked Model helps clarify engagement loops, user behavior, and experiments. It makes repeat behavior measurable and captures the result as a hooked loop, trigger map, and reward design. | When size or effort can only be estimated roughly, Dot Estimation condenses the group's experience into a quick range. It turns individual contributions into a visible selection. The result is captured as an Effort Heatmap, Risk Signals, and Discussion Targets. | 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. | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. |
Complexitydifferent | Medium | Low | Medium | High |
Timedifferent | Multiple workshops over several weeks | 5-20 min | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-8 | 3-20 | 2-12 | 1-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Hooked loop, Trigger map, Reward design, Ethics check | Effort Heatmap, Risk Signals, Discussion Targets | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | GrowthBehaviorRetention | EstimationEffortRisk | EstimationForecastingFlow | ForecastingFlowDelivery |



