methodatlas
Compare

View methods side by side.

Choose up to four methods. Add them using the search and share the comparison by copying its link.

Criterion
Paper illustration for Dot Estimation.
Facilitation
Dot Estimation
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration of Hooked Model with its method-specific working model.
Growth
Hooked Model
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
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.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.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
LowMediumMediumHigh
Timedifferent
5-20 minlaufendMultiple workshops over several weeks30-90 min Setup, danach laufend
Participantsdifferent
3-202-122-81-8
Formatdifferent
WorkshopWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Effort Heatmap, Risk Signals, Discussion TargetsThroughput Data, Flow Forecast, Slicing RulesHooked loop, Trigger map, Reward design, Ethics checkForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationEffortRisk
EstimationForecastingFlow
GrowthBehaviorRetention
ForecastingFlowDelivery
Add more methods