What-if Assumptions

Adjust the value of an assumption metric to see how it affects the projection metric.

Overview

Assumptions act like filters and can be added to any chart or analysis that contains a projection metric. Assumption metrics are specific to the model, so a metric may be adjustable in one model but not in another. For more information, see What-if Models.

Assumption types

What-if models can have two types of assumption metrics: general assumptions and focused assumptions. The type of assumptions that are included will differ between models.

Assumptions are made for future periods.

General assumptions are applied across time. For example, if the Resignation Rate is a general assumption and you change the assumption value from 3% to 5%, the new Resignation Rate will be used to calculate projections for all future time periods.

Focused assumptions are applied to a single point in time. For example, if the Resignation Rate is a focused assumption, you must specify the future time period that you want to make the assumption for. When you change the assumption value from 3% to 5%, the new Resignation Rate will only be used to calculate a projection for the selected time period.

Note:  

  • General assumptions are applied across all populations regardless of what you've specified in your analysis context.
  • Focused assumptions are applied to the population specified in your analysis context. This means you can make individual assumptions for the population in the selected future time period.
    • For example, if the Resignation Rate is a focused assumption, you can make an assumption for a specific population, such as High Performers. To do this, you first add a filter to your analysis context for High Performers. Then, you'd select a future time period and add an assumption for the Resignation Rate. When you change the assumption value from 3% to 5%, the new rate is applied only to High Performers and used to calculate a projection for the selected time period.

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