1. Abstract
When building scenarios that assume a consistent change across the forecast horizon, such as a uniform price increase or cost reduction, the Mass Apply Values feature allows users to update assumptions quickly, consistently, and without manual error.
2. Context
Use this best practice when creating scenarios that apply the same value, growth rate, or percentage change to one or more indicators across all future periods. This is especially useful for inflation, pricing, wage growth, or policy-driven assumptions.
3. Content
3.1 Why It Matters
Scenario analysis is meant to test assumptions, not manual data entry. Editing values month by month can:
- Be time-consuming
- Increase the risk of inconsistency
- Make scenarios harder to audit and explain
Mass Apply Values ensures that scenario assumptions are:
- Applied uniformly
- Easy to review
- Simple to reproduce or adjust later
3.2 How to Apply
- Open the Scenario Forecast you want to modify.
- Select the indicator (or indicators) to which the assumption applies.
- Click Mass Apply Values.
- Choose the type of adjustment:
- Raw value ("Raw")
- Year-over-year growth rate ("%")
- Percentage point spread relative to baseline ("% Spread")
- Enter the assumption (for example, +/- 5% YoY).
- Preview the resulting forecast values.
- Save the scenario with a clear, descriptive name (for example, “Real DPI +5% Scenario”).
3.3 Example
A finance team wants to test the impact of higher consumer incomes. They apply a +5% YoY assumption to the Real Disposable Personal Income indicator using Mass Apply Values. All future periods update instantly, and dependent indicators such as revenue adjust automatically.
3.4 Common Pitfalls
- Forgetting to rename or clearly label the scenario after applying changes
- Mixing raw values and percentage changes unintentionally
- Applying uniform growth to indicators that historically behave inversely (for example, costs vs. margins)
- Using Mass Apply Values without reviewing the preview
3.5 Expected Results
- Faster scenario creation
- Consistent, auditable assumptions
- Cleaner comparisons between baseline and alternative scenarios
- Reduced manual error and rework