Revenue Prediction
Forecast next-period revenue with probability ranges — P10, P50, P90
A simulation that uses your historical revenue or cost data to forecast what your numbers will look like over the next 12 months. It runs 10,000 scenarios and returns a probability range — so you know the realistic downside, most likely outcome, and best case.
Answer the question: "Based on past performance, how much revenue should we expect next month — and how confident are we?"
Data — What data do you need
| Field | Power BI field / example | Description |
|---|---|---|
| Date field | SalesDate, ExpireDate | The time axis of your historical data — used to learn the return distribution over time. |
| Numeric value field | Sum of Revenue, Sum of Cost | What you want to forecast. The model calculates the return distribution from this field. Map to the Values slot. |
| ID / Category field | Asset, Product, Region | Groups the forecast by entity — one probability output row per entity. |
Use Case — Forecasting SKU Revenue for Next Quarter
Scenario: A retail FP&A analyst has 12 months of historical SKU-level revenue data. They want to know: for each SKU product, what is the P10/P50/P90 revenue range for Q1 next year — without manually building a forecast model in Excel?
Configuration:
- ▸Problem Type: Revenue Prediction
- ▸Date field: SalesDate
- ▸Values: Sum of Revenue
- ▸ID field: ProductID (SKU)
Sample output — P10 / P50 / P90 revenue forecast per SKU:
| Product / SKU | Dec 15, 2023 | Sep 15, 2023 | May 15, 2023 | Apr 15, 2023 |
|---|---|---|---|---|
| SKU-005 | 29.50K | 23.50K | 24.20K | 22.50K |
| SKU-004 | 18.50K | 13.50K | 12.50K | 11.20K |
| SKU-003 | 15.60K | 12.50K | 12.80K | 13.60K |
| SKU-002 | 33.40K | 27.80K | 26.80K | 29.50K |
| SKU-001 | 18.90K | 15.80K | 15.30K | 14.50K |
| Total | 115.30K | 93.10K | 91.60K | 84.90K |
