StreamWave is a subscription video streaming company with ~8M paying subscribers and meaningful seasonality: Q4 is boosted by holiday promotions and new device activations, while Q1 typically sees elevated churn. Finance needs a next-quarter (next 3 months) revenue forecast to set content spend and cash-flow targets. The stakes are high: a 2% forecasting error can swing quarterly guidance by tens of millions of dollars.
You are given 24 months of historical monthly revenue (in $M) for the core subscription product. The business also ran a major price increase starting in month 19, which may have shifted the revenue level.
You need to propose and execute a statistically sound approach to forecasting revenue for months 25–27, including uncertainty quantification. You will compare a baseline seasonal model against a model that accounts for a potential level shift after the price change.
Monthly revenue (in $M):
| Month | Revenue ($M) |
|---|---|
| 1 | 120.4 |
| 2 | 118.9 |
| 3 | 121.3 |
| 4 | 125.8 |
| 5 | 129.1 |
| 6 | 131.7 |
| 7 | 134.2 |
| 8 | 133.5 |
| 9 | 132.1 |
| 10 | 136.4 |
| 11 | 142.8 |
| 12 | 151.6 |
| 13 | 123.0 |
| 14 | 121.7 |
| 15 | 124.6 |
| 16 | 129.9 |
| 17 | 133.4 |
| 18 | 136.2 |
| 19 | 146.8 |
| 20 | 145.9 |
| 21 | 144.1 |
| 22 | 149.3 |
| 23 | 156.7 |
| 24 | 166.9 |
Additional info:
| Item | Value |
|---|---|
| Forecast horizon | 3 months (months 25–27) |
| Price increase effective | Month 19 onward |
| Confidence level for intervals | 95% |