Revenue & profit
A revenue forecast built from your own history
A daily revenue projection with an 80% confidence band, trained on your order history and its seasonality. The same model runs per SKU for reorder planning.
The problem
Planning inventory and cash off last month’s number ignores the trend and the seasonal shape of your own demand. A flat extrapolation over- or under-buys, and a generic industry benchmark does not know your catalog.
How Sellrics does it
Sellrics fits a forecast to your historical daily revenue and its weekly and annual seasonality (backend/api/forecast_revenue.py, backend/analytics/forecasting.py, backend/analytics/seasonality.py) and projects it forward with an 80% confidence interval. The same method runs per SKU (backend/api/forecast_sku.py) to produce demand projections that feed reorder planning. The model needs a minimum history to run; until then the view says so rather than showing a shaky line.
What the numbers mean
The predicted line is the model’s central estimate for each future day. The shaded band is the 80% confidence interval — actual revenue should land inside it four days in five. A wider band means more volatility in your own history, not a worse forecast.
A worked example
Illustrative example- Scenario
- A brand with about 60 days of clean history forecasts the next 30.
- What Sellrics surfaces
- The projection holds around $42k/day with an 80% band of roughly ±$4.2k, trending up about $250/day as the recent weeks compound. The band is tight because the sample history is stable.
- The move
- Size the next inventory order to the lower edge of the band for safety stock, and use the per-SKU forecast to split the quantity across products.
See it with sample data
Fictional sample dataA view from the Sellrics dashboard, on fictional sample data — no store connected, nothing stored.
Revenue Forecast (Next 30 Days)
80% confidence band · sampleNext 30 days: $1.3M – $1.5M predicted (mid: $1.4M)