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Running e-commerce revenue forecast analysis...
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Analyze another fileForecasts future revenue using exponential smoothing with automatic seasonality detection. Aggregates transaction data to monthly totals, fits a model, and projects revenue forward with confidence intervals.
Use this when you have at least 24 months of transaction data and want to forecast revenue for the next 6-12 months.
If you have less than 12 months of data, forecasts will be unreliable — use Simple Trend instead. If you need daily forecasts (not monthly), use ARIMA.
Built for: CFO, e-commerce director, financial analyst, operations planner
Typical data source: Transaction or revenue data with dates, ideally 2+ years of history
Transaction data with dates and revenue amounts
Minimum 100 rows · Best with 500-50000 transactions over 24+ months
Forecasts future revenue from e-commerce transaction data using ETS exponential smoothing with automatic seasonality detection. Aggregates order-level records to monthly revenue, fits an auto-selected ETS model, and generates projections with 80% and 95% confidence intervals.
Historical revenue (solid line) and forecast (dashed) with confidence intervals (shaded band). Wider bands = more uncertainty. If the band includes zero, the forecast is too uncertain to act on.
Breaks revenue into trend (long-term direction), seasonal (recurring patterns), and remainder (noise). A strong seasonal component means timing matters for planning.
The repeating seasonal pattern by month. Shows which months are naturally high and low. Use this for inventory and marketing budget allocation.
Revenue by top countries/regions
Month-by-month forecast with confidence intervals. The 80% interval is what to plan for; the 95% is worst/best case.
How well the model fits historical data. MAPE below 10% is excellent; above 20% suggests the data is too noisy for reliable forecasts.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Simple Trend — Just need a trend line, not a statistical forecast
Need more power? Arima — Need ARIMA with custom differencing and seasonal parameters
Similar: Cash Forecast, Time Series Forecast
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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