12-month revenue projection with seasonality and growth rate.
Inputs
Range around forecast
12-month forecast
Annual revenue—
Low estimate
—
Best estimate
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High estimate
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Monthly breakdown
Month
Seasonality
Revenue
vs Baseline
About this calculator
Revenue forecasting is one of those exercises operators do once per year for a board deck and never look at again. That\'s a mistake. The value isn\'t in the absolute numbers — it\'s in spotting deviations early. A campaign launch that produces revenue 30% below forecast is a problem worth investigating in week 2, not at month-end.
This calculator combines three inputs: current run-rate revenue (your baseline), YoY growth rate (how the trajectory bends over time), and a seasonality profile (how revenue distributes across months). Together they produce a 12-month projection with a confidence range to account for forecast uncertainty.
The seasonality profiles built in reflect typical patterns: ecommerce (Q4 = 25-40% of annual), apparel (spring + Q4 dual peaks), gift-heavy (Q4 dominant, slow Q1), subscription (flat). Pick the closest match to your category. For brands with 18+ months of historical monthly revenue, custom seasonality from your own data beats any preset.
Track variance vs forecast monthly. Small deviations (±10%) are noise. 20%+ deviations should trigger investigation: campaign performance, competitive shift, channel disruption, or genuine inflection. The Cash Flow Runway and BFCM Planner tools complement this by translating revenue forecasts into cash and seasonal-specific planning.
Frequently asked questions
How accurate are 12-month forecasts?
For mature businesses (3+ years of data): 80-90% accurate within ±15% range. For new brands: highly variable — model wide ranges and revisit monthly. The forecast becomes more useful for spotting deviations than predicting absolute numbers.
What growth rate should I assume?
Use trailing 12 months as baseline. Year-over-year growth of 20-50% is healthy for established DTC; 100%+ for early-stage. Don't extrapolate viral months as ongoing growth — single events distort baselines. Look at 6-month rolling averages.
How should I model BFCM and seasonal spikes?
Most ecommerce categories see Q4 (Oct-Dec) at 25-40% of annual revenue, with November alone often 12-18%. The Q4 multiplier in this calculator (1.5-2.5×) reflects that. Holiday/gift-heavy categories run higher; commodity categories run more even.
Should I forecast by channel or aggregate?
Channel-level if you have data. Different channels have different seasonality (paid social spikes more in Q4 than email; Amazon spikes more than DTC). Aggregate forecasts hide channel-specific risks like an Amazon algorithm change disproportionately affecting your numbers.
When does this forecast break?
During major business changes — launching a new SKU line, entering a new channel, raising prices significantly, or losing a top creator partnership. Past data doesn't predict outcomes after structural changes. Re-baseline after any change of that magnitude.