How does Salesdamus forecast sales?

It fits proven statistical models to your history — linear trend, Holt-Winters seasonality and more — then backtests them and uses whichever one predicted your recent past best. Bootstrap and Monte Carlo simulation turn that into honest 95% confidence intervals.

Completely. No account, no credit card, no catch. Salesdamus runs entirely in your browser, and your data never touches a server.

How much data do I need?

Two periods to start. Twelve or more unlocks reliable trends and accuracy scores; twenty-four or more lets it spot seasonality and forecast holiday and quarterly cycles.

As accurate as your data allows — and it's transparent about it. Every forecast comes with a backtested error rate and a confidence interval, so you know exactly how much to trust the number.

Can I trust the forecast?

Treat it as a grounded estimate, not a prophecy. The 95% interval shows the realistic range, and it widens further out because the future gets fuzzier the longer you look.

E-commerce sales forecast calculator

Preloaded with a sample store's revenue curve — swap it for your own numbers and forecast next month's sales, free and without an account.

Online stores rarely sell at a flat pace — Black Friday, holiday shopping and seasonal categories create real cycles. If you have two or more years of monthly revenue, Salesdamus will test a seasonal model against a plain trend and keep whichever one actually predicted your recent months better, instead of assuming a straight line.

It forecasts total revenue from your history. It doesn't split out traffic, conversion rate and average order value as separate drivers — track those individually if you want to see which lever is moving the number.

> DATA FOR ANALYSIS

Paste from Excel (Ctrl+V), import a CSV, or load an example

TRY EXAMPLE:

MONTH ?	SALES ?	EXPENSES ?	CONVERSION % ?

Drop a CSV file anywhere on the table

> WHAT TO FORECAST

SALES

Total revenue for the period

PROFIT

Net profit after deducting expenses

CONVERSION

Share of buyers from total visitors

GROWTH

Growth relative to previous period

> CALCULATION METHOD

BAYESIAN SHRINKAGE[P(θ|data)]

Shrinkage toward the mean for small-sample forecast stabilization

BOOTSTRAP CI[x̄* ± CI]

Residual resampling for confidence interval construction (1000 iterations)

MONTE CARLO SIM[N(ŷ, σ²)]

Parametric simulation from forecast distribution (10,000 samples)

> FORECAST HORIZON

How many future periods to project. Confidence intervals widen further out.

3 periods

Press to launch forecast

FAQ

Does it account for Black Friday / Q4 spikes?

If your history includes at least two prior holiday seasons, the seasonal model can pick up that pattern and factor it into the forecast. With less history, it defaults to a trend line and won't predict the spike.

Weekly or daily data instead of monthly?

Paste it in as-is — the calculator treats each row as one period, whatever cadence you use. Just keep it consistent across your history.

→ How the forecasting models work