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.

Embed this forecaster on your site

Free to embed on any blog, template roundup or tool directory. Just the calculator, no branding clutter — one attribution link at the bottom.

Paste this where you want the tool to appear:

<iframe src="https://salesdamus.com/embed" width="100%" height="1400" style="border:1px solid #222;border-radius:8px;background:#0a0a0a" loading="lazy" title="Salesdamus sales forecast calculator"></iframe>

Or just link to us with a lightweight badge:

<a href="https://salesdamus.com"><img src="https://salesdamus.com/badge.svg" alt="salesdamus: free forecasts" width="192" height="20"></a>

> 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