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.

SaaS MRR forecast calculator

Preloaded with a sample MRR curve — replace it with your own numbers and forecast where your monthly recurring revenue is headed, free and without an account.

MRR compounds, so a straight-line forecast usually undershoots or overshoots it. Salesdamus fits your history to an exponential-friendly trend model, backtests it against your own recent months, and reports the resulting error rate — so you know whether the forecast is solid or shaky before you put it in a board deck.

It projects total MRR from your monthly totals. It does not decompose new, expansion, contraction and churned MRR separately — if you track those individually, forecast each series on its own for a cleaner read.

> 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

Can it forecast ARR instead of MRR?

Yes — paste your data as annual figures instead of monthly, or multiply your MRR forecast by 12; the underlying math is the same time series.

What if my MRR history is short?

It works from two months, but forecasts stabilize a lot around 12 months of history — that's also when Salesdamus can start backtesting to pick the best-fitting model.

→ How the forecasting models work