Free tool · Robert × Dor

Retail sales
forecast builder.

Forecast the next 12 months from visitors, conversion and average transaction. Pick or edit a seasonality curve, add a traffic trend, and download the result as a CSV.

12-month sales forecast

$558,061

Peak Dec at $72,966; quietest Feb at $34,447.

Visitors: 48,782
Transactions: 10,732
Average month: $46,505
Revenue per visitor: $11.44

A forecast is only as good as its inputs. Conversion and average transaction are held flat here. The traffic line is the one most stores guess; a door counter turns it into history.

What moves this forecast most

+1 point of conversion adds about $25,366 a year.
+$1 on the average transaction adds about $10,732.
Plan stock and staff for Dec: it carries 13% of the year.

Month by month

Month	Visitors	Transactions	Sales
Jan	3,204	705	$36,653
Feb	3,011	662	$34,447
Mar	3,622	797	$41,438
Apr	3,833	843	$43,848
May	4,045	890	$46,270
Jun	3,852	847	$44,065
Jul	3,861	849	$44,174
Aug	4,075	896	$46,613
Sep	3,880	854	$44,392
Oct	4,095	901	$46,843
Nov	4,926	1,084	$56,351
Dec	6,378	1,403	$72,966
Year	48,782	10,732	$558,061

Indexes are rescaled so the 12 months average 1.0; 1.2 means 20% busier than an average month.

Worked example.

A store averages 4,000 visitors a month, converts 22% at a $52 average transaction, and has a holiday-heavy year. Revenue per visitor is 22% × $52 = $11.44.

An average month is 4,000 × $11.44 = $45,760 before seasonality and trend.
With the holiday curve, December is 1.55× an average month and February 0.75×, so December alone carries about twice February’s sales.
A 3% yearly traffic trend adds a little each month, compounding across the year.

The most useful thing you can do with this forecast is test it. Change conversion by one point and watch the year change; then ask how sure you are of the visitor number every month is built on.

How to build a retail sales forecast.

Start with traffic. Last year’s visitors by month, or a few weeks of counts scaled up.
Apply your seasonality. Divide each month by the average month to get its index, and type it into the table.
Add conversion and average transaction. From your POS and door counts over the same period.
Set a trend. Use what your traffic actually did year on year, not what you hope.
Revisit monthly. Replace forecast months with actuals as they close and download the CSV to keep a record.

Questions retailers ask.

How do I forecast retail sales?

Multiply expected visitors by conversion rate by average transaction for each month. Visitors drive the shape of the year, so start from monthly traffic and apply your seasonality.

What is a seasonality index?

Each month’s sales or traffic divided by the average month. An index of 1.5 means 50% above an average month. This tool rescales your indexes so they average exactly 1.0.

Where do the preset seasonality curves come from?

They’re illustrative shapes to get you started, not industry data. Replace them with your own monthly history for a real forecast.

Why forecast with traffic instead of last year’s sales?

Sales mix three things: how many people came, how many bought and how much they spent. Forecasting each one separately shows which assumption carries the most risk.

Can I open the forecast in Excel or Google Sheets?

Yes. Download the CSV and open it in any spreadsheet. The link to this page also keeps your inputs so you can share it.

Forecast from counted visitors.

Dor keeps an hourly history of foot traffic next to your POS sales, so next year’s forecast starts from what actually happened.

Book a Dor demo

Retail salesforecast builder.