Restaurant Sales Forecast Template

Start with the Assumptions sheet — it drives everything else. Fill in your seating capacity, days open per week, and average covers by meal period. Then enter your average check size for each day part (breakfast checks are typically lower than dinner), and set your expected channel mix: what percentage of revenue comes from takeout, delivery, catering, and alcohol. If you have historical POS data, use last year's actuals as your baseline — most restaurants find the initial setup takes 20–30 minutes and requires a few passes to get the numbers to feel right.

Once your assumptions are set, review the Monthly Forecast sheet to check whether the projections look realistic month by month. Apply seasonal adjustment factors for months where you know volume shifts — summer patio season, holiday weeks in December, the January slowdown. The Scenario Comparison sheet is worth filling out early too: set a conservative downside (maybe 15% fewer covers than base) and an optimistic upside, then check whether the range feels plausible based on your experience. This takes another 20 minutes but gives you a much more defensible forecast.

The ongoing value is in the Actual vs Forecast sheet. After each month closes, pull your POS revenue report and enter the actuals. The variance calculations will show you immediately whether you're tracking to plan and which channels are outperforming or lagging. If delivery revenue consistently comes in below forecast, your assumed delivery mix might need adjustment. If your average dinner check is running higher than expected, that's a sign your menu pricing is working. Most restaurant operators find 15–20 minutes per month is enough to keep the forecast current and useful.

Restaurant revenue is more predictable than most operators believe — but only once you break it down into its components. Total revenue is covers × average check × days open, repeated across each day part and channel. The problem with gut-feel forecasting is that it treats revenue as one number, so it's impossible to pinpoint what drove a good week or a bad one. A driver-based forecast makes the assumptions explicit: if your Wednesday lunch covers are 40 and your average lunch check is $18, you know exactly what you're projecting, and when actuals differ, you know exactly where to look.

For restaurants, the highest-leverage forecast variables are covers by day part and channel revenue mix. Covers are driven by seating capacity, table turnover rate, and occupancy percentage — and these vary dramatically by day of week and season. A well-built restaurant forecast reflects that Wednesday lunch runs at 60% occupancy while Friday dinner pushes 95%. Channel mix has become a major variable post-2020: delivery platforms take 20–30% commission on a lower average check, so a shift from 20% to 35% delivery mix meaningfully changes your effective revenue per cover even if total order count stays flat.

The forecast becomes an operational tool when you compare actuals against it monthly. A restaurant that forecasts $180K in revenue for October but lands at $162K needs to know whether the gap came from fewer covers, a lower average check, or a shift in channel mix — because each has a different fix. Fewer covers might mean a marketing problem. Lower check might mean the new menu mix isn't working. More delivery than expected might mean your in-house dining experience needs attention. This template is built to surface those distinctions so you're making decisions based on data, not guesswork.