Forecast Pub Sales in 2026

Last updated: 11 April 2026

Most pub landlords forecast sales by guessing based on last year—and then get shocked when March arrives and the numbers don’t match reality. You’re not making poor decisions; you’re making decisions on data that doesn’t account for the variables that actually move your till. The difference between a pub that survives a slow quarter and one that runs into cash flow problems isn’t luck—it’s whether you know how to forecast pub sales UK operators face.

If you’ve ever wondered whether you could actually predict revenue instead of reacting to it, this guide is for you. I’ll show you the practical methods that work in real pubs, the data points that actually matter, and how to build a forecast that your bank manager will actually believe. This isn’t theoretical—it’s based on running Teal Farm Pub in Washington, Tyne & Wear alongside managing systems for 847 active users across SmartPubTools, all dealing with the same unpredictable variables you face.

You’ll learn exactly what to measure, how to structure your data, and how to adjust forecasts when reality doesn’t match the plan. You’ll also discover the one metric most pubs ignore that predicts cash flow better than anything else.

Why Most Pub Forecasts Fail

I’ve watched landlords forecast sales the same way they’ve done it for ten years: take last year’s total, add 5%, split it across 52 weeks, and hope for the best. Then January arrives, nobody comes in because the weather’s terrible and there’s a pub two streets away running a January promotion, and suddenly the forecast looks useless.

The reason forecasts fail is that they treat a pub’s sales as a steady line, when in reality pub revenue moves in waves. A Wednesday is not the same as a Saturday. A week with a quiz night is completely different from a week without one. A bank holiday brings a different crowd and spend pattern than a regular day. Most forecasts smooth all of that out.

The second reason forecasts fail is that they’re built on guesses, not data. I spent weeks evaluating EPOS systems for Teal Farm Pub before settling on one that could actually give me the granular data I needed—transaction count, average spend per customer, payment method breakdown, hour-by-hour till patterns during peak trading. Most systems that look good in a demo struggle to give you useful historical data when three staff are hitting the same terminal during last orders. Once I could see the actual patterns, everything changed.

A proper pub sales forecast should take 4–6 weeks of accurate, transaction-level data from your till and build forward from there. Not guesses. Not industry averages. Your data.

The Data Sources That Actually Matter

Your EPOS Till Data (Non-Negotiable)

If you’re running a modern EPOS system, this is your primary data source. If you’re still using a basic till that only records daily totals, you’re making decisions blind. You need:

• Transaction count per day and per hour—not just total sales, but how many customers came through the till
• Average spend per transaction—whether customers are buying singles or rounds (this changes with day of week)
• Category breakdown—wet sales (draught, bottled, spirits), food, soft drinks, payments
• Day-of-week patterns—a working Thursday is not a Thursday during school holidays
• Hour-by-hour data during peak times—shows when your pub makes most of its money

If your current till doesn’t give you this detail, upgrading your till system is your first investment. The cost of a better EPOS system pays for itself in forecast accuracy alone.

Historical Data from Your Own Pub (12+ Weeks Minimum)

Industry benchmarks are useful for sense-checking, but they’re not your pub. Your pub has its own rhythm. Teal Farm runs quiz nights on Tuesday and Thursday, which lifts those days by roughly 35% compared to a Monday. Saturday is peak. Food sales on Friday and Saturday are 3x higher than Tuesday. That pattern is specific to us—your pub’s pattern is different.

Pull 12 weeks of till data (minimum—16 weeks is better if you can get it). Break it down by:

• Day of week (Monday, Tuesday, etc.)
• Week type (school holidays, bank holidays, normal weeks)
• Special events (quiz nights, match days, food events)
• Weather or local factors (seaside pub in summer behaves differently than in winter)

This becomes your baseline. Once you know your actual Monday average and your actual Saturday average, you can forecast forward with real numbers.

Staffing Costs and Payroll Timing

This isn’t a revenue data source, but it’s essential for forecasting cash flow, which is what actually matters when the bank comes calling. Use your pub staffing cost calculator to understand exactly what your team costs in a high-trading week versus a low week. I manage 17 staff across front-of-house and kitchen at Teal Farm, and wage timing doesn’t match trading timing—Friday’s trading pays for the following week’s wages. That lag matters.

Supplier Payment Terms

Most pubs are on 30-day terms with their main wholesaler. That means Friday’s beer sale doesn’t become cash-in-hand until you’ve paid for next Wednesday’s delivery. That gap is where cash flow problems hide.

How to Build a Working Forecast

Step 1: Calculate Your Weekly Baseline

Take your 12 weeks of historical data and calculate your average for each day of the week. You’ll get something like this (these are real patterns from a wet-led pub, not made up):

• Monday: £680 average
• Tuesday: £750 average (slightly higher due to quiz night)
• Wednesday: £620 average
• Thursday: £740 average (quiz night again)
• Friday: £1,280 average
• Saturday: £1,460 average
• Sunday: £890 average
• Weekly baseline: £6,420 average

This is your foundation. Everything else layers on top of this. This is the sales forecast before you account for seasons, events, or disruption.

Step 2: Add Seasonal Adjustment Factors

Not all weeks are equal. Winter weeks trade lower than summer weeks. January is different from December. Create adjustment factors based on your actual data.

Example seasonal adjustments (these vary by pub type and location):

• January: -15% to -20% (post-New Year, cold, darker)
• February: -12% (shortest month, cold continues)
• March: -5% (spring starts, marginally better)
• April–August: +5% to +15% (summer months, garden trade, lighter evenings)
• September: -8% (back to school, summer ends)
• October: Baseline (transition month)
• November–December: +10% to +20% (Christmas parties, darker evenings drive pub trade)

These are examples. Calculate your own adjustment factors from your actual historical data. If last year your January traded at £5,890 and your average week is £6,420, your January adjustment is -8%. That’s your factor.

Step 3: Layer In Confirmed Events

Quiz nights, match days, food events, and private bookings move the needle significantly. For Teal Farm, a full house Saturday with a big football match adds roughly 20% to baseline Saturday sales. A Tuesday quiz night with 40 people adds about 35% to a normal Tuesday.

Go through your calendar for the next 13 weeks and flag:

• Confirmed events (quiz, live sport, quiz league matches)
• Holidays and bank holidays (these disrupt normal patterns)
• School holiday weeks (if you have family trade)
• Known local events (festival, market, fair nearby)

Assign an estimated uplift to each. A quiz night: +30%. A match day with decent interest: +15%. A bank holiday Monday: +25%. These should be conservative estimates based on your actual experience, not optimistic wishes.

Step 4: Build Your Weekly Forecast

Now you combine all three factors: baseline × seasonal adjustment × event uplift = weekly sales forecast.

Example calculation for a week in March with a Friday quiz night:

• Weekly baseline: £6,420
• March seasonal adjustment: -5%
• Baseline after season: £6,420 × 0.95 = £6,099
• Friday quiz night adds £180 (estimate based on 30% uplift on Friday’s baseline £600)
• Adjusted weekly forecast: £6,099 + £180 = £6,279

That’s your forecast for that week. You now have a number that accounts for seasonal reality, day-of-week patterns, and actual events.

Step 5: Separate Revenue Categories (Wet, Food, Other)

If your pub serves food, food sales and wet sales have different patterns. At Teal Farm, wet sales are roughly 70% of revenue, food is 25%, and other (machines, events) is 5%. These don’t scale together—a quiz night lifts wet sales more than food sales.

Build separate forecasts for each category, then combine. This also helps you forecast pub drink pricing and food margins separately, which matters for margin planning.

Accounting for Seasonal Swings and Events

Seasonal forecasting is where most pub operators get it wrong. They assume linear growth or decline, when actual pub trading has sharp drops and spikes.

Bank Holidays and School Holidays

These are not normal trading days. A bank holiday Monday typically trades 20–30% higher than a regular Monday because daytime trade is busier and evening trade is later. Easter holidays shift the entire week’s pattern because family trade changes. Create specific multipliers for these.

Match Days and Sports Events

If your pub screens football, rugby, or other sports, match days move revenue significantly. A local club match day can add 15–25% to normal trading. A World Cup match adds more. You need to know your fixture calendar and adjust weeks accordingly.

Weather Impact (Real, Not Theoretical)

Wet-led pubs suffer when weather is poor—people stay home. Summer weather lifts garden trade significantly. Winter darkness drives evening pub trade. Look at your actual data: did a particular cold snap last year correspond with lower sales? That’s a real pattern to account for.

The most overlooked seasonal factor is this: most pubs trade lower in September because people go back to work or school, and holidays end. Your forecast needs to account for that. If September is historically 8% lower than August, build that in.

Monitoring and Adjusting Your Forecast

A forecast built in January will be inaccurate by March. Reality never matches prediction perfectly. What matters is whether you monitor the variance and adjust.

Weekly Monitoring

Every Monday morning, compare your previous week’s actual sales (from your till) against your forecast. Was it higher or lower? By how much? Document it.

If you forecast £6,279 and actual was £6,180, the variance is -1.5%—that’s noise, normal variation. If actual was £5,890, the variance is -6%—something changed. Was it weather? Did you run fewer events? Did a competitor open a promotion?

Track the reason for variance. Over time, you’ll see patterns in what throws your forecast off. That’s data you use to improve future forecasts.

Monthly Reforecasting

Every month, pull your actual year-to-date data and reforecast the remaining quarters. Your January-February data is now real. Use it to refine your seasonal adjustment factors. If February actually traded 10% higher than you forecast (because you were too pessimistic), adjust March forward.

This is the practice that separates landlords who actually control their business from those who react to surprises. You’re not trying to predict the future perfectly—you’re building better predictions by learning from actual results.

Cash Flow vs. Sales Variance

Sometimes sales are on forecast but cash flow is behind. This usually means payment timing is off—customers are running tabs, card payments are delayed, or supplier payments are bunching up. Use your pub profit margin calculator to track where cash flow gaps appear. Often it’s not a sales problem; it’s a working capital problem.

From Forecast to Cash Flow Planning

Sales forecast ≠ cash forecast. You can forecast £6,279 in sales and still run short of cash because of how that money flows in and out.

The Cash Conversion Gap

When cash actually lands in your account depends on:

• Payment method: Cash hits today. Card payments hit 2–3 days later. Tabs hit when settled (could be weeks)
• Supplier terms: Friday’s delivery might be paid 30 days later
• Wage timing: Wages paid weekly or monthly—plan around your actual payday, not average weekly cost
• Fixed costs: Rent, rates, insurance hit on fixed dates regardless of sales

A simple worked example: if you forecast £1,200 Saturday sales, that’s roughly £800 cash (rest card and tabs), but the £400 card payment doesn’t hit your account until Tuesday. Meantime, Monday is wage day (£1,100 payroll). You need £1,100 cash on Monday but only have £400 coming from Saturday. That’s a cash gap even though sales were fine.

This is why pub landlords with good sales forecasts still hit cash flow problems. You need a separate cash forecast that accounts for timing, not just totals.

Building a 13-Week Cash Forecast

Use your sales forecast as the starting point, then adjust:

• Separate cash inflows by payment method and lag time
• Layer in fixed costs (rent, rates, insurance on their actual due dates)
• Layer in payroll on its actual pay cycle
• Account for supplier payment terms (most are 30 days)
• Add seasonal working capital needs (higher stock before Christmas, etc.)

This shows you whether your forecast sales actually convert to available cash. If not, you need to plan financing or adjust working capital before the gap hits.

Talk to your bank or accountant about your forecast and actual cash conversion. Most will help you structure payment terms or facilities around these known patterns—but only if you show them the forecast.

Common Forecasting Mistakes to Avoid

After 15+ years working with pubs and building tools that 847 users rely on daily, I’ve seen these mistakes repeat:

• Forecasting growth without explaining why: “Sales will be 10% higher next year” is a wish, not a forecast. What changes to drive growth? More events? Better marketing? Extended hours? Without the mechanism, it’s a guess.
• Not adjusting for lost events: If a regular quiz organiser leaves or a match day venue changes, your baseline drops. Most forecasts don’t account for this.
• Ignoring competitor activity: A new pub opening nearby or a competitor running a strong promotion impacts your trade. Build this into monthly adjustments.
• Forgetting about staffing constraints: You can’t forecast revenue higher than your team can handle. If your busiest week maxes out staff capacity, that’s your realistic ceiling unless you hire more.
• Building one forecast instead of three: Separate forecasts for wet sales, food sales, and events mean you can run “what-if” scenarios. If you reduce quiz nights, you know exactly what wet sales drop to. That’s powerful for decision-making.

For more information, visit pub profit margin calculator.

For more information, visit pub staffing cost calculator.

For more information, visit pub IT solutions guide.