A Guide to Conducting Precise Restaurant Sales Forecasts

Sales forecasting is always tricky in the restaurant world. Partly, this is because the industry is inherently unpredictable. Under normal circumstances, you could use previous years’ sales figures to forecast the upcoming year – but, as we emerge from two years of COVID closures, staffing shortages, and general turmoil, previous sales’ figures definitely can’t be trusted. 

For many restaurants, forecasting upcoming sales in a post-pandemic world is just as hard as forecasting sales for an entirely new venture.

So, perhaps it’s worth going over exactly how you’d forecast sales for a new venture.

Let’s follow the case of a fictional restaurateur. Her name is Chloe, and her dream is to open a bistro by the sea. She’s picked what could be the perfect spot – a recently vacated cafe on the seafront. She hopes to renovate it and turn it into the fish bistro of her dreams, catering to happy beachgoers all year round. 

However, before she can do this, she has some startup costs. She needs to upgrade the property, get the relevant certificates, and so on.

And before she can do any of that, she needs a business loan.

Chloe’s plan is charming, and she’s very enthusiastic – but lenders do not deliver on charm and enthusiasm alone. They will want to see returns on their investment. So, Chloe has to present them with a sales forecast.

Forecasting restaurant sales isn’t always easy. There are a whole host of factors that can come into play like economic conditions, the weather, eating trends, or even our old friend the pandemic. 

All in all, you can’t rely on things like an average ecommerce conversion rate when forecasting sales for a restaurant.

However, our Chloe is no amateur. She has a background in restaurant management, so she already has a decent understanding of the industry and market she’ll be working in. She’s confident that she can come up with a reasonably precise sales forecast.

How does she do that? Let’s take a look:

1 – Capacity Calculations

First, Chloe sits down and works out her baseline capacity. That is, the average amount she should be able to take each day. 

Chloe’s bistro will be opening for drinks and small lunches in the daytime and providing a limited number of hot meals (booking only – it’s a small place!) in the evening. 

She has faith in her food and her staff, and she’s good at things like outbound lead generation, so she’s confident that she can build up a loyal following. But she needs more than confidence to take to the lenders. So, she starts doing some calculations.

Assuming that 80 percent of seats are filled for both sittings and that each customer orders something of average price, Chloe can establish a rough baseline calculation for a day’s trading. She can then multiply her day’s trading average by the number of typical workdays in a month to reach an average monthly capacity estimate.

She can also add in the cost of additional extras, like puddings, side dishes, and so on, into her baseline capacity calculation – whether she does so or not depends on how hard she intends to push them

Now, these estimates are all very good – but what’s to say that Chloe didn’t pluck the figure of 80 percent of seats filled from thin air? What’s she basing her estimates on?

Well, it’s largely educated guesswork.

Luckily, the banks understand that educated guesswork is the best tool at Chloe’s disposal when it comes to a baseline capacity calculation. And, as Chloe knows the industry well, her educated guesses are more educated than most.

However, there are things Chloe can do to make her forecast a bit more precise and, therefore, a bit more appealing to lenders.

2 – Expectation Adjustment

If you’re experienced in the restaurant industry, you’ll have immediately spotted a problem with Chloe’s baseline capacity calculations: not all days are alike. Not even close.

For example, during the summer, Chloe’s seaside bistro is likely to be a lot busier than during the winter. Similarly, she will likely do more trade on weekends than on workdays. And certain holidays (Valentines Day, for example) may be busier than average, while others (Christmas, for example) will leave the bistro empty.

This is where adjustments come in.

To get an idea of when she can expect the most custom, Chloe buries herself in market research. She digs out year-on-year trends for restaurants in the area and studies average monthly takings for her closest competitors.

Chloe’s offering is not identical to those of the other eateries along the seafront, but that doesn’t matter. What she’s looking for here isn’t precise numbers – it’s things like footfall estimates, the amount of passing trade, the busiest and slowest times, and so on. She can use things like the local chamber of commerce statistics, area research, competitor research, and even good old-fashioned observation to draw accurate conclusions.

Using all of this, she can start to get a bit more precise with her baseline calculations. For example, if she discovers that the beach is busy on a Saturday afternoon but virtually empty on a Monday, she can adjust her day-by-day calculations accordingly to come up with a more tailored average weekly calculation.

Then, she has to bring this to bear on her monthly baseline calculation. Which means it’s time to get even more precise.

3 – Predicting the First Year

Just as each day in the week is different, each month is also different. Chloe will have to adjust her monthly calculation to account for the complexities of each individual month. This is particularly important for the first year.

For example, even if Chloe opens on the busiest potential day in summer, it’s likely that her bistro will take a while to get established. So, for the first few months, she will need to lower her baseline calculation to account for this.

Then she will have to take the particulars of each month into account. For example, February might see a spike in trade over Valentine’s Day, while September is notoriously slow for restaurants all over the world.

At this point, it’s wise to start getting techy. Luckily, Chloe is a bit of a geek. She loves a spreadsheet and regularly browses the B2B ecommerce sites for good restaurant software. 

Opening up her tech deck, she inputs her estimates into a sales forecasting template. She adds things like average prices, specific prices (for example, she plans to do a set Valentines menu, so adds this into her February row), overheads, soft and hard costs, footfall estimates, and so on. 

She gets pretty granular as she works, accounting for things like increased heating overheads in winter months and increased staff costs during busy months. 

However, she doesn’t get so granular that she’s accounting for every tiny thing. For example, rather than listing “fish pie,” “chicken sandwich”, etc., for her daily lunchtime menu, she just puts “lunch” and an average price. Her sales forecast doesn’t have to account for every little detail – it’s a big picture kind of deal.                                                     

Once she’s got her annual sales forecast/estimate, all Chloe has to do is convince her lenders to pay up. We’ve got confidence in her – she’s smart, and she’s got a great business plan. They’re bound to love her.

Her forecast isn’t just useful for wowing lenders, though. It’s got a lot of applications beyond that. For example, if she’s not sure how much inventory to order in June, a glance at her sales forecast could prevent her from over-ordering or under-ordering. The same goes for things like seasonal staffing.

If she’s feeling clever, Chloe could even factor things like seasonal produce into her forecast. After all, she’s pretty focused on fish, and the catch of the day is likely to change a lot with the seasons. Sometimes, she’ll be paying more, sometimes less – so her sales forecast could help her a lot in working out whether or not the cost of certain catches will be repaid in sales.