Restaurant sales

A Guide to Accurate Sales Forecasting for Restaurants | modern restaurant management

Sales forecasting is always tricky in the restaurant world. This is partly because the industry is inherently unpredictable. Under normal circumstances, you can use sales figures from previous years to predict the year ahead – but, as we emerge from two years of COVID shutdowns, staff shortages and general unrest, sales figures previous ones are certainly not reliable.

For many restaurants, predicting future sales in a post-pandemic world is just as difficult as predicting sales for a brand new business.

So, maybe it’s worth revisiting exactly how you forecast sales for a new business.

Let’s follow the case of a fictional restaurateur. Her name is Chloé and her dream is to open a bistro by the sea. She chose what could be the perfect place: a recently liberated cafe on the seafront. She hopes to renovate it and turn it into a bistro. of fish of his dreams, welcoming happy swimmers all year round.

However, before she can do that, she has start-up costs. She needs to upgrade the property, get the relevant certificates, etc.

And before she can do anything, she needs a business loan.

Chloe’s plan is charming and she’s very enthusiastic – but lenders don’t just deliver on charm and enthusiasm. They will want to see returns on their investment. Chloé must therefore present them with a sales forecast.

Forecasting restaurant sales is not always easy. There are a whole host of factors that can come into play such as economic conditions, weather, food trends or even our old friend the pandemic.

Overall, you can’t rely on things like an average e-commerce conversion rate to predict a restaurant’s sales.

However, our Chloé is not an amateur. She has a background in restaurant management, so she already has a good understanding of the industry and market she will be working in. She is confident that she can make reasonably accurate sales forecasts.

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

1 – Capacity calculations

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

Chloé’s bistro will be open for drinks and breakfasts during the day and will offer a limited number of hot dishes (reservation only – it’s a small place!) in the evening.

She’s confident in her food and her staff, and she’s good at things like outbound lead generation, so she is convinced that she can build customer loyalty. But she needs more than trust to approach lenders. So she starts doing calculations.

Assuming that 80% of the seats are taken for both sessions and that each client orders something at an average price, Chloe can work out a rough baseline calculation for a trading day. She can then multiply her day’s trading average by the number of typical working days in a month to arrive at an average monthly capacity estimate.

She can also add the cost of extra extras, like puddings, side dishes, etc., into her base ability calculation – whether or not she does this depends on how hard she intends to push them.

Now, those estimates are all pretty good – but what about Chloe not snatching the 80% seat-filled figure out of thin air? What does she base her estimates on?

Well, that’s largely an educated guess.

Fortunately, Banks understands that educated guesses are the best tool Chloe has when it comes to calculating baseline capacity. And, since Chloe knows the industry well, her educated guesses are more educated than most.

However, Chloe can do a few things to make her predictions a little more accurate and, therefore, a little more attractive to lenders.

2 – Adjustment of expectations

If you’re experienced in the restaurant business, you’ll immediately spot a problem with Chloe’s base capacity calculations: Not every day is the same. Not even close.

For example, during the summer, Chloe’s seaside bistro is likely to be much busier than during the winter. Likewise, it will likely trade more on weekends than on business days. And some holidays (Valentine’s Day, for example) can be busier than average, while others (Christmas, for example) will leave the bistro empty.

This is where the adjustments come in.

To get an idea of ​​when she can expect the most personalization, Chloe dives into market research. She unearths year-over-year trends for area restaurants and studies the average monthly revenue of her closest competitors.

Chloe’s offering isn’t the same as other beach front restaurants, but that doesn’t matter. What she’s looking for here aren’t hard numbers – they’re things like footfall estimates, amount of trade passing through, busiest and slowest times, etc. She can use things like local chamber of commerce stats, area research, competitor research, and even good old-fashioned observation to draw accurate conclusions.

Using all of this, she can start to get a little more precise with her basic calculations. For example, if she discovers that the beach is busy on a Saturday afternoon but mostly empty on a Monday, she can adjust her day-to-day calculations accordingly to arrive at a more personalized average weekly calculation.

Then, it must take this into account in its monthly base calculation. Which means it’s time to get even more specific.

3 – Predict the first year

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

For example, even if Chloe opens on the potential busiest day of the summer, it’s likely that her bistro will take a while to get established. Thus, during the first few months, she will have to reduce her basic calculation to take this into account.

Then, it will have to take into account the particularities of each month. For example, February could see a spike in trade during Valentine’s Day, while September is notoriously slow for restaurants around the world.

At this point, it’s wise to start getting technical. Fortunately, Chloe is a bit of a geek. She loves spreadsheets and regularly scours B2B e-commerce sites for good restaurant software.

Opening her technical platform, she enters her estimates into a sales forecasting model. She adds things like average prices, specific prices (for example, she plans to do a set menu for Valentine’s Day, so she adds that in her February row), overhead, overhead, and indirect, attendance estimates, etc.

It gets pretty granular as it works, taking into account things like increased heating overhead during winter months and increased staffing costs during busy months.

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

Once she has her annual sales forecasts/estimates, Chloe just has to convince her lenders to pay. We trust her – she’s smart and she has a great business plan. They are bound to love him.

However, his predictions aren’t just useful for wowing lenders. It has many applications beyond that. For example, if she’s not sure how much stock to order in June, a look at her sales forecast might prevent her from over-ordering or under-ordering. The same goes for things like seasonal staff.

If she’s feeling smart, Chloe might even factor things like seasonal produce into her predictions. After all, she is pretty focused on fish, and the catch of the day is likely to change a lot with the seasons. Sometimes she will pay more, sometimes less – so her sales forecast could be a big help in determining whether the cost of certain catches will be reimbursed by sales or not.