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We have access to more data than ever. It brings with it a wealth of opportunities – and a lot of noise. Restaurant analytics is key to filtering out the noise and uncovering the opportunities within that data; whether that’s spotting patterns in changing consumer behaviours, optimising menus, supporting staff or reducing waste. Data – and data analytics – provides restaurants of all sizes and maturity with a competitive advantage.
Data might be an invaluable tool, but its value depends solely on how it is used. Effective analytics allows businesses to learn more about their operations, removing the guesswork so they can optimise performance – and profits.
For restaurant operators, this means making sense of huge volumes of data. Every activity within a restaurant is a potential data point, whether that’s footfall, point of sale (POS) data or wastage. Restaurant analytics tools process all of this data to identify patterns and trends that restaurant owners and operators might not otherwise spot, providing actionable insights that can be used to improve every aspect of operations and make informed business decisions.
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With supply chain challenges, economic issues and an ongoing labour shortage; data is becoming increasingly important to operators. Restaurant analytics can be used to understand guest preferences and enhance operational efficiency, but it also opens up a wealth of other opportunities. For example, operators can leverage their data to tailor products to individual customers, meet sustainability goals, and ultimately provide a better dining experience.
Restaurant analytics is supercharging all aspects of restaurant operations, marketing and growth, and can be leveraged to:
The operational efficiencies created by restaurant analytics are far reaching and can have a significant impact on the bottom line – we’ll dig into those applications in more detail shortly. Operators that already have a degree of data maturity can build on those operational efficiencies to really boost profits with restaurant analytics tools.
Combining data points such as POS system data, customer satisfaction feedback, labor costs, employee insights and market trends shines a light on areas ripe for revenue growth. For example, this data can be used to identify complementary items, empowering staff to upsell these pairings and maximise both the average order size and sales revenue.
Pricing optimisation is another key application for restaurant analytics tools, particularly if operators are able to include third party sources – such as competitor pricing – in the analysis. Combining this data with demand and profitability insights allows restaurants to maximise revenue and profit margins by making informed pricing decisions.
It might sound like common sense, but a survey by McKinsey found that only half of restaurants collect price data on competitors and that’s generally limited to certain items. The opportunities presented by analysing competitor information is one very few operators currently capitalise on.
Restaurant analytics also allow for better understanding of consumer behaviour, which is particularly important at the moment when demand is volatile and difficult to understand. Pulling in broader insight from third party sources to augment your restaurant data is as helpful here as it is for pricing optimisation.
Local consumer demographics, weather data and even sentiment searches on social media can all help to provide insight into what consumers are thinking. This is valuable information and restaurant analytics enable operators to harness it to accurately forecast demand, adjust scheduling and manage restaurant inventory – optimising restaurant efficiency to boost profits.
Restaurant analytics tools enable restaurateurs and managers to really understand their guests; who they are, how frequently they visit, and what they order. This restaurant reporting in turn gives them the insight to maximise the guest experience, providing promotions with genuine appeal and developing a robust loyalty program.
Every customer creates a wealth of data that can be used to optimise the overall guest experience. That might be directly, through feedback mechanisms or indirectly through the choices they make when they visit. Harnessing this big data via a restaurant analytics platform creates a complete picture of demand trends and enables operators to launch offers and promotions tailored to specific customer segments.
For example, information on menu preferences can be combined with the times of a visit and point of sale (POS) data to tailor menus and create bespoke offers for your regulars. If a particular demographic indexes towards specific menu items and typically attend at the same times throughout the week, there’s an opportunity to run a limited offer during those times that appeals to them and makes their experience more enjoyable.
Tools like restaurant apps and loyalty cards support similar activations at an individual level, enabling operators to understand the preferences of specific customers and send hyper-targeted marketing campaigns their way, rewarding their loyalty and maximising their engagement.
Restaurant performance and customer data can be just as helpful when it comes to maximising the guest experience. Understanding how time to service fluctuates throughout the week or even month means managers can make sure they have enough staff scheduled to prevent guests waiting too long for their order.
The experience of your customers is largely dependent on the experience of your employees. A happy, engaged and committed workforce are motivated to go the extra mile for guests. They’re also far less likely to be poached by competitors.
Labour shortages are expected to continue for at least the next eighteen months, which means staff satisfaction and retention is top of the agenda for most employers. Managers are a key driver of the employee experience – nearly 60% of UK hospitality workers say a good manager is the main reason they stay in their role – and a good analytics tool can greatly enhance the experience of both managers and employees.
Staff member scheduling is a huge administrative burden for restaurant management, and 23% of them would like to see investments in technology to help ease this. Restaurant analytics, with the ability to quantify demand, takes a lot of the guesswork out of scheduling and frees up manager time. It also improves scheduling outcomes for staff, ensuring busy shifts are appropriately staffed.
But focusing on data collection brings with it a wealth of other benefits to staff. Feedback mechanisms make staff feel heard and empowered to contribute, while also providing useful insight operators can use to optimise both their experience and operational efficiencies. Similarly, performance data can be used to identify and reward high performers and ensure they’re well dispersed across shifts throughout the week to support more junior teammates.
The World Food Programme estimates that nearly a third of all the food produced is wasted before it can be consumed. That’s obviously not all related to the restaurant industry, but hospitality is a big contributor to food waste.
For most restaurants, there are a few common causes of food waste:
Data can be a helpful tool in inventory management and limiting waste. Restaurant analytics provides a better understanding of customer preferences and demand, and this is vital insight for restaurants routinely struggling with overproduction. Restaurant analytics tools that use past data to provide an accurate forecast of demand enable clear decision-making, allowing operators to adjust production levels and minimise waste.
Similarly, restaurant analytics can provide real-time data and insights into the ingredients that are being underutilised. Restaurants can then respond quickly, offering discounts or promotions on those menu items to sell them before they spoil, reducing the chance of food going to waste and freeing up storage for more popular ingredients.
Collecting data throughout shifts to understand what proportion of food is lost to spillage and incorrect orders is also useful. This information can be used to identify how much of a problem these issues are for particular venues and determine if training is necessary, or whether changes need to be made to the layout of the kitchen or menu itself.
Leveraging inventory data has huge potential for reducing food waste. It can also enhance operators’ ability to delight their customers. Restaurant analytics provide insight into demand and behaviours, enabling operators to optimise their orders and ensure the availability of popular menu items, preventing missed sales and disappointed customers.
Managing down labour spend is a major challenge for many restaurants. It requires a balancing act; operators need enough staff to cover demand, reduce wait times, and maximise the customer experience without scheduling so many staff that some are underutilised.
Many are turning to restaurant data analytics to maximise their labour efficiency. By assessing past demand and sales data alongside third party sets of data like weather forecasts, restaurants can more accurately predict peak times. This enables operators to move away from prescriptive scheduling – i.e., setting rules around the number of people that need to be scheduled at certain times of the day – and instead take a more tailored approach, scheduling staff based on likely demand patterns.
But the right number of staff is only half the equation, restaurants also need the right mix of skill sets to ensure they can cover every operational activity. With restaurant analytics dashboards, operators and managers can drill down even further into their data, looking at revenue forecasts within subcategories such as drinks, coffees and food.
This allows for more forecasting flexibility, and provides managers with the insight they need to understand demand and predict peaks across different areas of the business so they can ensure they have the right number of bar, serving and kitchen staff to optimise customer experience while maximising efficiency.
Restaurant analytics also gives operators the ability to collect information about individuals’ performance, enabling them to identify members that might benefit from additional training. This in turn can maximise the productivity of the whole team, optimising labour spend.
McKinsey highlights that the fundamentals of restaurant marketing haven’t changed as more data becomes available. Operators still need to build brands that resonate, and identify key moments throughout a customer’s journey that present opportunities for upselling or encourage repeat business. But the management consultancy does point out that digital marketing – and by extension data – has opened up an almost infinite number of routes with which restaurants can engage and market to customers.
Restaurant analytics provide granular, detailed insights into customer types, behaviours and preferences. All of which can be used to optimise targeting and reduce advertising costs. For example, a detailed profile of customers with data on their location, ages, likely occupations and socioeconomic status can be used to target ‘look-a-like’ audiences. These are groups of prospects that have characteristics in common with a restaurant’s typical customer.
By narrowing advertising and just targeting these groups, operators can maximise the impact of their adverts, since they’re more likely to appeal to this cohort than a broader cross-section of the public. It’s a common strategy for social media targeting, which in itself offers a host of opportunities to enhance engagement. Pizza Express, for example, achieved an 11% increase in revenue over 18 weeks with its ‘Spin to Win’ campaign, personalising rewards for customers via social media.
Loyalty programmes are a fantastic strategy for both collecting data for restaurant analytics and encouraging repeat customers. A 2022 study by McKinsey found that over a third of loyalty programme members would choose a brand over competitors, and half would be more likely to recommend it to friends. It’s little wonder then that loyalty programmes pay, often generating upsell opportunities through bigger order sizes and more frequent visits from members.
What’s your most profitable menu item? It’s a common misconception that the most popular item is a restaurant’s most profitable – this isn’t always the case. If managers routinely over order the ingredients for that item, for example, then the profit margins on it fall with every ounce of food wasted.
Restaurant analytics tools are incredibly helpful for gaining a better understanding of exactly what profitability looks like for each item on the menu. They can provide a clearer understanding of menu performance, from whether certain items appeal more to first time customers to which items are routinely ordered by repeat customers. It’s all invaluable insight that can be used to plan promotion strategies, optimise inventory and (of course) supercharge your menu.
Data on customer preferences and wider trends can be helpful when deciding what to add to the menu – as well as what to drop. It can be tempting to remove poor performing items altogether, but some may have untapped potential.
For example, a lower selling item may have an above average return rate for guests. With restaurant analytics, operators can clearly identify any trends like this, leveraging this data to improve the menu design and make these items more prominent, with tactics such as adding an image to the menu or improving the description.
Despite the obvious advantages, this is still a strategy employed by the minority. A survey by management consulting firm McKinsey found that two fifths of respondents had not evaluated items on their menu in the last two years, and nearly two thirds only reviewed pricing twice a year. Operators that fail to employ restaurant analytics to regularly review and optimise menus are effectively leaving money on the table.
Reducing labour costs and optimising the employee experience are two meaningful applications for restaurant analytics, but getting to grips with your data provides even more opportunities to maximise staff efficiency.
Every workforce will have individuals of varying experience and capabilities. High performers are typically easy to spot, and there is a tendency among managers to schedule these individuals for the busiest shifts since they know they can be trusted to maintain service levels in challenging situations. This can lead to burnout and disengagement among top performers, and negatively impacts their retention rate.
With restaurant analytics, managers can instead leverage data points such as time to service, guest satisfaction and even the total value of tips for each shift to better understand the effectiveness and performance of specific teams on specific days. This insight can then be used to optimise scheduling, ensuring restaurants have the right mix of experience levels scheduled for each shift to ensure the best performance, without putting unnecessary pressure on specific individuals.
Self-service tools are great for data collection and, together with improved analytics and KPIs (key performance indicators), are giving rise to a host of data-driven growth strategies, enabling operators to make informed decisions about menu changes and promotion, as well as staffing more effectively.
Quick service restaurants (QSR) in particular are well positioned to make use of innovations such as self-ordering technologies. These can free up staff for food preparation, serving and clearing, reducing the number of employees needed even at peak times. They also help to reduce incorrect orders and minimise waste, and can encourage customers towards upsells or promotions.
Multi location restaurants bring with them their own set of challenges. Whether that’s ensuring consistency across venues, managing complex supply chains, or hiring for multiple sites, operations get exponentially more challenging with each new addition.
Costs vary widely between locations and there are likely to be nuances in how each of the locations run. It’s increasingly common for restaurants to offer a variety of store formats tailored to location; one site may need more seating, while another is set up mainly to provide takeaways.
It is common for restaurant operators with little or no experience of restaurant analytics to keep their data siloed. Inventory, marketing and point of sale data might all be recorded in different systems. This makes it almost impossible to gain a full overview of business operations, and is one of the reasons why analytics tools deliver value almost straight away – for many, this is the first time they’ve connected siloed data and been able to spot broad trends.
For operators with multiple locations, the potential to fractionalise data is amplified. Different teams in different locations might have very different systems.
Restaurant analytics tools that can unite data from multiple teams across multiple locations are therefore a necessity to multi site operators. Multiple sites create more data, putting operators in a position to identify and capitalise on emerging trends far more quickly than a single location restaurant would be able to. This additional data can also be used to improve forecast accuracy, which in turn allows operators to further refine inventory and scheduling.
While owned data is essential for maximising the value operators get from their restaurant analytics tool, third party data sources are just as important. These ensure operators have full oversight of how external factors such as weather and events impact demand, enabling them to make more informed decisions at every level of the business.
Alongside optimisation, the other major benefit of restaurant analytics is the ability to spot trends as they emerge and respond quickly to them. This has become particularly valuable over the last few years, when external factors such as the lockdown restrictions and the cost of living crisis impacting customer preferences on an almost monthly basis.
Price is now an important driver in consumer behaviour and operators with a good analytics tool are well positioned to notice small changes – such as reduced order amounts or skipped side dishes – almost immediately. They can then respond to this quickly with dynamic pricing or by adding more economical dishes to their menu.
These operators can identify much bigger shifts in consumer behaviour quickly as well. We know from Fourth’s latest Hospitality Workforce Report that consumers aren’t cutting back on takeaways, even though 63% say they’re eating out less this year. With a robust restaurant analytics tool, operators that cater to both the dining in and takeaway markets would have been able to spot this trend as it emerged, allowing them to scale back restaurant staff and increase back-of-house staff, maximising efficiency.
One of the best uses for restaurant analytics dashboards are their utility in unearthing trends and patterns operators have never noticed before. By analysing huge data sets, and leveraging technologies like machine learning, restaurant analytics platforms can provide additional insights that would have been tricky or even impossible to determine manually. They help to identify blind spots and challenge assumptions – such as the specific number of staff you need for opening and closing – that could be creating inefficiencies.
Aside from rent, food and labour are the main expenses for operators. A slip up on one or both will have a significant impact on the bottom line. Yet, historically, the costs and performance associated with food and labour have been reviewed infrequently and in arrears – an operator might assess performance once a month or even once a quarter. This means that, if there is an issue, restaurants can lose revenue for weeks or months before noticing it.
By contrast, many restaurant analytics tools allow for near real-time assessment. Dashboards automate this analysis, so operators and managers can see how demand and efficiency fluctuate on a daily or weekly basis, and identify any issues before they have a chance to negatively impact the business. For example, a short-term sales decline might go unnoticed on a quarterly basis, but reviewed weekly it’s easy to identify the trend and its cause – maybe staff illness led to a decrease in speed of service. With restaurant analytics, operators can see problems, investigate, and remedy them quickly.
Data collection offers opportunities for personalisation, gamification and rewards. Loyalty programmes create channels by which operators can capture data at an individual level – they can be used to determine where customers live, their age, the items they prefer and how frequently they visit. But they have benefits that reach far beyond just data collection.
Loyalty programmes offer customers discounts and added value – perhaps they get a free item every fifth visit or personalised discount codes based on their purchase history. These rewards can drive upsells and repeat purchases.
They also generate data that can be further leveraged for restaurant analytics. Do customers routinely purchase a particular product, but opt for a more premium item for their reward? If this premium product is a slow seller, this could indicate that its current price is prohibiting sales.
Similarly, an individual’s data can be connected to other touch points throughout the business. For example, if a reservation is frequently made by a particular email address or phone number and that customer commonly fails to show, operators can take measures to mitigate that one occurrence without having to apply blanket policies that might deter new business.
Not every application of restaurant analytics to customer loyalty needs to be high tech. A better understanding of what’s happening on the restaurant floor could be as simple as recognising that the same couple visit on the same day every month. Having their favourite wine ready on the table for them, or offering a free drink, creates a special experience that encourages loyalty and enhances their enjoyment of your restaurant.
The turmoil of the last three years have amplified the need for operators to really understand the nuances of their businesses so that they can maximise efficiencies. Against this backdrop, restaurant analytics has grown in importance and is rapidly becoming an essential tool for operators of all sizes.
Advanced analytics from Fourth is designed specifically for the hospitality sector. Our business intelligence solutions help operators to connect disparate data from across their organisations into a single restaurant analytics dashboard, giving leaders and managers access to real-time insight quickly and easily.
Third party data sources are just as important as owned data, particularly when it comes to forecasting. These data sources mean operators can understand how factors such as the weather or major sporting events impact their footfall. Fourth’s AI forecasting capabilities automatically combine relevant third party data with an operator’s own historic data to project demand. This means operators have a clearer idea of when peaks will occur and can confidently manage scheduling, inventory, production and prep – minimising waste and optimising efficiency in the process.
Leading London restaurant chain Comptoir Libanais relies on multiple solutions from Fourth to optimise its business operations. Click here to read Comptoir’s restaurant analytics case study.
“Our franchise partners need to know the financials before making a decision to franchise with us. Before Fourth, we only had estimates. Now we deliver up-to-date figures.”
— Adil Loudiyi, Food, Beverage and Systems Controller, Comptoir Group\
Make informed decisions with Advanced Analytics by Fourth. Get the power of your data to drive smarter performance with actionable insights.
The complexity of the environment restaurants operate in has increased exponentially over the last few years. Margins are tighter than ever and operators can’t afford to over staff restaurants or waste resources.
The importance of restaurant analytics has grown rapidly in response. These tools are now essential, and allow restaurants to unite data from across their business to gain a better understanding of operations, identify trends and maximise efficiencies.
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