Restaurant Data Analytics – A Comprehensive Guide

In 2024, the restaurant industry is in the middle of a restructuring fuelled by data, AI, and analytics. The sector is favoring data-driven approaches and technologies that power up data analytics. The data-first mindset in the restaurant industry is gaining traction, especially after 2020. Today’s restaurant businesses are trying to find avenues to acquire insights such as restaurant location data, restaurant delivery analytics, and many more to decide future actions. This shift toward restaurant data analytics is healthy and proving to be highly profitable for restaurant owners relying on restaurant analytics to make key business decisions. The rise of third-party food delivery apps, cloud kitchens, and take-out-only restaurants (ghost kitchens) in the last few years points towards the shift from dining to delivery. To capitalize on this trend, even traditional restaurants have joined the delivery platforms to start online food deliveries. Many restaurant chains have started their own online platform where customers can place orders for online food delivery. However, amidst this evolution in the restaurant industry, all industry participants such as restaurant owners, food delivery apps, ghost kitchens, and restaurant chains require restaurant analytics to keep themselves ahead of the competition. From fine-tuning their food menu as per current customer demand to creating loyalty programs that match competitor offerings, restaurant data analytics offer insights that are critical for success in the restaurant industry. In this guide, we will cover all aspects of restaurant data analytics, including its meaning, key metrics, collection sources, extraction methods, benefits, use cases, and best practices.

What is Restaurant Analytics?

Restaurant analytics are numbers or ratios in the form of key performance indicators (KPIs) that provide insights into the core processes of the restaurant business. These metrics or analytics include data insights that restaurant owners can use for making improvements in their business or making investment decisions. For instance customer food preferences analytics. Suppose a newbie restaurant wants to know the top 5 dishes that it should list on its website or a third-party food delivery partner app. It will need data that reveals the top 5 dishes that are ordered the most online from competitor restaurants listed on popular food and restaurant aggregator apps that serve the locale ((say regional restaurants in a radius of 5-10 miles). With such data in hand, the newbie restaurant can create targeted menus that resonate with the customer base in that particular locale. The restaurant can also create targeted marketing campaigns to promote those top dishes or cuisines that align with their customers’ tastes. The restaurant can make sure to keep inventory adequate to cook those top dishes. This will also reduce waste as the newbie restaurant can plan to eliminate some items that are unpopular and rarely ordered. Now, the above is just one example of how restaurant analytics work. With the right tools and restaurant data analytics solutions, restaurant businesses can track several important metrics to keep their business ahead of the competitors.

Top Restaurants Data Analytics and Metrics to Track

Restaurant businesses must track the below analytics and KPIs to gain a competitive advantage, achieve higher profit margins, and increase resilience to disruptions.

#1 Competitor Restaurant Analytics

There were 156,715 single-location full-service restaurant businesses and 349,000 chain restaurant businesses in the US alone in 2023. The competition is extremely high in the restaurant industry which is growing at 13.6% CAGR. By examining competitor details, a restaurant can know how many restaurants operate in a particular region, how many have dine-in and takeout facilities, how many only have takeout facilities, etc. The data also reveals direct and indirect competition among restaurants in a particular region. Direct Competition refers to restaurants offering the same dishes to the target market. In the restaurant industry, examples of direct competition include McDonald’s and Burger King: Both are fast-food chains offering similar types of food (burgers, fries, etc.) and targeting similar consumer demographics. Indirect competition involves restaurants offering different dishes but targeting the same customer preferences. For example, Subway and Jamba Juice: While one offers sandwiches and the other gives smoothies and healthy snacks, they both cater to health-conscious consumers looking for quick, convenient options. Knowing the competition that you will face when operating your restaurant is critical to building strategies to beat the same.

#2 Menu Analytics

Competitive menu analytics means identifying and analyzing competitors’ menus (dishes offered, best sellers, specialties, etc).  This data will help a restaurant to tailor its menu to meet local demand, offer everything that competitor restaurants serve, and add more items to keep their menu better than the competitors.

#3 Promotional Analytics

This restaurant analytics focuses on finding the current offers, promotions, discounts, and loyalty points competitor restaurants offer. An analysis of competitor’s combo offers, free meals, complimentary items, etc. will help a restaurant business implement its own promotions and loyalty programs. 46% of US diners are a part of a loyalty program.

#4 Marketing Analytics

People today search online before ordering from restaurants or visiting them for dine-in. Social media suggestions, videos by food influencers, and digital marketing campaigns by restaurants impact footfalls and orders. Restaurant businesses need to analyze these marketing campaigns and strategies to guide their marketing efforts.

#5 Pricing Analytics

Competitor pricing analysis ensures that pricing is not discouraging customers from ordering. Determining the best pricing for menu items and understanding the cost and profit margin of each dish helps in setting prices that maximize profits while being acceptable to customers. Prices affect how customers perceive a restaurant. Analytics help in setting prices that match the restaurant’s desired brand image.

Perhaps that’s because variable pricing is as much a part of the business as knives and forks, says Peter Romeo, Editor at Large for Restaurant Business.

#6 Review Analytics

Review analytics provide insights into competitors’ strengths and weaknesses. Analyzing competitor reviews from sites like Yelp, Open Table, Gayot, Google Reviews, Deliveroo, and Foursquare can help restaurants understand customer preferences, such as specific menu items that receive high praise or aspects of the dining experience that customers appreciate. This knowledge can guide menu planning and service improvements. Similarly, when customers pinpoint weaknesses, such as complaints about food quality, slow service, or cleanliness issues, it can be used to take corrective actions.

#7 Dish Analytics

Dish analytics provide a glimpse into the performance, popularity, and profitability of the items on a restaurant’s menu.  It means tracking which dishes are selling well and which are not. Example: Data shows that a competitor’s most ordered item is a specific type of burger. This knowledge can influence the restaurant’s menu decisions, potentially leading to the introduction of a similar popular item or a unique variation. Identifying the food items with poor reviews from competitors’ menus can offer valuable information to avoid potential pitfalls. This analysis can guide the restaurant in refining its menu. For example, a competitor’s specific dish consistently receives negative feedback for being overcooked. This prompts the restaurant to ensure that its own dish is cooked at an optimal level.  Understanding how different dishes perform during various seasons or events can help in seasonal menu planning.

#8 Delivery Analytics

Delivery analytics reveal how much time competitors take to deliver the ordered food. For instance, if competitors consistently deliver orders within 30 minutes, even during peak hours, you will know you have to match that delivery speed to stay competitive.

# 9 Customer Segmentation Analytics

Customer segmentation analytics is the process of dividing a restaurant’s customer base into distinct groups based on various criteria such as behavior, demographics, preferences, and spending patterns. Customers can be segmented by their location, and behavioral factors including dining frequency, spending patterns, menu preferences, and responsiveness to promotions. By understanding the different needs and preferences of each segment, restaurants can create more personalized experiences.

#10 Online Ordering Analytics

With the increasing trend of online food ordering, monitoring competitors’ online ordering platforms, user experience, and delivery accuracy can help enhance the restaurant’s own online ordering system to meet or exceed customer expectations and stay competitive in the digital marketplace.

#11 Location Analytics

Location analytics can help determine the saturation of restaurants in an area and potential market size. Restaurant location data analytics help in identifying areas with high/low competitors. Such analysis can help aspiring restaurants select the best location for their new restaurant. The geospatial analysis not only helps identify the best areas for opening new outlets but also assists restaurants in understanding local preferences to customize menus. Restaurants can create service offerings to match regional tastes and dietary preferences.

KPIs to Monitor For Restaurant Data Analytics

KPIs have a direct impact on restaurant businesses. The key performance indicators are calculated via data analytics and present multiple aspects of the restaurant business like their financial performance, sales, operations, customer satisfaction, and delivery metrics, in easy-to-understand form. For Example, food cost percentage. This analytics reveals your ingredient costs as a percentage of the revenue those ingredients produce. This means, that if ingredients costs are $5 for a dish and revenue obtained from selling the dish is $8 then the food cost percentage will be $5/$8*100 =62.5%. A high percentage is negative for business profits. The ideal percentage in the restaurant industry is considered somewhere between 25% to 40%.

Top KPIs to Derive and Monitor with Restaurant Data Analytics:

How to Gather Data For Restaurant Analytics?

Restaurant businesses have multiple avenues, touchpoints, and platforms from where they can collect data which they will use for analytics purposes. The best ways to gather data sets for restaurant analytics are discussed below:

#Web Scraping

If you are a newbie restaurant and want to collect data for restaurant analytics, you will have to use restaurant web scraping solutions. This means you will have to scrape the websites of third-party food delivery platforms, competitor restaurant websites, and competitor social media channels to find the relevant data. Restaurant data analytics solution providers can web scrape publicly available data from the above-mentioned sources and gather valuable insights for you. Even if you are an established restaurant business, web scraping can help you collect real-time data from relevant sources to keep yourself ready for dynamic changes in the industry. Third-party Food Delivery Platforms: These are apps like food delivery services that are not owned by the restaurant but can be used to order food from them. Restaurants can learn from the ordering habits of customers on these apps. By scraping sites like DoorDash, Uber Eats, GrubHub, and Postmates, a restaurant can find data related to menu, pricing, popularity of dishes (based on reviews), and delivery time and delivery areas of competitors. By looking at what people are ordering, a restaurant can figure out which dishes are hits. Seeing the prices of items from different restaurants on the delivery app can help a restaurant position its pricing competitively. Competitor Restaurant Websites: Scraping competitors’ websites can provide data related to their menu, prices, offers, promotions, specials, dining space, booking methods, etc. Competitor Social Media Channels: Social media is a rich source of customer sentiment and trending topics. Scraping data from these channels can reveal how competitors engage with their audience (posts, reels, images, comments, and promotions. Today most people post about their restaurant visits on social media channels. Reviews on social media and posts about competitor restaurant websites by customers can be a good source of data for restaurant analytics. Publicly Available Reviews: Online reviews from sites like Yelp, TripAdvisor, and Google are valuable for sentiment analysis. Also, reviews on restaurant’s own websites can be helpful for understanding customers’ likes and dislikes.

#POS Systems

Restaurant Point of Sale (POS) systems are not just for processing transactions; they’re a goldmine of data. Restaurant owners can learn a lot about their customers by looking at the times and details recorded when customers make a purchase or reservation via POS systems. This information can tell them which restaurant location customers like best, when they like to eat etc. So, if a restaurant has many branches in a city, they can use this information to understand and serve their customers better at each location. Modern POS Systems can Track:

• Sales data: Detailed records of customer bills, what they ordered, at what time (breakfast, lunch, dinner), and in what combination.
• Customer data: POS has systems for keeping customer profile details like their billing history with your restaurant, and preferences, It can record if the food was ordered online or dine-in, etc.

Note: You can collect POS data of only your own business and not of your competitor.

#Inventory Management Systems

Inventory systems can be a precious data source for restaurant data analytics. Inventory data analysis can reveal trends in ingredient usage and costs. Restaurants can identify items that are being wasted or determine the profitability of each menu item by analyzing the cost of ingredients versus the selling price. However, this data is not publicly available, so competitor tracking is not possible. You can only use the data that you collect for your own restaurant business.

#Geotagging

Geotagging is a way for restaurant owners to reach out to customers by using their GPS location. When a customer who has the restaurant’s app on their phone is near the restaurant, the restaurant can send them a special message or offer. Restaurants can set up a virtual boundary in the area around them using GPS technology. When someone enters this area with their smartphone, the restaurant can send ads or promotions (Geofencing for ads) to their phone. Geoconquesting can be used to beat competitors. This is when a restaurant sends deals to people who are at or near a competing restaurant, trying to get them to come to their place instead.

Other Sources:

Online Reservations and Feedback Forms: Collecting data from online reservations helps understand customer preferences, dining times, and frequency of visits, while feedback forms provide direct insights into customer satisfaction and areas for improvement.

How Important Are Restuarant Analytics?

“Businesses that take the time to collect and interpret market data have a competitive advantage over their competitors who do not”, concludes an IJCERT Study. Restaurant data analytics are a cornerstone of personalized customer experiences. Restaurants looking to deliver tailored dining experiences or custom food deliveries to their customers can benefit from restaurant data analytics. The strategic application of data analytics in restaurant business operations presents a significant competitive advantage. From dynamically adjusting menu prices to designing targeted marketing campaigns, insights gained from data analytics enable restaurateurs to drive success in everything they do.

Restaurant Data Analytics Help Restaurant Operators in the Following Ways:

1. Menu Optimization

Restaurant operators can use data analytics to know which dishes sell more on various food delivery platforms, competitor restaurants, and also at their own restaurants. With this analysis, they can keep the menu that brings more money and discard the unpopular one. By cutting the losers and rarely ordered items from the menu, the restaurant will not need to spend on ingredients that no one wants.  This menu optimization by discontinuing dishes that do not bring revenue increases profit margins for restaurants. There are many restaurant analytics case studies about menu data scraping helping restaurants to make informed decisions. Restaurants can use data analysis to come up with menus that appeal to their customer’s preferences and which are rightly balanced. With Data Analytics Restaurants can understand what dishes are liked by many people and what ingredients are mostly required then they can design a menu that will cater to all tastes.

2. Implementing Strong Loyalty Programs

Restaurant data analytics provide a clear idea of how your competitors and even third-party restaurant aggregator platforms are delivering promotions. By knowing their reward schemes, loyalty point mechanisms, membership benefits, discount coupons, and vouchers, a restaurant business can develop a better loyalty program. For instance, if the competitors offer 100 loyalty points on a bill of $100 at a restaurant dine-in or takeaway, the restaurant can offer either the same value or more to attract customers.

3. Prevent Food Wastage

Data-driven strategies can help restaurants identify popular items and those that are not consumed more. This analytics allows restaurants to prevent overstocking items that are not ordered. By accurately predicting the demand for dishes, restaurants can prevent the wastage of ingredients that are not used in food items.

4. Better Forecasting

By leveraging restaurant data analytics, businesses can gain valuable insights into customer preferences, peak dining times, popular menu items, and sales trends. This rich data set enables restaurant owners and managers to accurately forecast future demand and customer behavior. Data analytics dissect vast amounts of sales data to identify patterns related to seasonal demand, promotional effects, and even the impact of external factors such as weather or local events to predict footfalls or takeaways.

5. Competitive Advantage

With so many restaurants offering delivery services, customers have many choices, picking from many different options right from their phones. You must develop a competitive moat to differentiate your business from peers. Restaurant data analytics will help you streamline operations, optimize resource utilization, personalize your menu, etc., and will lay the groundwork for sustained success.

6. Improves Kitchen Workflow

Data gathered from various touchpoints, such as point-of-sale systems, reservation platforms, and customer feedback channels, can help restaurant managers determine peak times. This will enable restaurants to allocate staff according to requirements (e.g., avoid understaffing during rush hours or overstaffing during quieter times). Data Analytics optimizes staffing levels and ensures that there are enough waiters at each table during peak hours. This results in shorter waiting periods, which makes customers leave satisfied!

7. Saving Money

Identifying areas of overspending in restaurants and cost-cutting measures is another way in which data analytics can be used to save money, e.g., controlling ‘Expenses’ within kitchen management. By evaluating sales trends and operational costs, restaurants can aim to reduce waste while still maximizing profits for healthy financials.

Challenges in Using Data Analytics in Restaurants

Data is abundant in the restaurant industry, but only a few players possess the correct methods and strategies of analysis. Only a few restaurants use data analytics to identify trends. Restaurants collect data from multiple sources, including the supply chain, employee software, inventory management, point-of-sale systems, and customer feedback. For several reasons, this data is rarely used.

• Not Knowing the Benefits: Many restaurant owners don’t understand how data can help their business, which prevents them from conducting data analytics.
• No Clear Data Plan: Restaurants need a plan for collecting, storing, organizing, and using their data. While having data at one’s disposal is beneficial, it doesn’t fulfill its purpose if it doesn’t contribute to improved operational decision-making.
• No way of deriving insights: The data must deliver actionable insights. If the information is not presented in an easily digestible format, the sheer quantity of data will get overlooked or discarded.
• Outdated Tech: Legacy systems for extracting data do not work in modern times. When data is generated at many touchpoints, you need the latest tech to scrape and combine it.
• Scalability: As a restaurant grows, its data analytics systems may struggle to keep up with the demand. Restaurants need a system to continuously collect and use data for real-time analytics.

Solution: If you are moving in the unknown trajectory of restaurant data analytics, getting help from one of the best restaurant data analytics companies to navigate without hassle is better.  Restaurant data analytics solutions providers like FoodSpark.io have years of expertise to guide you through deriving valuable restaurant analytics.  First off, figure out what you want to achieve with data. Whether making customers happier, managing your inventory better, fine-tuning your menu, or boosting profits, knowing your goals gives you a roadmap for using analytics.

QSR Data Analytics: A Game Changer For the Quick Service Restuarant Industry

Quick Service Restaurants Market size for the USA alone is estimated at 406.17 billion USD in 2024. These restaurants have minimal preparation time, fast service, focused menus, and strategic locations. The QSR restaurants serve specialty food items like Taco Bell, Burgers, Pizza, etc. Most have a pickup counter and a few chairs outside to sit. Quick Service Restaurants (QSR) Include:

• Fast-food restaurants
• Limited-service eating places
• Pizza-delivery Places
• Ice cream parlors
• Beverage bars
• Cafeterias
• Carryout sandwich shops
• Takeaway service shops

QSR Analytics Involves the Systematic Analysis of Data Related to QS Restaurant Operations and Restaurant Location Data Analytics.

• Sales Data: Tracking daily, weekly, and seasonal trends in item sales.
• Customer Data: Understanding customer preferences, demographics, and behavior.
• Inventory Management: Monitoring stock levels and identifying patterns in ingredient usage.
• Employee Performance: Assessing staff efficiency and productivity.
• Financial Metrics: Analyzing costs, revenues, and profit margins.

Quick-service restaurants (QSRs) can benefit from detailed menu, consumer, and competitor metrics. The restaurant analytics enable these QSRs to further improve their work and offer highly personalized services like those offered by full-service restaurants. The actionable insights obtained through QSR data scraping can help these businesses identify competitors, their strategies, and locations. These insights will also uncover trends in the drive-thru food industry and ensure QSRs adapt to those trends early.

Bottomline:

Intelligent restaurant analytics play a crucial role in increasing your restaurant’s revenue and customer base. From outsmarting competitors to optimizing menus to match customer preferences, restaurant predictive analytics can further help you identify potential hotspots for new restaurant locations. If you are looking for a reliable restaurant data analytics company to collect meaningful data for your business and present it on interactive dashboards, FoodSpark can be your go-to destination. FoodSpark specializes in food data scraping and analytics for the restaurant industry. With cutting-edge web scraping solutions and APIs, FoodSpark delivers actionable analytics that helps restaurant businesses understand multiple aspects of their business for better decision-making.