The restaurant industry is a staple to America’s food culture. When dining out or ordering in, customers expect delicious, fresh food, and the convenience of being able to have it delivered to their own home or office space. Connoisseurs around the country look to the food industry to provide affordable, healthful, comforting options and tend to select restaurants that can provide the best all-around dining experience.
In 2021 in the US, the total retail and food service sales totaled $6.2 trillion. Because of the important role the restaurant industry plays in our lives, restaurants must compete with one another to gain customers, keep regulars, and find new ways to expand their menus and services in order to generate revenue and remain competitive in this cut-throat industry. One powerful tool restaurants are using to gain this competitive advantage is big data. Food industry analytics provides restaurants, franchises, and other food establishments with the means to cut down on costs, increase business, and spot new trends. By keeping track of data on customers, sales, operational procedures, and delivery practices, restaurant owners can gain key insights into how to improve their own operations, as well as how they can optimize current industry trends and stay ahead of the competition.
What is Restaurant Analytics?
Restaurant analytics is a form of data analytics that involves gathering, measuring, and combining data so that helpful insights can be extracted. Whereas restaurant reporting only compares sets of data, restaurant analytics takes a deep dive into what these numbers mean. This field can provide insights into the “why” certain meals aren’t selling as well as others, or why a particular neighborhood could use a Thai restaurant but not another pizza parlor.
Not only is restaurant analytics useful for individual restaurant owners, but it’s also an especially helpful tool for those who own restaurant chains or multiple franchises. It provides owners with a streamlined, single platform that conveys information for all restaurant branches or locations, which can save time and money. By having a more robust understanding of the factors that make a restaurant profitable, as well as the variables that affect sales, owners can adjust operations, change inventories, revise menus, and offer employees more tailored training options.
This article will explore 12 ways the restaurant industry benefits from restaurant analytics.
12 Benefits of Using Restaurant Analytics
Restaurant analytics is changing the way we dine out. The insights provided by data analytics empower customers to make more informed dining decisions and also help restaurant owners to attract new and repeat clients. Here are just a few of the many ways the restaurant industry is evolving with the use of restaurant analytics:
- Quality control: Big data provides important insights into the supply chain cycle of food as it is shipped around the country. For example, products that are sensitive to temperature fluctuations, such as milk, ice cream, fruits, and vegetables, must be kept in strictly monitored environmental conditions to prevent them from spoiling or melting. By using IoT-driven sensors, it’s possible to receive real-time data about the products being shipped, which can indicate which are damaged and must be replaced.
- Food delivery: Most competitive restaurants now offer options for food delivery. Using data analytics, restaurants are better able to manage delivery services by providing information on variables such as weather or traffic patterns. These insights allow drivers to deliver meals in a timely fashion and ensure that they arrive to customers when the food is still fresh or warm.
- Franchise data: Customer management platforms help franchise owners monitor customer habits at hundreds of locations. These cloud-based services provide a means of collecting data from multiple sources to identify avenues into making a brand more profitable.
- Meal size: Restaurant owners can adjust not just the quality and availability of foods used, but the portions offered to customers, based on the data gathered through restaurant analytics.
- Personalizing customer experience: A variety of digital tools, such as mobile apps, kiosks, and table-side tablets, not just improve customer service, but also help owners mine data on their customers to better meet their needs.
- Reducing food waste: One of the main problems a restaurant owner faces is food waste. The financial and environmental tolls of food waste can be tackled by centralizing the data-collection process and keeping suppliers informed about sales data. When suppliers are put in contact with sellers, they can more accurately predict demand and decide on the appropriate amount of food to provide to a restaurant.
- Optimizing restaurant space: Data-gathering sensors can be installed in restaurants to keep track of how customers travel around the restaurant itself. Information about traffic flow allows restaurant owners to improve the physical layout of a space.
- Customer segmentation: By separating customers into groups based on the characteristics they share, restaurants are able to gather important insights into how to better target messaging. Information like gender, age, location, and marital status can help restaurants and individual franchises find more effective methods for marketing to these specific groups.
- Restaurant location: Where a restaurant is located has important ramifications for how successful it will be. Predictive analytics provides those who are looking to open a restaurant with crucial insights about whether a given location is a potential hotspot for customers, as well as what dining format may be most popular among customers in a given city or neighborhood.
- Locating taste clusters: Advanced analytics is a powerful tool that allows restaurants to cater to the tastes of their customers. Taste clusters provide location-specific and time-specific insights about what customers like and dislike. This information allows food providers to integrate their ordering and billing systems in order to tailor their offerings to customers’ cravings.
- Feedback: One of the most productive uses of restaurant analytics is its ability to offer customers a platform to provide restaurants with feedback on their dining experience. By collaborating the feedback customers provide on different modes, such as inquiry forms, mobile devices, and websites, a restaurant chain can work to provide customers with better service and dining options.
- Revising menus: It can be very difficult to figure out why a particular menu is working, or why it isn’t. The process of correlating data points can provide restaurant owners with a better understanding of what appeals to customers. Examining repeat orders, most popular dishes, and customer reviews can provide insights into why a particular menu is succeeding or failing. Owners can then decide which dishes to replace, which to discount, and even how to display various dishes on a menu in order to help bring attention to the most popular dishes.
Collecting big data on customers’ purchasing and eating habits is a valuable tool for restaurants. However, until a Data Analyst can extract insights from these numbers, the data isn’t actionable. This is why more than ever data analytics play a crucial role in the restaurant industry; it offers restaurant owners effective strategies and solutions that can increase sales and create an all-around better dining experience for customers.
Hands-On Data Analytics Classes
For those who want to learn more about how to process and analyze big data, Noble Desktop’s data analytics classes are a great starting point. Courses are offered in New York City, as well as in the live online format in topics like Python, Excel, data science, and data analytics technologies, among others.
In addition, more than 130 live online data analytics courses are also available in data analytics and visualization from top providers. Topics offered include FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to six months and cost from $219 to $27,500.
Those who wish to study data mining or data analytics in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
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