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How the food industry is making sense of big data

6 min read

Restaurant and Foodservice

(Photo: Flickr user Philippe Put)

Every restaurant, supermarket, convenience store, food truck, hospital cafeteria, manufacturer, distributor — every food business — generates a huge amount of data. Data on average checks, typical wait times, cooler temperature, product inventory levels, loyalty card usage rates, social media engagement — the list goes on. And that list grows every day, as new technologies and services enter the market.

But, until recently, the food industry made use of only a very limited amount of this data. Managers would check sales at the end of the night, a process made easier with computerized point-of-sale systems. But as technology infiltrates every aspect of the food industry, and with innovative startup companies looking for ways to improve food operations (see the huge variety of meal delivery startups that have popped up in recent years), suddenly everyone is looking to harness this “big data” and make crunching the numbers automated, immediate, and actionable. Now this data can help target and engage customers, improve inventory management, better manage profit margins, and assist in staffing and training. According to research we did for a recent issue of Datassential‘s Creative Concept TrendSpotting publication focusing on big data, 36% of operators already consider their operations to be data-driven.


“Data is everywhere in this business,” one operator told us. “It helps us make decisions on which items we serve, to whom we are trying to sell to, and how to get them to keep coming back.”

At “social dining experiment” Dinner Lab, which holds pop-up dinners in nearly 25 cities across the country, guests share their opinions at the end of every meal — how did the dish taste, was it creative, what would you change, and is the dish restaurant-worthy? All of this data is tabulated and shared with the chef, who can use it to adapt the menu in the future. Now the company is planning to open brick-and-mortar restaurants, with the locations, menus and chefs all determined through this diner feedback.

Much of big data analysis is focused on creating a more customized, customer-centric experience by learning what customers want, when they want it, how much they want to pay, etc. Today’s restaurants track customers throughout their entire dining experience, from the amount of time they wait for a table to the dishes they order to what they say on social media. Software tracks all of this, creating customer profiles — food allergies, or favorite wines — to personalize the dining experience. Starbucks, which has 13 million mobile payment app users representing 16% of total transactions, uses this data to track what individual customers like, or what makes someone different from other customers, all of which is used to create more relevant marketing efforts, like an offer designed to entice a customer who hasn’t visited in a while.

Cincinnati-based supermarket chain Kroger analyzed the data from its tens of millions of Kroger Plus cardholders (98% of Kroger’s customers are members) to personalize the shopping experience and target rewards and new promotions. A decade after the initiative began, 95% of the brand’s growth came from existing shoppers, according to Advertising Age.


Operators are also using the data to improve overall operations, from stock levels to food temperatures. Kroger used its data to adjust pricing, monitor and adjust inventory at individual stores, and even solve its most common customer complaint — slow checkout lines. According to Information Week, which placed the brand third on its Elite 100 list of innovative companies, Kroger used infrared sensors at the door and register and predictive analytics based on historic shopping data to cut the average checkout wait time from 4 minutes to less than 30 seconds. The quicker checkout experience not only made customers happy, but those happy customers had a bonus effect — improved cashier friendliness, according to customer surveys.

At data-driven Clover Food Lab, a rapidly-expanding healthy fast casual concept based in Cambridge, Mass., data is used to track everything from employee scheduling to the moisture levels of the plants in the restaurants. When founder/CEO Ayr Muir wanted “real time [cooler/freezer] temperature monitoring, accessible from anywhere, with full stored history” he turned to FreshTemp, a graduate of MIT’s Media Lab, which makes wireless temperature monitors and sends a text message when temperature levels dip to unsafe levels.

A number of startups like FreshTemp have launched in recent years, all looking to make big data more useful and actionable for operators. Swipely, the payment system and data analysis company, is now processing $4 billion in payments a year, and all of that data is combined with social media information to analyze restaurant operations and customer habits, allowing operators to decide which menu items to cut and track which servers are earning the most tips. Avero, a system “designed for restaurants by restaurateurs” analyzes data by tracking popular (and unpopular) dishes, helps predict purchasing needs, and automatically monitors employee behavior for red flags, like tip inflation. You’ll find companies that track marketing data (Beckon), sales data (Roambi), online reservations (Venga), and employee schedules (ShiftNote), to name just a few of the companies looking to turn big data into big business.

The future is data

The tracking and analysis of big data within the food industry shows no signs of slowing. According to Datassential’s research, nearly a quarter of operators plan to use data to measure staff effectiveness in the future, such as which servers are selling the most specials, and 18% are already doing it. And one out of every five operators plans to use software to create guest profiles, twice the number of operators using such technology today. And while the restaurant industry has been slow to adopt the type of scannable POS systems available at retail, providing an overall view of the segment, these new technologies may be the next best thing — and then some — as big data companies start to combine and analyze larger sets of data — “bigger data” so to speak. Technology company Oracle recently purchased Micros, one of the top POS companies in the industry, for $4.6 billion. Now Oracle will have access to data at more than 330,000 businesses, including restaurants and hotels, in 180 countries.

At Datassential, we’ve long considered ourselves one of the original big data companies catering to the food industry, using enormous amounts of menu data to study and predict food trends, and we continue to add to those capabilities with systems like SCORES, which crunches the numbers on consumer opinion data for every new menu item and LTO introduced by major chains each month.

“It’s all about the data — without the data I would be lost,” one operator told us. “A chef these days works more with data than with recipes.”

Maeve Webster is the senior director and Mike Kostyo is the publications manager at Datassential, a leading supplier of trends, analysis, and concept testing for the food industry. To purchase the Creative Concepts: Big Data TrendSpotting Report mentioned in this article contact Webster at 312-655-0596 or


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