The Big Business of Big Data

Published: 01 Dec 2015
The Big Business of Big Data
From QSR  

The Big Business of Big Data  


In April, Josh Patchus began his new job at Cava Grill, the upstart Washington, D.C.–based fast casual.

In a world of cooks and cashiers, marketers and managers, Patchus acknowledges that his title—chief data scientist—is an odd one, seemingly out of place at the emerging 14-unit Mediterranean concept best known for dishing out bowls, pitas, and hummus. But in the modern restaurant world, Patchus’s responsibilities are anything but out of place. In fact, some argue a guy like Patchus is downright necessary in a world hurling data at restaurant chains from both in-house (structured, as they say in the business) and public (unstructured) channels.

Big data, after all, is big business.

“Quick-service chains recognize just how competitive and fast-paced the world is, and, subsequently, just how much big data matters,” says Brad Marg of Clutch, a Pennsylvania-based firm offering advanced customer management platforms.

Charged with breeding a data culture at Cava Grill, Patchus shares data with leaders throughout the six-year-old organization, piecing together large volumes of insights to propel informed decision making.

“It’s a sign of where our brand wants to go,” Patchus says. “And we recognize just how much data can drive performance.”

Once a trendy catchphrase, big data has hit the mainstream, with restaurant concepts around the country utilizing the ever-accelerating mounds of data—those digital fingerprints extracted from point-of-sale systems, customer relationship management programs, social media, Internet searches, sensors, and more—to fuel efficiencies and profit.

“The more information you have, the more you can analyze and aggregate and the more you can make quantitative decisions to move the business forward,” says Laura Rea Dickey, chief information officer at the 500-plus-unit Dickey’s Barbecue Pit.

Big data’s rise
Using data to drive decision making isn’t new in the restaurant space. Traditionally, however, restaurants leaned on trailing data. Leadership reviewed things that had happened to inform current and future planning, and that information was often handled in silos with little collaboration between different departments, such as marketing, development, or operations.

But now a rising number of restaurant enterprises are focusing on leading indicators, eager to gain a window into what’s happening at the present moment—near real-time results regarding things such as a marketing offer, pricing changes, or weather’s impact on traffic.

“That’s a significant mind shift and one capable of providing a more holistic view into the business,” says Paul Konkel, CEO of Actus Data, a provider of business analytics solutions. “You can better understand the events that are most relevant to the restaurant, customer response, inventory needs, and more.”

The classic example, Konkel adds, is the ice cream shop that

opens early on hot days to sell more ice cream.

“That’s the holy grail: turning data into information and then action,” Konkel says. “You take a big bucket of data and transfer it into usable info that can drive restaurant improvements. It’s about how I can impact my day now and, ultimately, impact the bottom line.”

While big data remains a daunting, complex process on the technological side, particularly as data floods in from a variety of angles at an increasingly rapid pace, more restaurants are investing in third-party software or devising their own systems to shatter the traditional silos and fuel performance. Such software synthesizes data and produces accessible insights—charts and color codes, text notifications and graphs—for restaurant leadership.

“Now it’s a more fluid exercise,” Konkel says. “We extract the complexity of technology so you can run your business better, and big data becomes a business issue and not a technological issue.”

As a result, big data has moved beyond a restaurant company’s IT department and dropped into the hands of others throughout a restaurant organization, informing decisions around site selection, marketing, staffing, menu development, operations, inventory, and more.

“Data isn’t just for the analysts anymore,” says Jim Boswell, director of financial planning and analysis for the 650-unit Marco’s Pizza chain. “All business leaders need data at their fingertips to answer questions instantly.”

When Boswell arrived at Marco’s three years ago, he says, the Toledo, Ohio–based chain was already moving down the path of being a more data-driven enterprise. The company had a data warehouse and was beginning to leverage data to make decisions.

“It’s become apparent to everyone in our organization that technology is a partner to every function of our business,” he says.

Marco’s efforts have only intensified of late, including the creation of a proprietary system that brings data into a singular place so leaders can review and respond. “That’s helped us turn a corner,” Boswell says.

To wit, Marco’s is riding six consecutive quarters of same-store sales growth, and the average ticket in 2014 was up nearly 5 percent compared with 2013.

Big data at work
Though big data is not a new term, many limited-service brands are still in the early days of learning exactly how to leverage millions of data points to improve results. Here are nine ways restaurant brands are turning data into action:

1. Stronger site selection
The right location can contribute mightily to a store’s success or failure.

Marco’s marries key market data—such as the number of competitors in a given market, population density, household income, and psychographics—from a third-party provider to its own store performance figures, creating a robust site selection model that optimizes each eatery’s shot at success.

“Data is the starting point and helps us determine the right locations for our restaurants,” Boswell says.

2. Labor efficiency
Labor remains a key driver to success at every restaurant, especially amid rising labor rates and Affordable Care Act mandates. The restaurant enterprise that takes its eye off the labor ball does so at its own profitability peril.

Wanting to better understand how its business model fits within changing labor laws, Cava Grill reviewed historical clock-in and clock-out data to understand individual employee hours. Patchus’ team then ran simulations to understand which stores were at the greatest risk of higher-than-necessary labor costs, a key step in aligning expenses with profitability.

3. Menu development
In 2013, Tropical Smoothie Café released its Island Green Smoothie, the chain’s first vegetable smoothie.

“We just thought it was a great idea,” Tropical Smoothie CEO Mike Rotondo says. But the decision was also prompted by consumer data points.

The green smoothie rushed to the top of the chain’s most beloved smoothies. Soon after, the company debuted other vegetable smoothies, including beet, ginger, and carrot, broadening a category Tropical Smoothie leadership learned carried substantial consumer interest. The chain even saw when the vegetable smoothies were selling, which prompted marketing efforts to capture added traffic during those dayparts.

“We were able to identify more smoothies we could create and learned how we could best present those to the consumer,” Rotondo says.

4. Answering customer needs
Historically, restaurants leaned on surveys to better understand their customers. But surveys, while valuable, can often be flawed by perception; data, on the other hand, delivers an objective look at what’s selling and to whom. That allows operations to segment their business, producing offers from a general awareness campaign to specific promotions for specific people.

For example, Clutch built a mobile app for upstart fast casual Elevation Burger that enabled customers to order in advance and then receive their meal immediately upon entering the restaurant. The app has produced faster service and greater satisfaction for those customers desiring that convenience.

“It’s about knowing what your customers need and then being willing to invest in the things that create a new reality for them,” Marg says. “Marrying the product to the person is the real convenience of big data.”

5. Highlighting consumer preferences
From its POS data, Cava Grill noticed an increasing number of people moving away from pitas as their meal’s base, choosing rice or salad instead. That recognition sparked the fast-casual chain to place a greater emphasis on the appearance of its salads in stores as well as in promotional materials.

“We saw the long-term investments we needed to make and how we needed to treat salads,” Patchus says.

Similarly, when Cava Grill offered sodas as its lone beverage option, Patchus says, many consumers bypassed a beverage purchase, a drain to the average ticket and margins. When the eatery tested juices, however, drink purchases increased. Those findings spurred Cava Grill to introduce handcrafted juices and teas into its stores.

“With the metrics in hand, we were able to make the investment into juices with a lot more confidence,” Patchus says.

6. Optimizing operations
Rather than looking at data at day’s end or the following morning, Dickey’s Smoke Stack, the company’s proprietary data platform, pulls data from each of the chain’s stores every 15 minutes in a stock-ticker style format for local, regional, or corporate leaders to review.

“This gives us an opportunity to impact the moment,” Dickey says. “It’s been a huge shift for us from asking, ‘How did yesterday go?’ to asking, ‘What do we need to do for today’s dinner based on today’s lunch?’”

Similarly, Marco’s creates a robust balance scorecard using transaction counts, customer service input, sales, store audits, and performance metrics such as out-the-door times to measure and rank unit performance at several different levels.

“This allows us to target performance issues and get ahead of them,” Boswell says.

7. Arrangement of the products
Formerly, Cava Grill placed bags of pita chips at the end of its meal assembly line. Patchus and others wondered if pita chip sales might increase if the chips sat elsewhere. Testing that theory, the chain discovered customers were more likely to purchase the pita chips when presented in the middle of the meal-building process.

“In the line, while customers are building their meal, their thoughts are on the meal; at the cashier, their thoughts turn to payment, and selling the pita chips became more difficult,” Patchus says. “The data helped us understand the psychology of people going through the line and their ordering process.”

8. Store design
Dickey’s Smoke Stack allows the barbecue chain to optimize customer feedback, squeezing together information from social media, the company’s loyalty program, and its online feedback program to see a holistic view of the customer experience.

That information informed Dickey’s latest store prototype, allowing the chain to turn “customer care-abouts” into tangible elements of its store design, such as upgraded sound systems broadcasting acoustic tunes and smoking pits visible to guests.

“We learned just how much these dining-room elements mattered, even for our take-out customers,” Dickey says.

9. Business benchmarking
Each year, Tropical Smoothie Café requests profit and loss statements from all of its 400-plus units. The corporate office then takes those numbers and crunches them into a database segmented by store sales performance and distributed systemwide.

This effort, Rotondo says, helps store operators benchmark themselves against data from the entire system as well as stores capturing the same revenue, helping operators spotlight potential areas for improvement.

“We’ve seen profitability improve year over year because of this,” Rotondo says.
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