Operations | May 2013 | By John Morell

Get a Grip on Big Data

Brands must cope with a new analytics trend to get the most out of marketing and operations.

Quick service brands are using big data to streamline and improve business.
Brands like Haute Dogs & Fries are capitalizing on the big data trend to better understand consumer behavior. Haute Dogs & Fries
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Franchisees, C-suite executives, and marketing experts in the limited-service industry have likely all come across the term big data in some form or another over the last couple of years.

But unless their background or interests lie in analytics or statistics, they probably shrugged it off as part of a conversation between numbers nerds.

Now, however, things are about to change.

“A revolution is going to happen,” says Chris Diener, vice president of analytics for San Francisco–based AbsolutData, which consults with companies in the hospitality industry and others about collecting and interpreting data.

“We can’t say when, but we’re already seeing the start of it, and it’s going to change the way businesses operate forever,” he adds.

“The quick serve that masters big data first will have a huge edge on its competition. It will be flying over the marketplace while everyone else is on a bicycle.”

In a nutshell, big data refers to the mass amount of information companies must contend with, both now and in the future.

If one pictures a quick-service headquarters as an airport, for example, data like Twitter comments, kids’ meal sales in San Antonio, and the price of Equal packets are circling above in a holding pattern.

Data analysts are tasked with figuring out which pieces of information they need to “land” and then place them into one of two categories: structured or unstructured data, says Julie Washington, senior vice president and chief brand officer for Jamba Juice.

In the structured bin goes information coming from the POS that shows what’s selling, where, and at what time.

Information is also compiled from social media—such as responses and likes—and from hard information like names, e-mail addresses, and physical addresses gathered through a loyalty program.

In addition, information comes in from suppliers regarding product availability and internal transportation, among other bits of data.

In large organizations, there exists a lot of data and information to be digested, much of which is likely to help a brand determine its health and direction.

However, the information is also internal. This means that analyzing all of the structured information might give a decent view of what’s selling and when, but no indication as to why, Diener says.

That’s where unstructured data comes in. It’s the information outside the business that may possibly have an effect on it and effectively answer the “why” questions.

This includes information about weather patterns, traffic patterns, and population variations at a location at various times of the day, for example.

Some companies even employ “scrapers” to dig and mine for mentions of the business on social media networks, forums, and chat sites.

The art and science of big data involve taking both structured and unstructured data and figuring out how to incorporate them into marketing and operations planning. Modern high-tech companies like Google, Amazon, and Facebook have been doing this for years, and now other industries—limited service included—are trying to capitalize, too.

“It’s all about making predictions based off of this super data,” says Jim Gallo, national director of business analytics at Information Control Corporation.

“It’s taking geospatial data, such as the knowledge that you have a large number of loyalty club members passing by your interstate exit on their way home from work, and sending them texts about a special dinner offer you’re having today,” he says.

“If done right, you’ll also know about how many will use the offer and how much product you should stock.”

Although organizations have been producing an impressive volume of data for years, it wasn’t until big data recently rolled around that they’ve been able to figure out exactly how to gain critical insights and value from it, says Brendan O’Meara, managing director of worldwide retail for Microsoft in Seattle.

“Historically, companies threw away most of this data because of prohibitive storage costs and weak analysis tools,” he says.

At one point, for instance, a brand may have noticed bigger lunch crowds in a particular unit every other Friday, but may not have investigated the reasons that were fueling the trend.

However, once data is mined and examined, the brand may discover that those high-traffic lunch periods are the result of three nearby factories that have paydays on those Fridays.

This means there may be value in reducing lunch specials on paydays and increasing them on other days to drive more regular traffic, O’Meara says.

Not only has big data been around for many years, but marketing based on this information has, too.

For instance, restaurant operators know that coffee and hot chocolate sales go up when the weather cools down in the fall, and they generally plan their product stock accordingly.

However, the rise of big data promises to take this type of business truism and put some science and statistics behind it, with a little social media thrown into the mix, as well, Diener says. “There’s basically one phrase that all of this data gets funneled down to,” Washington says. “What is the ROI? How does the data we’re collecting relate to the bottom line for the company? In some cases, maybe you’re collecting data that you already have or you’re paying for information you can get for free,” she says. “You’ve got to stay on top of the data coming in and determine how useful it is to your bottom-line goals.”

Big data can alter the way customer reactions are judged, for instance, when a new product is tested, Gallo says.

An item may be advertised through point-of-sale signage in a handful of stores, as well as through social media channels, with a company carefully watching the tweets and Facebook responses to gauge customer reaction.

“It’s a more immediate way to poll if you have a winner,” he says. “You get a good picture of what your loyal customers are saying, and you can use that to quickly make adjustments if needed.”

But using this type of information and customer data doesn’t mean the venerable focus group has been replaced, Washington says.

“I think like the post office, it will always be around,” she says. “Social media gives you immediate reactions to your products, which is wonderful, but there are times when you really do need those face-to-face encounters with consumers to ask them questions and get into what drives them to buy.”

Just a decade or two ago, digital storage space could be costly, and analysis was considered guesswork with fewer numbers, Diener says.

Now, however, many businesses have become hoarders of virtually any bit of information they can get on their current and potential customers.

“The key is, you’re not just in the restaurant business anymore,” he says. “You’re in the information business, and you’ve got to collect and use that information to stay on top of the market.”

Though big organizations may have more manpower to put behind big data analytics, smaller quick-serve operators can use this information, too.

For them, however, the race to keep up with the big guys in terms of data analysis is often confined to a few minutes on a laptop or smartphone here and there throughout the day.

“We went into this business with a very definite plan that social media would drive our marketing, and that’s how it’s worked out,” says Lionel Holmes, co-owner of Haute Dogs & Fries, a two-unit quick-serve operation based in northern Virginia.

“In the mornings, you do a check of Twitter and Facebook to get a pulse of what’s happening—what people in your community and in your demographic are talking about,” he says.

“Then you can start a conversation about a special you’re having that day, and odds are it’s going to get around quickly.”

Holmes says his years of experience in the restaurant industry and his interest in marketing have taught him that the ways in which customers prefer being approached electronically are evolving.

“It used to be that asking for someone’s e-mail address was no big deal,” he says.

“Now it’s like you’re asking for their social security number,” he continues. “Or lots of people have one e-mail for personal use and another to sign up for subscriptions that they never check, so e-mail addresses have become less reliable in terms of contacting people.”

The big data trend not only means changes for operators at the store level, but also for brands at the corporate level.

In the years ahead, Gallo says, it’s likely that a company’s chief information officer and marketing director will work so closely together that they may want to be in the same office.

“Some organizations are already putting in place a chief data officer to oversee all of the information coming in, and others are hiring data scientists to analyze and make projections about the business,” he says.

And while it may seem like a computer, rather than a person, could gather and interpret all of the big data, it’s not that simple, Diener says.

“We often get caught up trying to find the easy solution—the simple program that allows us to push a button and tells us what product will sell on what day—but you can’t rely too much on that,” he says.

“It’s all about what information is fed into the program and how the program is designed,” he adds. “You still need someone to look over the results and give a detailed interpretation; we don’t have the computer program for that just yet.”