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.”