Why Big Data Is ‘Strategic Spaghetti’ For Retailers (PYMNTS.com)

pymnts-logo-womply

By PYMNTS

Posted on November 10, 2017   

Retail secret sauce? Throwing spaghetti strategically makes it much more likely to stick.

Nicole Jass, Vantiv VP of Data Products, said in a recent interview with Karen Webster that data must be the core of that strategy. But not just any heap of data will do. It must be the right data, distilled and synthesized into something retailers can actually use to help aim their flying spaghetti.

Everyone loves to talk about “Big Data,” but Jass said the biggest data in the world is of little use if merchants don’t know what to do with it. There is a trend toward collecting as much data as possible — and that isn’t a bad thing — but if merchants are overtaxed to process it, then less may be more.

This is especially true for small- to medium-sized businesses (SMBs), which lack the resources of larger retailers to bring in data scientists but don’t have time to comb through the data either.

Merchants, said Jass, “aren’t asking for data dropped off on their doorstep. They want to know more about their customer: how they’re shopping in the store and out of the store, what trends are shaping up, how online behavior and mobile wallet behavior are shifting.”

“Data is a fuel; it’s not the end goal,” Jass explained. “The end goal is to better understand consumers, and a large part of that is to take the guesswork out so that retailers aren’t just throwing spaghetti at the wall and hoping to figure out which one sticks — but they can strategically throw spaghetti.”

Drowning Out the Big Data Buzz

The term “Big Data” came into use in the late 1990s. At that time, it had a specific meaning.

According to a timeline of Big Data by Forbes, scientists were, for the first time, starting to run computations that produced hundreds of gigabytes of data as a byproduct, and that data was too big to fit in the main memory of computers; oftentimes it exceeded even the size of the local disk.

To store all that data, scientists had no choice but to acquire more storage resources. The ability to retain more data, however, did not equate to an ability to process it.

If all that computing amounted to masses of numbers but no insights, then there was little value in the exercise, computer scientists noted — yet the penalties of potentially throwing out useful data proved more formidable than the penalties of storing obsolete data, so the data was kept. And it got bigger. And bigger.

Like so many buzzwords, the meaning of “Big Data” has by now been so diluted that to each person and organization it means something slightly different. Jass offered a working definition that encompasses not only the amount of data, but its relative value or usefulness.

Jass said that Big Data means looking at millions of people and millions of lines of code and having a way to wrap one’s head around it all. Put another way, it’s piecing together a picture of the past and using it, through models and computing, to predict the future.

“At the end of the day, data is just truth,” said Jass. “A bunch of data for the sake of having data doesn’t really move the needle. But when you actually start distilling it into consumer experience and consumer behavior, you can move mountains with it.”

Getting to Know Each Other

Jass said Big Data can change both the merchant’s view of the consumer and the consumer’s view of the merchant, as well as its products — namely, which of them (and how many) they’re likely to buy.

Data helps merchants answer questions like, “How are my customers shopping, both in-store and out of store?”; “What’s trending?”; “How is online behavior shifting?”; “How are mobile wallet behaviors shifting?” and “How do recurring payments and cards on file change how consumers use their cards?”

Data fills in gaps in consumer profiles, appending information on demographics, shopping habits and spatial data, such as where customers spend their time during the week versus over the weekend.

That type of data doesn’t just drive better eCommerce. If merchants use it to decide where they’ll build a brick-and-mortar store, they can be sure to position it where customers are likelier to visit based on their location habits — a strategy known as “adjacent impact.”

For instance, said Jass, putting her favorite women’s boutique shop next to the grocery store she visits every Saturday would get her through the door and spending money far more often. Webster added that it could make a lot of sense to put grocery stores in shopping malls to create a one-stop shopping opportunity, especially since malls need all the help they can get.

On the consumer side, data offers insights into customer behavior and the impact of merchant outreach. Time of day and imagery are two huge factors that contribute to the success (or failure) of merchant campaigns, said Jass. Data can help retailers reach shoppers when they’re in shopping mode and more likely to make a purchase. Compelling pictures can help get them there.

“The visual aspect of the in-store experience has to find its way online and into emails to prompt people to spend their time and money there,” Jass said. “It takes a combination of art and science to create that better customer experience.”

Democratizing Data

Data points are a dime a dozen these days, and that’s good news for SMBs, Jass said. The availability of data gives the little guys tools to match some of what the big guys are doing.

Jass gave the example of an entrepreneur who runs a coffee shop. As a business owner, he’s focused on delivering a great product. He wants to make and serve the best lattes and bring in good customers who will become repeat visitors.

He likely doesn’t have the time or money to hire a data scientist, Jass said, but he does check his email every morning. Delivering data insights through that channel is a smart way to meet him where he’s at, rather than requiring additional investments on his part.

Jass said that’s what Vantiv does with its SMB-focused data product, BizShield. A daily digest email funnels the most important insights into business owners’ inboxes — for instance, if they got a two-star review on Yelp, or if they went from being the third most popular coffee shop in the area to the second. Merchants can see how much frequency they’re driving or whether their average cart size is bigger or smaller than similar local businesses.

Large retailers have different needs, said Jass. While the coffee shop owner is trying to drive five new customers through the door in the morning, a large retailer is looking to influence the movements of millions. Both sizes of merchant have similar end goals (to grow their business) and are using similar data to help get them there, but their tools and strategies will look very different.

To take it back to the see-what-sticks analogy: Large and small businesses aren’t even throwing the same kind of pasta. But with the right data, a smart business owner can make it stick.