Neither snow nor rain nor heat nor gloom of night stays these retailers from the swift completion of their appointed data gathering. (Illustration: James Boast)

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How Your Shopping Changes With The Weather

Closing the data gap between online shopping and its real-life counterpart.

It’s a miserable fall evening in New York City. Rain is being delivered in small gusts of wind, slashing into the eyes and inverting umbrellas. The entryway of UNIQLO on Broadway looks more like a bus station than a clothing store, with families huddled around strollers and European tourists folding their soggy maps. If there were an ideal night to throw an afghan around your shoulders, curl up on the couch with a cuppa tea, pop open your computer and do your shopping online, this would be the one.

And yet, further inside the store, people are waiting to try on clothes and a line is winding out from the bank of cash registers. “I’m meeting my friend here soon. So I guess it’s kind of a social thing,” says Uma, who is on a visit to New York from Oberlin, Ohio. She’s flipping through a rack of fall parkas while she waits. “I’m in college and my college town is really small. And they don’t really have a lot of stores. So, when I come to big cities I like to shop.” In other words, Uma explains, she’s here to catch up on trends, to see what people are wearing on the street in New York and to try and find it in the stores.

Despite remarkable gains in e-commerce (the Census Bureau estimates net profits from online sales to be 15 percent higher right now than they were at the same time last year), brick-and-mortar shops are still hanging on, in large part because they offer an experience that can’t be replicated over the Internet, giving shoppers a chance to see what’s popular on a local level and to set new trends with their own purchases. Many analysts are now saying that in order for brick-and-mortar stores to continue to fulfill this role and to retain the advantages they hold in their local economies, they will have to emulate their online competitors in at least one way. They will have to get much more sophisticated in the methods they use to collect and analyze data.

Part of this equation involves getting better at looking at the data that retail stores are collecting from their own customers. The trends that Uma notices on the street when she visits New York, for example, are just as likely to be found hidden in the troves of data that retailers collect every day on their cash registers—which products are selling best at which locations, what time of day people are most likely to snatch them off the shelves. All of this information comes back to store owners when a sale goes through. But that doesn’t mean they are seeing it.

“I think a big part of the problem is that the retail stores don’t have the fundamentals worked out, says Cathy Han, the founder of a startup in San Francisco, called 42, that uploads sales data onto the cloud and uses it to run diverse analyses for its clients.

Some store owners, explains Han, just don’t have time to look at their sales data. And those who do tend to rely on outdated methods like manually plugging the numbers from their daily sales into Excel templates and running the same report over and over.

This kind of analysis, says Han, may give store owners a broad overview of how profitable their businesses are. But it won’t tell them how or why they’re making money. And it certainly won’t help them to predict what they should be selling in the near future.

This is the problem that Han set out to solve. When a retailer hands its data over to 42, it gets back a dynamic report on what the top-selling products are, where they are trending, which items tend to get bought together, and which customers are spending the most money. The company then uses its reports to give retailers specific recommendations on marketing and inventory.  

With a static template, says Han, “you’d have to look at fifty, sixty different scenarios before you get to the point where you say this is the crucial part of the business. We can do that in fractions of a second instead of hours and days.”

But according to Karen Lowe, the global manager for IBM's retail industry, it’s no longer enough for brick-and-mortar stores to simply look at the data they are producing within their own four walls.

As part of its retail solutions services, Lowe's team at IBM compiles all of this data on the cloud and uses it to construct a predictive analysis of what will be trending each financial quarter.

For example, IBM has predicted that sales in health and beauty are going to go up 4.5 percent during this holiday season, an assessment that it was able to make because IBM has access to over 30 years of data from the U.S. Census Bureau and to sales observations from more than 46,000 retail establishments in North America.

In the past, these forecasts have proven to be 99 percent accurate and have prompted retailers to change their decisions about which items to put on sale, how to display them and where to concentrate their staffing.

And then, of course, there is the most notorious and elusive prediction of all: the weather. Having access to reliable short-term forecasts can help store owners prepare for all kinds of situations—snow storms that jam up traffic and delay the delivery of inventory, rush-hour downpours like the one that turned the UNIQLO entryway into an urban mudroom. But analyzing long-term climate trends can be just as useful. For example, retailers in the United Kingdom, which enjoyed unseasonably mild fall weather, were able to maximize their profits this year by delaying the clearance of their summer clothing.

“Retailers are trying to get smarter by anticipating their customer's needs,” Lowe says. “They are having to tap external sources of data. And that's not always easy.” To help them do that, IBM has provided retailers a way to consolidate and analyze data from the Internet of Things via an analytics cloud-based service to make key business decisions.

So whether they are making the data themselves or pulling it in from outside their stores, the retailers that stay afloat will be the ones that analyze it in the most intelligent way. Otherwise they risk becoming just another good place to get out of the rain.