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From Online to Offline: How Brands Use Big Data to Figure Out Where Their Customers Will Shop

Ecommerce brands are using big data to open stores exactly where their customers want them.
From Online to Offline: How Brands Use Big Data to Figure Out Where Their Customers Will Shop
Image credit: Viktor Koen
Magazine Contributor
7 min read

This story appears in the March 2018 issue of Entrepreneur. Subscribe »

It took Warby Parker three years to figure out exactly how to sate its customers’ demands. At first, when it launched in 2010, the company was online only: It offered to mail people five different fashion-forward eyewear frames so they could try them on at home, decide what they like and mail the rest back. When sample inventory ran out after two days, customers called the co-founders -- who were then students at Wharton living near campus -- and asked to come to their apartment to try on the product. (They said OK.) Then in 2011, Warby Parker moved into a building in Manhattan, and thousands of people arrived to try on the in-demand glasses. The company’s landlord threatened to evict it for monopolizing the elevator.

Related: 6 Steps to Building a Million-Dollar Ecommerce Site in 60 Days

So in 2013, the company finally found a solution. Though it had quickly become a famous ecommerce company, Warby Parker opened its first official store. By the end of 2016, it had 36 stores, and half of the company’s revenue -- then estimated at $250 million -- came from brick-and-mortar sales. Today, it has a total of 63 locations across the U.S. and Canada, with plans to hit 90 by 2019.  

This brick-and-mortar success comes at a time when the imminent death of retail is a regular news story. Real estate firm Cushman Wakefield estimates that 12,000 stores will shutter in 2018, up significantly from the 9,000 that already closed their doors in 2017. But Warby Parker isn’t alone in its commitment to retail. Other ecommerce leaders like Birchbox, Everlane and Bonobos are staking their next round of growth on brick-and-mortar outposts. 

What do these brands know that others don’t? For starters: exactly where to find their customers. 

As a digital-first merchant, Warby Parker has been gathering valuable information about its customers from day one -- first and foremost, where they live. That gives it an immediate edge in selecting locations for stores. To figure out if a space will maximize traffic and sales, Warby’s team reviews a mass of data points that help them find the best block on the best street in the most ideal neighborhood. Population density, the number of eyeglass customers and preexisting ecommerce sales in the area all play a part in the comprehensive consideration of an area. So do comparative sales data on nearby stores, available retail space and the neighborhood landscape, including factors like whether or not having a café on the same block as a Warby store will impact sales.

Related: 67 Fascinating Facts About Ecommerce vs. Brick and Mortar (Infographic)

Most of that data comes from Warby’s own documentation of customer behavior and a proprietary statistical model, but the company also taps anonymized data sold by cell carriers that track user movement. People don’t always shop where they live; some shop closer to their work. That’s important to understand when selecting a site.

“We are believers in deep data rather than big data,” says Neil Blumenthal, who, alongside his co-founder Dave Gilboa, serves as co-CEO.

Of course, Warby Parker can afford to build out an in-house data team to pinpoint perfect locations. But even young brands can take a page from its book. Ecommerce platform Shopify, popular among startups and established companies alike, gives merchants access to consumer behavior data, which savvy users have relied on as they consider expanding to brick-and-mortar stores. 

Sheena Brady, a Shopify employee who moonlights as the founder and CEO of Canada-based Tease Tea, launched an online store with plans to serve her small local market in Ottawa. A year later, she was surprised by orders placed by shoppers in the U.S. who had stumbled upon her site. Brady took a closer look and found Tease Tea had a strong fan base in the New York area. “I used Shopify reporting data and decided to open a pop-up in New York City,” Brady says. She set up shop in an indoor market in the Meatpacking District, and sales were so good, she opened a second location in the city with extended hours.

Similarly, U.K.-based athleisure brand Gymshark noticed a lot of orders coming in from Los Angeles. By digging into that sales data, as well as examining where their most active, engaged social media followers were based, the company decided to open a pop-up store in Santa Monica, which operated for a single weekend in January. “It was a huge success -- record setting in terms of brand awareness,” says Seb Mills, Gymshark’s IT director. Despite the pop-up victory, Gymshark, which launched in 2012, is taking it slow before investing in full-time retail stores. “There’s always potential to explore that avenue, and if and when we do, the locations will be data-driven decisions.” 

Meanwhile, traditional retailers are falling farther and farther behind their younger, more nimble competition. The nature of their business works against them: Most are saddled with inventory and pricey leases, and due to the nature of brick-and-mortar retail, they’re not gathering much useful data on their customers.

Still, retail insiders offer a word of caution: Unfortunately for them, we’re still in the early days of using deep-data science to make real estate decisions. At Foursquare, the social network that remade itself as a location technology company, CEO Jeff Glueck says his team is actively exploring data’s limits. It’s working with several companies on a pilot program to deploy the extensive data in Foursquare’s arsenal -- including foot traffic by time of day and demographic info from more than 140 countries -- on a real-time basis. “That’s powerful stuff,” Glueck says. But it requires great sophistication. Top hedge funds use foot traffic to predict earnings for publicly traded companies, but they have the capacity to process it. Glueck says this kind of data won’t be a part of the retail leasing toolbox for another five to 10 years. 

Related: 4 Ways a 'Data-Driven' Approach Anticipates Buyer Behavior

Nevertheless, the companies that can blend online and brick-and-mortar seem to have an advantage -- gaining access to good data while also connecting with people in person. That way, online informs retail, and retail informs online, creating a symbiosis that pays off for those who can master the balance. Industry-wide, when a digital retailer opens a store, traffic from internet users in the region jumps between 25 percent and 100 percent, according to Frank Layo, a managing director at Kurt Salmon, part of Accenture Strategy. The physical stores, he says, solve a key problem for digital etailers: “How do I get another touchpoint?”

The physical store also creates data that no online store ever could. For example, Warby Parker recently tested a kiosk-style pop-up in the middle of Brookfield Place, a mall in lower Manhattan. (Malls are different animals from street-front stores -- they can require merchants to be open at hours that aren’t productive and serve a different clientele.) The company learned something unexpected: Customers don’t like trying on prescription glasses in a public setting; it’s too intimate, Blumenthal says. But sunglasses sold well. Warby Parker has added that insight to its ever-growing collection of data.

“If you believe in putting the customer first as a strategy, then you just find ways to create better and better experiences,” Blumenthal says. “Eventually that takes you to data.” 

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