Despite the continued growth of e-commerce, successful brick-and-mortar stores are still a viable option.

Forbes reports that 94 percent of sales are made offline at physical locations. Google’s recent announcement of their first retail store, described as a “playground for tech lovers,” is yet another sign of a surprising trend in the business-to-consumer (B2C) sphere. Digital and e-commerce companies are increasingly opting for brick-and-mortar locations to allow customers to check out products in person.

Location and Successful Retail Execution

“Proximity to customers” is critical for effective retail execution, as consumer expert Barbara Thau recently highlighted. Petco, Wendy’s, and Dollar General are just three examples of major retailers using big data methods, including retail mobile data collection, to optimize store placement for long-term returns on investment. Companies must find locations where the pros of placement outweigh the cons, and more importantly, consider potential placements in light of long-term plans for expansion. When determining where to open a store, data shows that sophisticated brands will typically consider potential customer base, neighborhood and city demographics, potential for growth, and local competition.

Case Studies of Big Data-Driven Retail Experiments

While the use of big data to drive retail location selection has recently gained attention in the press, brands have been using available data assets to optimize placement for decades. The US Bureau of Labor and Industries reports that fixed point-of-sale location and design are crucial to attracting walk-in traffic. Here are three recent examples of big-name companies who’ve launched fascinating experiments in retail following big data analysis.

1. Starbucks Express Store

The address 14 Wall Street is in the heart of New York’s Financial District, an area where potential customers definitely don’t have the time or patience to navigate long lines. NY Eater reports it’s also the location of Starbucks all-new “express store,” an experiment in retail efficiency for busy workers in the Financial District. Customers can order via the app in advance of arrival, pay, and then pick up drinks while navigating a store designed in a loop to prevent lines from building up. Additionally, the store only offers drinks that can be quickly made.

2. Birchbox Rethinks Retail

Popular e-commerce retailer Birchbox entered the cosmetics and skincare space through an innovative monthly subscription product sold exclusively online. New York’s SoHo district is filled with high fashion shops and boutiques, and is known to attract a young, hip demographic that loves to shop. Birchbox’s first retail location is a well-thought-out “experiment,” with an interior that is organized and branded to match the company’s online presence.

3. Amazon Opens Doors at Purdue

Amazon Student is an existing service of the e-commerce giant, designed to provide cost savings and convenience for busy University students. The brand is experimenting with physical locations at Purdue University, and now offers a “secure location” for students to order tech devices, textbooks, and even groceries. Students who have their orders shipped to this location receive free, one-day delivery. Amazon reports they’ll expand the service beyond Purdue soon, and more immediately plan to open a second on-campus location.

While all three of these retail experiments have yet to prove long-term success, it’s clear they have an excellent shot at winning the hearts and dollars of consumers near these new brick-and-mortar locations. Their potential for success is partly ensured by smart data analysis, including a thorough understanding of customer demographics, behavior, preferences, and needs.

Retail mobile data collection offers powerful potential for store brands to understand their ideal buyers. Data collection can prove location effectiveness and best practices for catering to consumers, allowing brands to ensure a higher success rate when opening future locations. In the digital age, location matters more than ever when it comes to retail success. Successful retail execution has always required a comprehensive understanding of location and its related components. With the help of mobile-generated data, effective location selection is possible.

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