Big Data Analytics Gets Smarter With Location-based Services

The “location, location, location” slogan takes on fresh meaning when Jones Lang LaSalle, a $4 billion global real estate firm, applies big-data analytics to it. For every pair of coordinates on a digital map, there are data sets to explore, including basic economic trends and questions related to business operations.

Where should a multinational build a research center to attract and keep engineers in India? What is the risk of soil deteriorating if there’s an earthquake near a client’s data center in the Pacific Northwest? How should a global bank revise its ATM network in Singapore to boost performance?

Jones Lang LaSalle finds answers in its cloud-based mapping services hub, called MapIT. Launched this year, the system centralizes the formerly disconnected activities of 5,700 global users of disparate mapping tools, says Wayne Gearey, location intelligence officer.

Geographic information systems are no longer the domain of government cartographers and retailers looking for the next great place to set up a store. With the rise of analytics–and with every businessperson carrying a smartphone with a built-in mapping app–the idea of adding location data to big data is taking hold. But these projects aren’t always designed with collaboration from IT groups.

Jones Lang LaSalle uses its new apps, built on Esri’s ArcGIS mapping engine, both internally and externally with clients to uncover geography-based opportunities for competitive advantage, Gearey says. “We had Wal-Mart and Amazon in our office, and we could show that we knew how many people were ordering books online,” he says. “We could then show the locations for same-day delivery, the gaps in same-day delivery [and] labor available in those markets.”

Booth Babcock, director of store development strategy at retailer Lululemon Athletica, described at a recent conference how he gingerly introduced the idea of asking for postal code information from shoppers. (Lululemon’s culture generally guards against such requests.) The resulting analysis showed executives where Lululemon’s customers really come from.

“We found our guests were more diverse than we expected. They were more likely to be older, more likely to live in the suburbs and have kids,” Babcock said. In an interview, he says the analysis work was done “totally separate from IT” and could inform store site selection. Serving these customers could mean locating new stores in suburban malls, for example, and making floor sizes larger to accommodate families.

Selling the idea of location analytics requires marketing even when IT is on board, Gearey says. He hired a geo marketing consultant to help communicate the business value. Visualizations help, too. For example, in a meeting with client New Belgium Brewery, he says, “we brought our application in as a marketing tool and ended up being able to answer their location issues. The CEO was at the table [and we] answered some of the questions that were sitting at the back of her mind.”

Michael S. Goldberg is a freelance writer based in Massachusetts.