Last month, attendees at the World Economic Forum in Davos, Switzerland, pulled out their smart phones. Each attendee watched as the gadget displayed an analysis of his or her mental state—relative to others in the room—gleaned from data gathered by the phone itself.
A special program, which they’d agreed to install earlier in the conference, mined accelerometer data to tell when the phone had been moved, counted time spent on e-mail, and noted how long they’d kept the phone unlocked, and where and when they connected to Wi-Fi. “We could point out ‘you’re not very active right now,’ or ‘you’re not paying attention to this lecture,’ or ‘you’re jet lagged,’” Alex “Sandy” Pentland, the professor of media arts and sciences at MIT who developed the program, told the audience.
In the view of researchers like Pentland, the proliferation of smart phones—and our attachment to them—presents a prime opportunity to measure unseen behaviors and social interactions and use new algorithms to mine the value of this data. He calls this “reality mining,” and says it can make companies more innovative and deliver new marketing insights.
Some initial applications of cell-phone data have already been commercialized. For example, Sense Networks, a startup cofounded by Pentland, uses the ever-growing streams of real-time location information from cell phones and navigation devices—combined with historical research on consumer behavior—to predict the purchasing intentions of individuals across cities. Data on locations visited can indicate whether a person is going out for drinks or spending the day car-shopping.
The Sense Networks algorithms also continually update their analysis of anonymous cell-phone data to predict for clients whether the foot traffic in their neighborhood is, say, currently made up of business travelers or young adults out on the town. It looks at what kinds of locations they are visiting—such as a conference hall, rock club, or a hotel—and what times they are visiting them.
But Pentland sees many more commercial possibilities for cell-phone data. In a 2009 campus experiment, Pentland and a grad student used text-messaging and call logs, e-mail activity, GPS, and Bluetooth data from student volunteers’ cell phones to track informal social influence. Mining cell-phone data—and then surveying the students about what they were doing to create models that can help interpret future cell-phone data sets—allowed Pentland to track how students influenced each other’s political opinions, eating habits, or even their illnesses.
Pentland is known for developing a gadget called “sociometer” in the late 1990s. That’s a device worn around the neck that measures unspoken communication: a Bluetooth radio detects other nearby sociometers; infrared sensors detect face-to-face conversations; and a microphone picks up changes in intonation that can reveal who is the most influential speaker in a conversation.