A team of researchers at MIT has produced some technology that has interesting implications for real-time, primary research. They’ve developed some almost-affordable “badges” that are packed with sensors such as IR proximity detectors and accelerometers (motion and speed detectors). A timely MIT Tech Review article gives a very nice introduction to the technology and its implications.
Recently, they’ve demonstrated the technology by handing out the badges to attendees at a media conference, and harvested the data in real time to chart spontaneous networks and groups as the attendees interacted, discussed, clustered around booths, and shared notes and materials.
At the recent International Network for Social Network Analysis “Sunbelt” conference, MIT’s Benjamin Waber presented how they used the badge technology for mining conference behavior under the title “Sensible Organizations: Discovering Social Networks using Sociometric Badges”, and showed some interesting network data. My impression was that many of the attendees reacted the way I did - suddenly considering the implications for collecting communications and relationship data in an entirely new way. Social network data is traditionally (though certainly not exclusively) collected through surveys and interviews, or by mining digital sources such as social media, email communications, telephone logs, etc.
If the MIT spinoff company that is commercializing this technology - nTag - can get the cost and profile down, this sounds to me like a very promising tool for mining event activity for public relations and marketing. The badges currently cost somewhere in the order of $50-60 each, so their PR / Marketing applications may be limited for some time to come. Imagine being able to quickly categorize the behaviors of event participants, generate hard data on the popularity of particular booths/locations, who might be “interested shoppers” and who might be resistant outsiders - as the event is still happening. Privacy issues also remain to be addressed, which is a high-profile issue whenever social network analysis is discussed. But the potential is barely being scratched; the same technology has been used to reveal surprising behavioral and psychological characteristics of the subjects. For example, Mr. Waber reported that the accelerometers had been used with professional poker players to successfully predict when they were bluffing, merely from miniscule body motions that were invisible to the naked eye. With companies like Intel, Motorola and Honeywell investing hundreds of millions into distributed sensors for home healthcare and mobility applications, I think we will see versions of this technology become a regular part of the analyst’s toolkit.
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