Some of the more-important revenue streams communications service providers have uncovered and discovered have been of the accidental sort. Some enhanced services, such as caller identification (caller ID) were essentially a byproduct of a conversion from analog to digital switching. The switches needed that information to work, but new features were possible as a consequence.
Many consumers considered "push button" phones to be a premium device when the transition to digital happened. Engineers would simply have said that using DTMF tones was simply a better way of inputting number information to switches that now were digital, in fact computers rather than electrical appliances.
There now seems a glimmer of understanding that among the next great wave of value provided by mobile networks, sensor data might prove an unexpected boon. There already is talk of the growing value of "machine to machine" networks, of course, where remote sensors such as meters and gauges of various sorts communicate with servers located elsewhere.
But there is something of potentially equally-interesting value growing, and like M2M, will be a business-to-business value, with potential revenue streams that match. "At Northeastern University in Boston, network physicists discovered just how predictable people could be by studying the travel routines of 100,000 European mobile-phone users," the Wall Street Journal reports. "The scientists said that, with enough information about past movements, they could forecast someone's future whereabouts with 93.6 percent accuracy."
That, of course, requires the permission of the users tracked, as the data is personally identifiable, so there is an opt-in requirement.
In other cases, anonymous data might be equally useful, even when anonymous. Researchers are studying user data, in aggregate, to understand social effects, influence, the spread of ideas and trends.
Of immediate value to mobile service providers themselves are the business-relevant social effects uncovered in one study. By mining their calling records for social relationships among customers, several European telephone companies discovered that customers were five times more likely to switch carriers if a friend had already switched. The companies now selectively target people for special advertising based on friendships with people who dropped the service. That's a practical illustration of applying knowledge about social influence for a very concrete business problem.
Marketers try to use knowledge about social influence to reach people who, their social graphs indicate, can persuade others in their social networks, and who have bigger social networks. It takes little to imagine that firms will be eager to strike deals giving them access to opt-in data from mobile service providers that help them identify and reach such people.
All of which suggests that data mining for patterns could develop into quite a value driver and revenue stream. Perhaps it always will be a stretch to imagine a time when such data is so valuable that a service provider can afford to give away devices and services in exchange for opt-in rights to track and sell such information. But it isn't hard to see that it could become a major revenue stream, either.
That, of course, requires the permission of the users tracked, as the data is personally identifiable, so there is an opt-in requirement.
In other cases, anonymous data might be equally useful, even when anonymous. Researchers are studying user data, in aggregate, to understand social effects, influence, the spread of ideas and trends.
Of immediate value to mobile service providers themselves are the business-relevant social effects uncovered in one study. By mining their calling records for social relationships among customers, several European telephone companies discovered that customers were five times more likely to switch carriers if a friend had already switched. The companies now selectively target people for special advertising based on friendships with people who dropped the service. That's a practical illustration of applying knowledge about social influence for a very concrete business problem.
Marketers try to use knowledge about social influence to reach people who, their social graphs indicate, can persuade others in their social networks, and who have bigger social networks. It takes little to imagine that firms will be eager to strike deals giving them access to opt-in data from mobile service providers that help them identify and reach such people.
All of which suggests that data mining for patterns could develop into quite a value driver and revenue stream. Perhaps it always will be a stretch to imagine a time when such data is so valuable that a service provider can afford to give away devices and services in exchange for opt-in rights to track and sell such information. But it isn't hard to see that it could become a major revenue stream, either.
The fear is that such data could be stolen, a genuine concern, or that personally-identifiable information already is being shared with third parties, a concern that might strike some of us as far fetched, though the danger continues to exist.
But if researchers are correct, mobile phones will have immense new value as sensors. The data the sensors monitor will have value for marketing, sales and promotion, as well as many non-profit endeavors. You can say its one application of M2M, or you might argue it is related but separate. Either way, mobile sensor data looks like a huge potential deal.
But if researchers are correct, mobile phones will have immense new value as sensors. The data the sensors monitor will have value for marketing, sales and promotion, as well as many non-profit endeavors. You can say its one application of M2M, or you might argue it is related but separate. Either way, mobile sensor data looks like a huge potential deal.
The Really Smart Phone - WSJ.com (subscription required)
