Many of us don’t realize that the smartphone that we carry is actually a radio. In fact it can also double up as a probe.
It is a radio because when we make or receive calls, or download or upload data, we are actually using radio frequencies. It also becomes a probe because the smartphone is constantly sending out information about its user’s physical location, signal strength and other relevant telecom parameters.
The communication service provider’s network hub is therefore continually receiving an unimaginably large volume of data that can also be impossibly hard to manage.
Even five years ago it was far too expensive to store this data stream efficiently, and then query it intelligently. So the service provider used only a small fraction (5%?) of this data – chiefly for invoicing – and let the rest of the data stream simply flow away.
It was a severe loss of opportunity, but since the perceived opportunity cost was less than the likely data management cost, it was not seen as a profitable venture.
Today we have data management devices that are more efficient and less expensive. So the opportunity offered by such data mining suddenly acquires an exciting profitable edge.
We will look at three examples where such big data applications in telecom can translate into viable business opportunities.
Studying call drops
Imagine for instance that the call drop percentage in a certain neighborhood has grown alarmingly. Perhaps there are many more users now, or many more buildings have suddenly sprung up. It could also be a case of interference or of faulty towers.
This problem can often be tackled by simply analyzing all the data that a smartphone is continually sending out. Such data analysis could, for example, reveal that the alignment of an antenna requires an azimuth or tilt change. In fact the analysis can even calculate how a three-degree tilt can impact the call drop percentage.
If the call drop percentage in the neighborhood can be reduced from 50% to 20%, the savings will translate into astronomical sums.
Tracking traffic jams
Data from smartphones have also been successfully used to identify and track traffic jams in crowded city junctions or expressways. Assuming that every car driver has a smartphone, and assuming that the phone is switched on, the location data gathered by the communication service provider can be successfully plotted on a map to identify locations of high traffic density.
We can do even better by animating the traffic flow to identify the likely location of the next big traffic jam. Such animated pictures can be sold at a handsome price and will be much more informative and accurate that the dubious and often unreliable information currently provided by FM radio stations.
Influencing choice of restaurants
Geo-location data from smartphones can also be used in more innovative ways. Imagine that you are a salesman who drives to the western end of your city on Mondays, Wednesdays, and Fridays. You usually start at 10 o’clock in the morning and – on most days – you stop by for lunch at 1 pm at a big food court at the western periphery.
As you enter the food court your smartphone beeps out exciting offers from different hotels located inside: Subway might offer a 5% discount, Dominos might offer a 3% discount in the mall. Depending on your mood you make an impromptu choice … but you always wish that someone gave you a 10% discount instead.
One Wednesday your wish comes true! Just as you take the western express highway at 10.15 am, your smartphone beeps a 10% offer from Subway. You are pleasantly surprised and instantly make up your mind that you will lunch at Subway in the Food Court today.
This is just one example of big data in action. Based on the geo-location data sent out by your smartphone, the communication service provider has already created a model of your movements. So as soon as you go westwards on a Wednesday, the model determines that you will lunch at the food court with a high probability. It then ‘negotiates’ a profit percentage for itself with Subway. Subway is happy to pay the service provider the extra percentage because of the early booking advantage it gains vis-à-vis its competitors.