Location data can be a gold mine for Mobile Operators in their quest for new revenue generation.
There are many applications for Location Based Services (LBS): contextual marketing, geo-marketing studies (through the use of anonymized data), location-based advertising to opt-in customers and security solutions, to name just a few. Usually these LBS rely on the customers’ Cell-Id location. The level of precision varies from an average of 200 - 500 meters in urban environments, to between 1 and 10 kilometers in more rural areas. This level of accuracy is relevant in many situations. In the context of geotargeted advertising, for example, it would be considered too intrusive to text a customer when they happen to find themselves exactly in front of the corresponding point of sale. But there are cases where more precision is essential, and that’s where sub-cell accuracy comes in.
While cell -accuracy can be perfectly relevant to calculate the traffic in a mall or stadium, it becomes inadequate in the context of smaller venues, or when computing the audience around, for example, a billboard. Real estate studies are another area in which the accuracy of location measurements is crucial. In transport planning applications, cell-id can only work for rapid means of transportation (such as by car, train and plane) during which cell-id is often changing over the course of the journey. It isn’t accurate enough, however, to study bicycle or pedestrian paths, to distinguish the traffic from two neighboring parallel roads or to avoid false positives in security use cases. Even in location-based advertising, it can be critical to avoid confusion between the customer entering a mall and the one driving along on the nearby highway.
“Triangulating” the position of a mobile phone requires access to detailed information about the radio access network surrounding a mobile device. This information circulates within the OSS (Operation Support System) and SMLC (Serving Mobile Location Center) actively fetching the information needed to precisely locate a given mobile phone. While this method works well for individual tracking, it generates additional signaling within the network which means that it cannot collect all mobile phone locations within a given area.
Probes systems are another source of plentiful technical information. However, because this equipment was primarily designed to monitor the quality of service within the access network, they are not well adapted to tracking individual locations and movements: location data are often delayed, there is generally a lack of regular updates and they simply require too much investment when compared with the increased efficiency and pay-offs gained from Location Based Services.
Intersec has developed an original approach, extracting the necessary information from the RAN (Radio Access Network). This passive collection is performed from centralized equipment with a limited scope, which allows it to be both far more cost effective and more resilient to any network upgrades. What’s more, its superior processing performance ensures the ability to have quasi-real-time positioning data for all devices.
In instances in which no signaling is available for a period of time, an updated position is actively triggered by the network. This information is combined with all available networks (including Wi-Fi) to limit the number of blind spots for any individual device. The novel approach of combining real-time and mass scale for accurate location reports offers new perspectives to service providers, like more precise studies of compact urban areas, online visualization of crowd densities and real-time audience management for billboard advertisers.
Sub-cell is a powerful module of Intersec’s software platform: an insight factory allowing MNOs to leverage transactional and location data to deploy many different use cases.