Transport modelling has traditionally relied on time and resource intensive data collection methods. Using big data, for example Wi-Fi, cellular network and GPS data, we can derive a wide range of insights in a much more cost efficient and effective manner.
Tailored insights generated from big data sets can inform business decisions, either at a single store level, or across a network, on a high street and for shopping centres. To do this, we extract, clean and process location data, including cellular, Wi-Fi and GPS data.
We derive insights focusing on the demographic and behavioural characteristics of the people dwelling or passing a specific advertising space. These insights are derived from Wi-Fi, cellular network and GPS data sets, depending on the use case.
Insights can be derived from mobile network data to inform the planning decisions taken by planners, developers and housing corporations, who seek to deliver new homes to specific demographic bands in target geographies.
The pricing of car insurance policies is traditionally based on home location, driver experience and the driver record. Mobile network, app and/or telematics data can be used to assess the risk profile of a driver, based on a range of safe driving measures.