Neo-demographics

Neo-demographics is a response to a changing world – the digital technologies that now pervade our lives are producing more and more data about us. These digital footprints act as records of our interactions with the world around us, cataloguing our behaviour within it to ever-increasing levels of details. In one week, the average UK citizen has 3,254 pieces of personal information stored about him or her, and interest in analysing this data is on the rise in the commercial sector – but it’s time to make that data work for us too.

But we are creatures of routine and habit, and important patterns exist in the sea of data we generate: patterns that characterize us as the unique individuals we are. The aim of the Neo-demographics project is to not only to develop  cutting edge techniques that allow detection of these patterns, but also to provide tools that allow consumers, SMEs and policy makers to harness them while maintaining individual privacy – and not just leave such capabilities in the hands of  the monolithic corporations.

So how do we, as individuals, benefit from Neo-demographic analysis? Well the characterisations produced from our digital footprints are not only illuminating, but have the potential to challenge traditional market failures: they can increase product mobility (allowing individuals to more easily isolate services and tariffs that better match their archetypes);   they can remove barriers to entry for smaller businesses in market segmentation and product personalization (normally only available to large corporations due to network effects); and they can challenge outmoded demographic tools used by policy makers.

Neo-demographics will initially focus on three types of digital footprint data: Energy, Communications and Movement. Cutting edge mathematical techniques are being developed that will mine cycles, motifs and features within our datasets, while vitally maintaining privacy boundaries of the individual. In association with sociologist expertise, useful characterizations of human behaviour will be generated that are  not only useful, but interpretable  and add capability to the digital economy as a whole.

For more details please contact Dr J. Goulding: james.goulding@nottingham.ac.uk