The role of geography in data integration and predictive analytics 

 Tony Lea, Ph.D. Senior VP and Chief Methodologist, Environics Analytics

In a world where data governance and privacy concerns make it increasingly unable to use data on individuals it is important to have an alternative that works. This paper explains how the use of aggregated data for small scale geography can provide much of the solution in a privacy friendly way. This is called geodemography. After working through some theory, an example is presented that shows how spatial autocorrelation and segmentation can facilitate effective data integration and powerful predictive models. This approach allows users to have thousands of potential predictive variables – socioeconomic and demographic, behavioural, psychographic etc. – beyond those available from customer transactional and tombstone files Not only does this provide better targeting tools but equally important the ability to generate effective marketing communication strategies.