Consumer Data Research Centre

Case Studies

The aim of the Centre is to provide a national service to support a wide range of users to carry out research projects that provide fresh perspectives on the dynamics of everyday life, problems of economic well-being and social interactions in cities.  To demonstrate the value of our data holdings,  researchers at our host universities are currently undertaking Big Data exemplar research projects in the following areas:


 CDRC_Icons_July2015_Retail_Grey   Big Data and Retail

The retail industry is constantly changing; analysing consumer data can help us to better understand the industry in terms of business resilience, customer mobility and changing buying habits. CDRC is producing a highly detailed picture of patterns of consumer behaviour that is relevant to businesses, academia and wider society. Example projects include:


   Big Data and Urban Mobility

High quality movement data from a range of transport sectors (rail, car, bike etc.) allow researchers to better understand travel flows and commuter/passengers numbers and journeys. The detailed patterns of daily travel behaviour generated by the analysis of these data are essential in supporting transport providers and infrastructure planners in their efforts to design effective, affordable and sustainable networks. Example projects include:




 Big Data and Energy

The nature of household energy consumption is receiving increasing attention amongst academics and industry stakeholders. Smart meter technology is making it possible to monitor geotemporal patterns of energy consumption and gauge the sensitivity of energy consumption to price and other probable household and environmental circumstances. Issues are of interest to geographers, engineers, economists and a range of policy analysts include:

• Market segmentation of the domestic energy market according to patterns of energy consumption over time
• Visualising the geographic and temporal patterns of energy consumption, in the context of the supply of energy from different sources
• Coupling of energy consumption data with other data sources pertaining to demographic characteristics, energy efficiency, weather/climate and built environment attributes
• Developing different energy consumption scenarios and projections, in the context of alternative energy policy and pricing regimes



Big Data and Names and Ethnicity

CDRC is analysing data on people’s first names and surnames as potential indicators of demographic characteristics, including ethnicity. Such characteristics have been shown to have an identifiable correspondence with aspects of behaviour, including retail purchases and activity patterns. This can help retail organisations to profile groups of customers.



Big Data and Ethical/Sustainable

A large proportion of the population claim that they are motivated to consume ethically, and it is estimated that the UK’s ethical market is now worth over £32 billion. However research shows that the numbers of consumers that consistently act on those ethical motives are much fewer. Example projects include:

  • Ethical appeals – the effects of sustainability versus price discount type appeals
  • Sustainable consumption behaviour – tracking actual purchase behaviour against consumer pro-environmental attitudes
  • Understanding and informing consumers financial decisions 


   Big Data and Health

Health concerns such as obesity are a growing concern in the UK. Through the analysis of consumer data researchers are able to better understand how the environment in which we live influences our behaviours, which can lead to these health issues. Example projects include:


CDRC_Icons_July2015_Crime_Grey_125x125   Big Data and Policing

Analysis of social media data allows us to identify and better understand crime hot spots. This research explores ways to identify which times and places have the largest concentrations of potential crime victims, and hence which hotspots pose the most significant problems. The aim is to help incident response and crime reduction organisations to focus their resource more effectively. Example projects include:

  • How well can ‘new’ sources of data predict instances of crime?
  • Retail and geo-temporal patterns of criminal activity