Authors: Paul Longley*, University College London, Tim Rains, University College London
Topics: Quantitative Methods, Business Geography
Keywords: consumer data, big data, retail
Session Type: Paper
Start / End Time: 1:10 PM / 2:50 PM
Room: Congressional A, Omni, West
Presentation File: No File Uploaded
The UK Consumer Data Research Centre uses data science to mine store loyalty account data in order to better understand purchasing behaviour in relation to retail store networks and online shopping. The when and where of retail transactions and order fulfilment provide valuable indicators of the activity spaces of individuals and households, particularly in urban settings where different store formats are available to fulfil regular and convenience needs. Such data may be used alongside conventional census measures of travel-to-work and neighbourhood community structure to better understand the activity patterns of neighbourhood residents in which hardship (deprivation) is pronounced. Consumer data are updated in real time, making it possible to investigate changes over any convenient timescale.
Here we use UK store loyalty card data to investigate the geography of neighbourhood shopper activity patterns, including: the share of face-to-face transactions conducted during the working day; the share of multi-channel or online transactions; the spacing of transactions over time and space; and the mix of large, top up and ‘for now’ transactions. We pay particular attention to household purchases that are tagged using store loyalty accounts, and benchmarks of individual or household consumption. We pay special attention to the case of London, where metropolitan lifestyles are served by a ‘convenience culture’ of store formats in addition to conventional high street and out of centre shops, and online shopping behaviours are frequently in evidence.
We evaluate the value of customer loyalty card data to supplement conventional small area measures of community structure and hardship.