Rains, Timothy John Harding;
(2020)
Big Consumer Data: Understanding shopping patterns using loyalty cards.
Doctoral thesis (Ph.D), UCL (University College London).
Abstract
Big Consumer Data offer geographic research opportunities to better understand a range of phenomena in unprecedented detail at higher levels of spatial and temporal granularities. However, these and other new forms of Big Data are different from traditional sources, coming without many of the quality controls and reference points that are associated with more traditional forms of data. Efforts must first be made to understand the sources and operation of bias, the nature and contexts of the Consumer Data, and the potential implications for wider re-use of the data. However, few research findings using new forms of data examine these issues explicitly. This thesis therefore investigates such problems in detail using 52 weeks of loyalty card data, totalling more than a billion transactions from a major UK grocery retailer. This includes the degree to which loyalty cards are swiped when conducting different forms of transactions, the level of ‘completeness’ of household’s purchases which are recorded within the data, given the highly competitive nature of the UK grocery market, and the impact that this has on representation of different regions and geodemographic types. Having established techniques to ground truth loyalty card data, the thesis proceeds into a broad examination of shopping patronage patterns including the temporal patterns, mix of transaction types, and locations that shopping is conducted in. This results in a small-area classification that further extends the data lifecycle of the loyalty card data. Collectively, these resources can be used in analysis of both retail geography and social deprivation by supplementing night-time geographies with activity-based measurement of behaviours in order to extend understanding of issues such as food hardship and convenience culture. The thesis ends with an example application examining shopping patterns in a series of previously underfunded neighbourhoods.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Big Consumer Data: Understanding shopping patterns using loyalty cards |
| Event: | UCL |
| Language: | English |
| Additional information: | Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10094370 |
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