Trasberg, Terje; (2022) Exploring Britain’s Retail Landscape Using New Forms of Data. Doctoral thesis (Ph.D), UCL (University College London).

Text TRASBERG_T_PhD.pdf - Accepted Version Access restricted to UCL open access staff until 1 December 2024. Download (5MB)

Abstract

This thesis discusses the opportunities afforded by novel population datasets in the field of retail analysis. This work benefits from access to several commercially sensitive datasets and aims to harness these datasets to address aspects of the retail environment that have been understudied due to the lack of suitable data. The predominant aims of this work are firstly to address issues of uncertainty surrounding novel datasets by discussing the inherent biases and data quality, and secondly to provide practical use cases and examples for repurposing this data in research as well as in the retail industry. In Chapter 4, a measure of footfall derived from Wi-Fi sensors was combined with a dataset providing store transaction volumes to create an accurate sales forecasting model that can be used to evaluate store performance, as well as to predict the revenue of potential store sites in retail location analysis. The results of the analysis showed that footfall does have an impact on retail sales, but the strength of the correlation depends on the retail type. This thesis also demonstrates the value of integrating novel sources of data with more traditional data sources. In Chapter 6, data collected using mobile phone apps that detail human flows and interactions was combined with census data to provide context to the mobility patterns. Linking activity data and the socio-economic characteristics proved to be useful for understanding how the COVID-19 lockdown guidelines exposed the socio-spatial fragmentation between urban communities in Greater London. The results of this analysis, summarised in Chapter 7, showed that activities declined more in premium shopping destinations and less in suburban high streets. It is hoped that this research will be of equal value to the research community looking to widen the understanding of retailing environments and novel data sources as well as to retailers who are looking for new ways to measure and monitor the changing dynamics of consumer behaviour.

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