Using workplace population statistics to understand retail store performance

Abstract

We explore the value of recently released workplace geographies and accompanying census-based workplace zone statistics (WZS) and an associated classification of workplace zones (COWZ). We consider how these data could support retailers in their operational and strategic decision making, including the evaluation of retail demand and retail store performance in localities where trade is driven by non-I UK The Co-operative Group characteristics using a series of case study stores within Inner London. We use empirical store trading data to identify store and product category level temporal sales fluctuations attributable to workplace populations. We also use census-derived flow data to identify the spatial origins of workplace population inflow. We identify that store performance exhibits characteristics attributable to demand driven by these populations. We conclude that workplace population geographies, WZS and the COWZ afford considerable potential for understanding drivers of store performance, observed store trading patterns and evaluation of retail store performance. We suggest that the next step is to build these populations and their micro geography spatial and temporal characteristics into predictive models and evaluate their potential for store performance evaluation and location-based store and network decision making within this sector.

Open all hours: spatiotemporal fluctuations in U.K. grocery store sales and catchment area demand

“…We demonstrate ( Figure 5) the mean diurnal sales profile for stores within each of our four clusters at 30 minute intervals during a typical day. Most notably stores in cluster traditional supermarket exhibit a very different temporal sales profile to our other store clusters, with peak revenues accounted for earlier within the day The temporal profile for stores in cluster workday convenience exhibits a lunchtime and late afternoon peak, corresponding closely to previous research which noted increased convenience store sales at these times of the day in localities which experience large workplace populations (Berry et al, 2016). The sales fluctuations exhibited in Figure 5 are thus likely to be driven by the changing spatial patterns of demand as consumers go about their daily lives.…”

“…Progress has been made here by a number of population geographers who have begun to work on estimation techniques for considering variations in population distributions over time, both daily and weekly for all manner of service provision (Bell, 2015, Martin et al, 2013, Martin, 2011. They argue time is a driving factor of local population change (Bell, 2015, Martin et al, 2015, resulting in highly localised market conditions over 24hr periods, with consequential impacts on store trade and revenue (Berry et al, 2016, Newing et al, 2013a, Schwanen, 2004. Census-based residential populations are a poor representation of actual daytime populations (Bell, 2015, Martin et al, 2015, and new work-based estimations were required and are of particular importance for city centre retailing (especially convenience stores).…”

“…The arrival and departure of tourists during peak holiday times throughout the year appeared to correspond with uplifts in store sales in peak holiday destinations, further highlighting the impact of time dependant demand and its spatial distribution on store sales, in addition to demonstrating temporal fluctuation over longer timescales. Furthermore, a study of transient workplace populations focused on demand fluctuations in city centres and indicated an impact on localised store sales at city centre stores over an hourly timescale (Berry et al, 2016). In this instance store sales were suggested to correspond with the availability of workplace demand arriving and departing in the various catchment areas.…”

Intra-urban location of stores and labour turnover in retail

“…Surprisingly, research related to labour turnover in retail has not attracted a lot of research over the years, although there are some important exceptions (see e.g. Arndt et al 2006;Booth and Hamer 2007;DeConick and Bachmann 2005;Foster, Whysall, and Harris 2008;Harrison and Gordon 2014;Heidig et al 2018;Hendrie 2004;Hicks 2007;Ramaseshan 1997;Siebert et al 2006;Tang et al 2014;Tian-Foreman 2009). Research on labour turnover and employee mobility, in general, tends to emphasize the importance of human resource management and working conditions at the workplace (see review below).…”

Developing a National Geodemographic Classification of Workplace Zones

“…An interim classification of WZs for England and Wales (EW), called COWZ-EW, has been developed by Cockings et al (2015; http://cowz.geodata.soton.ac.uk/cowzew/). This has already been utilised by Berry et al (2016) in retail, by Greater London Authority (2016) in local government, and by Martin et al (2018) in travel-to-work analysis. COWZ-EW has also since been refined by Singleton and Longley (2019) to produce a bespoke classification for London (UK).…”