Using workplace population statistics to understand retail store performance. Issue 4 (7th August 2016)

• Record Type: Journal Article
• Authors: Berry, Tom; Newing, Andy; Davies, Deborah; Branch, Kirsty
• Is Part Of: International review of retail, distribution and consumer research. Volume 26:Issue 4(2016)
• Page Start: 375
• Page End: 395
• Publication Date: 2016-08-07
• Subjects: Workplace derived trade -- workplace zone statistics -- classification of workplace zones -- co-operative Food -- Inner London; Retail trade -- Periodicals; Physical distribution of goods -- Periodicals; Consumers -- Research -- Periodicals; 381.1
• DOI: 10.1080/09593969.2016.1170066
• Languages: English
• ISSNs: 0959-3969

Abstract

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-residential demand. In collaboration with major UK grocery retailer The Co-operative Group we explore the relationship between workplace population composition and store trading 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.