Visitor expenditure estimation for grocery store location planning: a case study of Cornwall

The International Review of Retail, Distribution and Consumer R

Davies

et al. 2016

Self Cite

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.

Section: Workplaces As a Driver Of Retail Demandmentioning

confidence: 99%

Section: Census-derived Workplace Population Geographies and Populatimentioning

confidence: 99%

Using workplace population statistics to understand retail store performance

Berry

The International Review of Retail, Distribution and Consumer R

Davies

et al. 2016

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“…In coastal regions of the UK for example, the store-level demand uplift for groceries driven by tourism can be as high as 200% in key months of the tourist season (Newing et al, 2013b). This form of demand is unique in that it is highly concentrated spatially, exhibiting clear clusters around major resorts and destinations, whilst also giving rise to a highly seasonal pattern of fluctuation driven by institutional factors such as school holidays alongside short-term fluctuations owing to the weather and local events (Newing et al, 2013a). Thus models which do not include tourist demand may seriously under-predict the revenue estimates which are such an important output from the SIMs.…”

Section: Spatial Interaction Modelling For Retail Location Analysismentioning

confidence: 99%

Developing and Applying a Disaggregated Retail Location Model with Extended Retail Demand Estimations

Clarke

2014

Geographical Analysis

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AcknowledgementThis research is supported by an ESRC CASE Award (2010)(2011)(2012)(2013) as part of RIBEN. Data have been provided by a collaborating retailer who guided the initial selection of study stores. AbstractThe Spatial Interaction Model (SIM) is an important tool for retail location analysis and store revenue estimation, particularly within the grocery sector. However, there are few examples of SIM development within the literature that capture the complexities of consumer behaviour or discuss model developments and extensions necessary to produce models which can predict store revenues to a high degree of accuracy. This paper reports a new disaggregated model with more sophisticated demand terms which reflect different types of retail consumer (by income or social class), with different shopping behaviours in terms of brand choice. We also incorporate seasonal fluctuations in demand driven by tourism, a -2 -major source of non-residential demand, allowing us to calibrate revenue predictions against seasonal sales fluctuations experienced at individual stores. We demonstrate that such disaggregated models need empirical data for calibration purposes, without which model extensions are likely to remain theoretical only. Using data provided by a major grocery retailer, we demonstrate that statistically, spatially and in terms of revenue estimation, models can be shown to produce extremely good forecasts and predictions concerning store patronage and store revenues, including much more realistic behaviour regarding store selection. We also show that it is possible to add a tourist demand layer which can make considerable forecasting improvements relative to models built only with residential demand.

“…It changes by season (i.e., larger in August compared to January) therefore visitor and residential visits to these sites does increase seasonally. The seasonal change in visitor to resident retail footfall is informed by previous work by Newing et al [22,35] analysing loyalty card data for stores within this region. The calculation of overnight visitors as a separate type of origin centroid dataset is described in the next section.…”

Section: Spatiotemporal Population Modellingmentioning

confidence: 99%

“…A major refinement in this approach, adopted by this paper, is the inclusion of seasonally varying overnight visitor population estimates developed by Newing et al [22]. These have been integrated within the flexible Population 24/7 data framework [23] which can be used to produce spatiotemporal gridded population estimates using variable kernel density estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management

Smith

Quinn

et al. 2015

IJGI

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Human populations are not static or uniformly distributed across space and time. This consideration has a notable impact on natural hazard analyses which seek to determine population exposure and risk. This paper focuses on the coupling of population and environmental models to address the effect of seasonally varying populations on exposure to flood risk. A spatiotemporal population modelling tool, SurfaceBuilder247, has been combined with LISFLOOD-FP flood inundation model outputs for a study area centred on the coastal resort town of St Austell, Cornwall, United Kingdom (UK). Results indicate strong seasonal cycles in populations and their exposure to flood hazard which are not accounted for in traditional population datasets and flood hazard assessments. Therefore, this paper identifies and demonstrates considerable enhancements to the current handling of spatiotemporal population variation within hazard exposure assessment and disaster risk management.