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Title: Evaluating alternative methods for forecasting convenience grocery store sales
Author: Hood, Nicholas Andrew
ISNI:       0000 0004 6061 2527
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2016
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Convenience grocery stores have become more commonplace in grocery retailing in Great Britain since the 1990s, with a substantial increase in the proportion of stores operated by the largest grocery retailers in Great Britain that can be defined as convenience grocery stores. Geographically, the convenience networks operated by the largest retailers are more spatially concentrated than their overall grocery networks bringing them into direct competition with retailers more traditionally associated with convenience retailing in some, but not all areas of Great Britain. As convenience stores have grown, so too has interest in site location research in finding techniques to best predict their success. This thesis is carried out with the support of Sainsbury’s and GMAP Ltd and specifically considers location based decision making for convenience grocery stores in Great Britain. Grocery retailers and their location planning teams employ models that are adept at predicting supermarket revenue. However, they find it more difficult to consistently estimate revenue to new or existing convenience store locations. From the outset of this research it was hypothesised that different locations in which convenience grocery stores are found may, in theory, require a different optimal methodology for forecasting revenue accurately. This thesis first offers a segmentation of the convenience market into 7 statistically distinct location types to begin to address this problem. Using the 7 location types as a framework, three methodologies for forecasting grocery sales are tested for their suitability for predicting convenience grocery sales in the different locations in which convenience grocery stores are found. These are: GIS buffer and overlay modelling, regression modelling and spatial interaction modelling. The different methods were found to have mixed success in predicting convenience store revenue. The regression model was found to be the most effective model on average whilst the spatial interaction model was found to be the best model for generating very good revenue forecasts. Contrary to popular belief, the GIS buffer and overlay model was outperformed by the regression model and spatial interaction model in the majority of locations in which convenience grocery stores are found. Overall, the modelling frameworks presented in this thesis provide a plausible kitbag of techniques which can be applied in different convenience location circumstances.
Supervisor: Clarke, Graham P. ; Clarke, Martin Sponsor: ESRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available