Clustering supermarkets: the role of experts
Introduction
As in Europe, the retail sector in Portugal is going through a restructuring phase. Several authors (e.g. Birkin et al., 2002; Dawson, 2000; Seth and Randall, 1999) identify such factors as increasing consumer mobility, increasing electronic commerce, changing household size, concentration of market power, home market saturation, and changes in planning legislation to justify the new trends in retailing. In the food retail, in particular, after an unprecedented period of hypermarkets growth, since the late 1970s, both in number and market share, it is now clear that hypermarket activity has slowed down significantly on behalf of the small or medium supermarkets (chain outlets including discount and hard discount chains) that nowadays present a larger dynamism.
In Portugal, market share data shows that since 1996 the supermarkets were the only ones to grow simultaneously in the number of outlets and in the volume of sales and, consequently, to increase the market share from 28% to 34% in the A.C. Nielsen universe. In 1997, the supermarkets reached the leadership and consolidated its expansion strategy. According to the most recent data, in 2001, supermarkets’ sales were already broadly superior to the sales in hypermarkets: 47% against just 35% of the total sales of outlets with alimentary products (see Fig. 1).
This change in food outlet type is also found in other European countries (Birkin et al., 2002). Much more stringent legislation and the fact that consumers are more demanding, force the retail groups to invest in outlets of smaller dimension, and so in a proximity and quality of goods and services strategy. This investment has a longer run return as well as smaller economies of scale, which forces careful decision-making (McGoldrick, 2000; Salvaneschi, 1996). Because smaller outlets are heterogeneous in aspects as location, dimension, and client behaviour, the definition of outlet clusters is essential in outlet performance and site evaluation.
In 2 Clustering supermarkets and the role of experts, 3 Data collection, 4 Methodological approach the next sections, an empirical classification of variables used in measuring supermarkets performance and the role of expert knowledge in clustering and marketing applications is presented. The data collection phase is described and three approaches for experts’ knowledge integration in supermarket cluster are explained. In Section 5 these approaches are compared and a cluster profiling is presented. This paper finishes with conclusions and a methodological discussion.
Section snippets
Clustering supermarkets and the role of experts
This work is part of an expansion study of a supermarket chain with small and medium dimension outlets and is intended to cluster the existent outlets. The classification is not just useful to evaluate the relative performance of different locations and outlet management, but also to use in analogy forecast methods for the identification of potential site locations (Mendes and Themido, 2004). For that purpose, several performance measures and other attributes were collected in a framework
Data collection
A large number of variables were collected in order to account for the diversity of attributes that may influence outlets performance evaluation. This diversity of base clustering data is considered essential (see for example Wedel and Kamakura, 2000). Of all data collection procedures, explained in the next sections, a total of 250 variables were obtained, measured in all kind of scales, and covering all the aspects in the suggested variable framework (Fig. 2).
Methodological approach
In spite of the data abundance following from last section, the number of outlet supermarkets was very small. This fact hindered the process of variable choice for the outlet clustering and respective characterization. To overcome this difficulty, three different procedures were considered for experts’ knowledge integration:
- 1.
In a priori integration approach, the experts were required to compare pairs of outlets and evaluate their dissimilarities in a perceptual scale. The dissimilarities matrix
Results comparisons and profiling
In order to reveal further differences between the cluster structures yielded from the three approaches, results are compared based in the sales turnover dispersion and in the proportion of explained variance. Finally, the supermarket clusters resulting from the interactive approach is profiled.
Discussion and conclusions
When a large number of variables are available for clustering a small amount of observations, the need to integrate experts’ knowledge in the clustering process becomes particularly relevant. In order to cluster a small number of supermarkets with a large number of available attributes three alternative approaches are presented which integrate experts’ knowledge: the a priori, the a posteriori and the interactive.
According to the analysts’ expectations, the a priori approach should integrate
Acknowledgements
The authors wish to express admiration and gratitude for the location experts that enthusiastically adopted this project and without whom this work would be impossible. The authors also wish to thank three anonymous referees for careful review and helpful suggestions and insights.
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