Optimization of retail clusters by improving individual store performance

Clustering is a common phenomenon seen all around the world in industries, and the service sector. Clustering is a complicated case in retail, and mainstream literature is populated with studies that define store performance for single stores; however, not much is available when they are in clustering, as the conventional trading boundaries, which form the area in which the store´s influence extends, cannot be defined. The present study was conducted to improve the overall performance of the entire cluster, by dealing with individual stores. It was conducted in a large retail cluster dealing exclusively in stationary. The store facilities are analysed using fuzzy linguistic modelling from both, the customer and the retailers stand point. A model of such clusters is then prepared for the current demographic. The model generated aims to provide a holistic approach to grade the facilities available in order to determine returns. This also gives a framework for retailers to upgrade their existing facilities according to the cluster characteristics, thus improving not only individual performance, but also the performance of the cluster.