Consumption universes based supermarket layout through association rule mining and multidimensional scaling

The success of retail business is influenced by its fast response and its ability in understanding consumers' behaviors. Analysis of transaction data is the key for taking advantage of these new opportunities, which enables supermarkets to understand and predict customer behavior, has become a crucial technique for effective decision-making and strategy formation. We propose a methodological framework for the use of the knowledge discovery process and its visualization to improve store layout. This study examines the layout strategy in relation to supermarket retail stores and assists managers in developing better layout for supermarkets. We use the buying association measure to create a category correlation matrix and we apply the multidimensional scale technique to display the set of products in the store space. This is a new approach to supermarket layout from industrial categories to consumption universes that is consumer-oriented store layout approach through a data mining approach. This framework is useful for both academia and retail industry. For industry professionals, it may be used to guide development of successful layout. Retailers can utilize the proposed model to dynamically improve their in-store conversion rate. As the empirical study, a practical application proceeded for Migros Turk, a leading Turkish retailing company.