Supermarket layout planning with shopping approaches and data analysis methods

Planning the store layout is significant in supermarkets. Analysis of supermarket sales transaction data is an important key to understanding customer behaviour and making inferences. Through today's technological developments, data mining method is used to obtain meaningful results from very large data sets. In this research, different shopping approaches to improve supermarket store layout are experimentally tested, data analytics are applied and a design shopping layout framework is proposed. Firstly, three different shopping layout strategies are designed and experimentally tested to provide a better layout for the supermarket and increase its profitability. In the next step, the items in the sales transaction data are associated to form the association rules of the product groups selected by customers during their shopping trip in the supermarket, and the Multidimensional Scale (MDS) method is applied to place each group of items with relational similarity in the store area. With the results obtained from the shopping approaches applied in real time, space and with customers and from the data analysis of all product groups on the supermarket shelves, a different supermarket layout, both consumer-oriented and business-oriented, is presented. It is thought that the store layout approach developed with contrasting shopping theories will be a useful guide for both academia and the retail sector.