Retail Market analysis in targeting sales based on Consumer Behaviour using Fuzzy Clustering - A Rule Based Mode

Product Bundling and offering products to customers is of critical importance in retail marketing. In general, product bundling and offering products to customers involves two main issues, namely identification of product taste according to demography and product evaluation and selection to increase sales. The former helps to identify, analyze and understand customer needs according to the demographical characteristics and correspondingly transform them into a set of specifications and offerings for people. The latter, concerns with how to determine the best product strategy and offerings for the customer in helping the retail market to improve their sales. Existing research has focused only on identifying patterns for a particular dataset and for a particular setting. This work aims to develop an explicit decision support for the retailers to improve their product segmentation for different settings based on the people characteristics and thereby promoting sales by efficient knowledge discovery from the existing sales and product records. The work presents a framework, which models an association relation mapping between the customers and the clusters of products they purchase in an existing location and helps in finding rules for a new location. The methodology is based on the integration of popular data mining approaches such as clustering and association rule mining. It focusses on the discovery of rules that vary according to the economic and demographic characteristics and concentrates on marketing of products based on the population. Keywords—Decision Support, Product Segmentation, Knowledge Discovery, Data Mining, Association Rule Mining, Clustering