Retail Marketing Segmentation and Customer Profiling for Forecasting Sales

摘要

Product Bundling and offering products to customers is of critical importance in retail marketing. In this paper, a predictive mining approach is presented that predicts sales for a new location based on the existing data. The major issue lies in the analysis of sales forecast based on the dependencies among the products and the different segment of customers, which helps to improve the market of the retail stores. 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 an entirely new location. A novel methodology and model are proposed for accomplishing the task efficiently. The methodology is based on the integration of the popular data mining approaches such as clustering and association rule mining. It focuses on the discovery of rules that vary according to the economic and demographic characteristics and concentrates on marketing the products based on the population.