Customer segmentation through multiple correspondence analysis

The aim of the paper is to highlight the categorical methods' potential of application which could be useful when drawing companies marketing strategies. Multivariate statistical techniques have always been used for quantitative analysis in marketing decision support systems (MDSS), nevertheless, Big data now provide numerous qualitative variables whose processing requires the use of categorical statistical methods. Dealing with Big data in marketing research, we carried out a Multiple Correspondences Analysis to show how to investigate the intricate relations among the categorical variables observed and their categories with the specific regard to consumers purchasing behavior data. These analyses allow to detect the essential aspects as a rational basis for adopting more effective marketing strategies.