An Analysis of Drug Store Product Category Purchasing Using Block Clustering Method

Abstract

Drug stores have recently expanded in size and widened available product categories. Efficient store management requires accurate and rapid data analysis and in-depth understanding of customers' purchasing characteristics. In this study, we apply the block clustering method to point-of-sale (POS) data with identifications (IDs) in drug stores in Japan's Gifu region to examine the purchasing characteristics of product categories. Since this method can simultaneously evaluate customers and product categories, it is possible to investigate data more easily and quickly than other analytical methods used for customers and product categories. We construct blocks combining clusters of customer and product categories and identify the customers that stores should prioritize and specific product categories that should bolster marketing measures. This study proposes a method that retailers can employ when considering decisions on marketing activities such as product lineups, sales floor layouts, or pricing strategies.