Every customer transaction tells a story. Market based analysis enables capture of those stories in order to write a personal book on retailing. Ranging from retail giants to small departmental store to stay relevant in the race, it is high time the data driven decision making model has to be employed at the business setup. The main objective of this project is to help a small neighbourhood departmental store understand the behavioural pattern of its customers, understand what products to place together to increase the basket size of the customers and to achieve these, design the store layout accordingly from its point of sale (POS) data. For this purpose, four months’ transaction data was collected from the store. The objective is achieved through an algorithm called APRIORI or in other words, association rule mining that helps in affinity analysis of products. This algorithm was successfully put to use through the tools R-Studio, MS-Excel and XL-STAT (An Excel add-in). Insights on what products to place together to increase sales, which segment of customers to concentrate on (retail or wholesale), which products should they improve their sales and what they can do in future for better decision making were given to them. Also, a store layout that maximizes overall lift was designed to increase their profit, revenue and sales.
Volume 11 | 04-Special Issue
Pages: 2418-2431