A method for retail product selection using data mining

Abstract

Product assortment planning is considered as one of the important problems in the retail business. Traditional approaches to product selection in the assortment are largely based on individual product popularity or margins. More recent research works in the literature utilize the cross selling potential of products to improve profitability of the overall assortment. This paper builds on the extant literature and proposes a new product selection method for assortment planning. The proposed method makes use of association rule mining for better assortment planning. Our method is evaluated on a real-life retail dataset and the results are found to be quite promising. A detailed comparative evaluation and sensitivity analysis is also presented to demonstrate the utility of the new method.