Application of Sequence Alignment Method to Product Assortment and Shelf Space Allocation

In retailing, decisions about product assortment and shelf space allocation have a significant effect on customers’ purchasing decisions. Traditionally, researchers usually employed the space elasticity to optimize product assortment and space allocation models. However, the large number of parameters requiring to estimate and the nonlinear nature of space elasticity can reduce the efficacy of the space elasticity based models. Instead of space elasticity, this paper utilizes a data mining approach, Sequence Alignment Method (SAM), to resolve the product assortment and allocation problems in retailing. In our approach, the SAM is applied to explore the relationships between product categories and is compared to association rule mining. Experimental results show that SAM achieves better quality than that of association rule mining and can generate very useful information to shelf space management.