A Multi-Data Mining Approach for Shelf Space Optimization - Considering Customer Behaviour

Abstract

A well product-to-shelf assignment strategy can help customers easily find product items and dramatically increase the retailing store profit. Previous studies in this area usually applied the space elasticity to optimize product assortment and space allocation models. However, a well product-to-shelf assignment strategy should not only consider product assortment and space elasticity. Thus, this study develops a product-to-shelf assignment approach by considering both product association rules and traveling behaviour of consumer. Specifically, the first task of this research is to develop a method to discover traveling behaviour of consumer, which includes both product association rules and traveling behaviour of consumer, in the store. The second task is to construct and solve a product-to-shelf assignment model, based on the information provided in the first task. In this research, products are classified as major item, minor item and the others. Only minor will be reassigned. Experimental result shows our proposed method can reassign minor items to suitable shelves and increase cross-selling opportunity of major and minor items.