A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales

Recent marketing research has suggested that in-store environmental stimuli, such as shelf-space allocation and product display, has a great influence upon consumer buying behavior and may induce substantial demand. Prior work in this area, however, has not considered the effect of spatial relationships, such as the shelf-space adjacencies of distinct items, on unit sales. This paper, motivated in great part by the prominent beer and diapers example, uses data mining techniques to discover the implicit, yet meaningful, relationship between the relative spatial distance of displayed products and the items' unit sales in a retailer's store. The purpose of the developed mining scheme is to identify and classify the effects of such relationships. The managerial implications of the discovered knowledge are crucial to the retailer's strategic formation in merchandising goods. This paper proposes a novel representation scheme and develops a robust algorithm based on association analysis. To show its efficiency and effectiveness, an intensive experimental study using self-defined simulation data was conducted. The authors believe that this is the first academically researched attempt at exploring this emerging area of the merchandising problem using data mining.