A temporal data mining approach for shelf-space allocation with consideration of product price

Marketing research has suggested that the in-store stimuli such as shelf-space allocation and product assortment have great influence on customer buying behaviour and may induce sales by maximizing impulse buying and cross-selling. The previous studies, however, have ignored the effect of product price in shelf-space arrangement. That is, they study the relationship between products and their simultaneous sales in a static fashion, disregarding the dynamic changes of their prices. The changes in product price may change the association between products such as complementarity and substitutability relationships. Consequently, it would affect the applied strategies of shelf allocation. In this paper a new approach is developed to optimally select and price the products and allocate them to shelf space with consideration of their prices. This paper takes advantage of data mining techniques, association rules, to find relationships between products regarding to their prices. Finally, to show the efficiency and effectiveness of the proposed approach, the experiment on real world data is executed.