Mining paths and transactions data to improve allocating commodity shelves in supermarket

How to deploy commodities for sale in different shelves in a supermarket in order to obtain better benefit for merchants with considering convenience for customers is an important topic in the retail area. In this paper, we present a new method for allocating commodity shelves in supermarket based on customers´ shopping paths and transactions data mining. Therein, customers´ shopping paths data can be obtained by shopping cart or basket, on which RFID (Radio Frequency Identification) tags located. And shopping transaction data can be obtained from POS (Point of Sales) machine. Through integrating and mining the frequent paths data and transactions data, See-Buy Rate, which refers to an approximate probability to purchase this commodity for customers when they see this commodity, can be calculated for each commodity. Based on SeeBuy Rate, we build benefit optimization model to obtain the optimal allocating solution with considering the profit, sales volume, and purchase probability of the commodity. At last, one computation example is illustrated to show how to apply this method to practice.