Modeling customers speed of movement from POS- and RFID-data

Abstract

The scope of this research lies in big data analysis. First of all, the urgency of the problem of not analyzing most of the information that can be recorded and analyzed to improve the functioning of any area of life is stated. Second, the case of a grocery store is used in order to identify useful dependencies. The data from the store contains information that characterizes consumer behavior. As a result, it becomes possible to evaluate the characteristics of interest, such as the average time spent by the buyer in the store, the average speed of movement and movement itself, the distance that the buyer travels during one visit to the store. A linear increasing dependence of the quantity of goods was also found, which, on average, is bought in each department from the inverse average speed of movement of a person in a given department. In the case of the analysis performed, the informativeness and importance of using big data for pursuing profit optimization goals become convincing enough.