Customer Movement Measurement Based On Pos And Rfid-Data Analytics

The scope of this research lies in big data analysis. Two datasets from a grocery store are used in order to identify useful dependencies, describing customer movement from RFID and POS-data. 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 becomes convincing enough.