Analysis and simulation of sales receipt data in supermarkets

For the prediction of product sales, simulations of interactions of different individual customer behaviors in supermarkets are an appropriate approach. In order to parameterize the simulation close to reality, analysis of shopping baskets based on real sales receipt data is one of the key steps. The combination of the data analysis and simulation is in the scope of this paper Analysis of real sales receipt data was done by determining so-called customer prototypes, which are representatives of different customer classes. In order to make the simulation insensitive against short transient changes, longitudinal analysis and clustering was carried out, additionally to the common cross section analysis. Longitudinal analysis was necessary to calculate similarities between shopping basket clusters of different points in time. The resulting stabile customer prototypes then were used to parameterize a supermarket simulation. As result, this paper shows the analysis of consumer data as well as the parameterizing and validation of a supermarket simulation. The combining of data mining with simulation results in a better accuracy of analysis and the ability to evaluate influencing factors that can not be extracted directly through data mining.