STANA

The introduction of computerized technology particularly the electronic Point of Sales (POS) to supermarkets confronted marketing decision makers with vast resources of data. POS databases are multi-dimensional comprised of monthly and daily transaction records therefore time-consuming to analyze. This has led to a new desire on the part of managers to understand the behavioral demand for majority of customers and make better marketing decisions based on new variables. This research presents the use of data mining models Kmeans clustering and Apriori Association rule to construct pricing and store layout decision support system for retail or marketing managers.