Categories Customers Using Data Mining for Effective Communication and Increased Profitability

Abstract

The aim of the categories of customers; identify their purchase behavior and finding similar patterns in their purchase database So that customers can buy and then the same customers for the loyalty to the organization's products. This research tries to focus on data mining models in the field of clustering, to categorize customers to improve customer relationship management and marketing strategies for each category of customers. Therefore, after reviewing the research and literature review of the research literature, RFM variables are used to category Customers and The data collected using the software SPSS Clementine was analyzed and K-means algorithm is used to cluster clients. Finally, using decision tree algorithm rules for each category of customers are extracted and the accuracy of the model was evaluated by the software. Obtained according to the rules that act on the basis of If-Then rules can be used to discover the hidden rules of payment data And to extract the pattern lies in each category. The results show that the proposed model can be used for marketing strategies