Application of Data Mining Methods in Grouping Agricultural Product Customers

Abstract

The sheer complexity of the factors influencing decision-making has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. On the other hand, agricultural products need proper planning and decision-making, like any country's economic pillars. This is while the segmentation of customers and the analysis of their behavior in the manufacturing and distribution industries are of particular importance due to the targeted marketing activities and effective communication with customers. Customer segmentation is done using data mining techniques based on the variables of purchase volume, repeat purchase, and purchase value. This article deals with the grouping of agricultural product customers. Based on this, the K-means clustering method is used based on the Davies-Bouldin index. The results show that grouping customers into three clusters can increase their purchase value and customer lifespan.