چكيده به لاتين
In today's competitive world, accurate identification of customer needs and preferences is the key to business success. Customer clustering is one of the effective ways to group customers based on their common characteristics. With the aim of providing targeted marketing strategies, this research examines the clustering of customers of rail transport companies using data related to bills of lading issued in 1401. Primary data includes 1,025,660 registered transactions from 176 customers. Each row in this data represents the information of a bill of lading. At first, we got to know the organization and then pre-processed and prepared the data. In this research, the K-Mean algorithm is used as one of the most widely used clustering algorithms. Based on the silhouette criterion, the appropriate number of clusters has been obtained. Also, the RFM (Recency, Frequency, Monetary) model has been used to determine the characteristics of each customer, including purchase recency, purchase frequency, and purchase value. By combining these two methods, customers are divided into 4 distinct clusters. The cluster of big customers has 58 customers, the cluster of valuable customers includes 30 customers, the cluster of low-value customers has 30 customers, and the cluster of exhausted customers includes 57 customers. After clustering, the characteristics of each cluster are carefully analyzed and based on the results, marketing strategies suitable for each cluster are proposed. In order to derive these strategies, in addition to examining past studies, experts from rail freight companies have been used. For this purpose, a questionnaire was designed and provided to experts in this field. The strategies extracted from this questionnaire briefly include special offers, discounts, loyalty programs and appropriate communication channels for each group of customers, and finally we matched the results of the designed questionnaire with clustering. The results of this research show that customer clustering based on the data of railway waybills and using the RFM model and the K-Mean algorithm can help rail transport companies to gain a deeper understanding of their customers and implement effective marketing strategies. Using this information, you can retain key customers and attract new customers with high added value. By focusing on high-value-added customers and providing customized services, you can increase customer loyalty and expand market share.
