Nowadays, the global marketing strategy is making business extremely competitive,rndynamic and subject to rapid change. Hence, businesses should be highly concerned tornneeds and wants of their customers in order to respond accordingly. CustomerrnRelationship Management is the overall process of exploiting customer - relatedrninformation and using it to enhance the revenue flow from an existing customer. Datarnmining techniques are used to extract important customer information from available datarnbases.The major objective of this study is testing the application of data mining techniques tornsupport CRM activities for Ethiopian shipping Lines. The customer profile file of ESLrncontains individual shipment activities of more than 20 ,000 records, out of which aboutrn4,000 are unique customers. After the data is collected, the necessary processing stepsrnare conducted on it in order to make it applicable for the modeling process.K - Means clustering algorithm was used to segment individual customer records in tornclusters with similar behaviors. Different parameters were used to run the clusteringrnalgorithm before arriving at customer segments that made business sense to domainrnexperts . After the clustering is made, decision tree classification techniques werernemployed to generate rules that could be used to assign new customer record to thernsegments.The results from this study were encouraging which strengthened the belief that applyingrndata mining techniques could in deed support CRM activities at Ethiopian shippingrnLines . In the future , more segmentation studies using demographic information andrnemploying other clustering algorithms could yield better results