The airline industry is highly competitive, dynamic and subject to rap id change. As a result,rnairlines are being pushed to understand and quickly respond to the individual needs and wants ofrntheir customers. Most airlines use frequent flyer incentive programs to win the loyalty of theirrncustomers, by awarding points that entitle customers to various travel benefits. Furthermore,rnthese airlines maintain a database o f their frequent flyer customers.rnrnCustomer relationship management (CRM) is the overall process of exploiting customer- relatedrninformation and using it to enhance the revenue flow from an existing customer. As part ofrnimplementing CRM, airlines use their frequent flyer data to get a better understanding of theirrncustomer types and behavior. Data mining techniques are used to extract important customerrninformation from available databases.rnThis study is aimed at testing the application of data mining techniques to support CRM activitiesrnat Ethiopian Airlines. The subject of this case study is Ethiopian Airlines' frequent flyerrnprogram 's database, which contains individual !"light activity and demographic information ofrnmore than 22,000 program membersrnThe data mining process was divided in to three major phases. During the first phase, data wasrncollected from different sources since the frequent flyer database lacked revenue data, which wasrnessential for the study's goal of identifying profitable customer segments. The data preparation onrnphase was next, where a procedure was developed to compute and fill-in for missing revenuernvalues. Moreover, data integration and transformation activities were performedrnIn the third phase, which is model building and evaluation, K-means clustering algorithm wasrnused to segment individual customer records into clusters with similar behaviors. Differentrnparameters were used to run the clustering algorithm before arriving customer segments thatrnmade business sense to domain experts. Next, decision tree classification techniques werernemployed to generate rules that could be used to assign new customer records to the segments.rnThe results from this study were encouraging, which strengthened the belief that applying datarnmining techniques could indeed support CRM activities at Ethiopian Airlines. In the future, morernsegmentation stud issuing demographic in formation and employing other clustering algorithmsrncould yield better results.