摘要：In today’s competitive climate, customer relationship management (CRM) has become an essential component in airline business strategies. CRM in the airline industry would be based on analyzing customer data in order to understand preferences and behavior. In this paper, we apply data mining techniques to real airline frequent flyer data in order to derive CRM recommendations and strategies. Clustering techniques group customers by services, mileage, and membership. Association rules techniques locate associations between the services that were purchased. Our results show the different categories of customer members in the frequent flyer program. For each group of these customers, we can analyze customer behavior and determine relevant business strategies. Knowing the preferences and buying behaviors of our customers allow our marketing specialist to improve campaign strategy, increase response and manage campaign costs by using targeting procedures, and facilitate cross-selling, and up-selling.
