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The Application of Data Mining to Support Customer Relationship . Management at Ethiopian Airlines

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dc.contributor.advisor Getachew, Mesfin (PhD)
dc.contributor.author Abera, Denekew
dc.date.accessioned 2020-06-01T10:15:23Z
dc.date.available 2020-06-01T10:15:23Z
dc.date.issued 2003-06
dc.identifier.uri http://etd.aau.edu.et/handle/123456789/21379
dc.description.abstract Airlines are being pushed to understand and quickly respond to the individual needs and wants of their customers due to the dynamic and highly competitive nature of the industry. Most airlines use frequent flyer incentive programs and maintain a database of their frequent flyer customers to win the loyalty of their customers, by awarding points that entitle customers to various travel benefits. Customer relationship management (CRM) is the overall process of exploiting customer- related data and information, and using it to enhance the revenue flow from an existing customer. As part of implementing CRM, airlines use their frequent flyer databases to get a better understanding of their customer types and behavior. Data mining techniques playable here by allowing to extract important customer information from available databases. This study is aimed at assessing the application of data mining techniques to support CRM activities at Ethiopian Airlines. The subject of this case study, the Ethiopian Airlines' frequent flyer program, has a database that contained individual flight activity and demographic information of over 35,000 program members Having the objective of filling the gap left by a related research, which was carried out by Henok (2002), this study has used the data mining database prepared by Henok (2002). In the course of using the database to attain the objective of this research, a data preparation tasks such as driving new attributes from the existing original attributes, defining new attributes and then preparing new data tables were done.The data mining process in this research is divided into two major phases. During the first phase, since there has been an attempt to use three different data mining software, data was prepared and formatted into the appropriate format for the respective data mining software to be used. The second phase, which is model building phase, was addressed in two sub-phases, the clustering sub-phase and the classification sub-phase, the major contribution of this study. In the clustering sub phase the K-means clustering algorithm was used to segment individual customer records into clusters with similar behaviors. In the classification sub-phase, J4.B and J4.B PART algorithms were employed to generate rules that were used to develop the predestined model that assigns new customer records into the corresponding segments As a final output of this research, a prototype of Customer Classification System is developed. The prototype enables to classify a new customer into one of the customer clusters, generate cluster results, search for a customer and find the cluster where the customer belongs, and also provides with the description of each customer clusters The results from this study were encouraging and confirmed the belief that applying data mining techniques could indeed support CRM activities at Ethiopian Airlines. In the future, more segmentation and classification studies by using a possible large amount of customer records with demographic information and employing other clustering and classification algorithms could yield better results en_US
dc.language.iso en en_US
dc.publisher Addis Ababa University en_US
dc.subject Information Science en_US
dc.title The Application of Data Mining to Support Customer Relationship . Management at Ethiopian Airlines en_US
dc.type Thesis en_US


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