Mining airline data for CRM strategies. (c2006)

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dc.contributor.author Maalouf, Lena
dc.date.accessioned 2011-10-17T09:18:27Z
dc.date.available 2011-10-17T09:18:27Z
dc.date.copyright 2006 en_US
dc.date.issued 2011-10-17
dc.date.submitted 2006-06-27
dc.identifier.uri http://hdl.handle.net/10725/794
dc.description Includes bibliographical references (leaves 114-115). en_US
dc.description.abstract In today's competitive climate, Customer Relationship Management (CRM) has become an essential component in the airline business strategies. Building CRM in the airline industry requires a comprehensive view of customer behavior. This view has to be based on analyzing customer data in order to understand customer preferences and learn from his/her behavior. In this thesis, 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 detennine 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. Furthermore, we explore the characteristics of data mining algorithms for this application and uncover relative merits of the algorithm employed. en_US
dc.language.iso en en_US
dc.subject Data mining en_US
dc.subject Airlines en_US
dc.subject Consumer behavior en_US
dc.subject Customer relations -- Management -- Data processing en_US
dc.title Mining airline data for CRM strategies. (c2006) en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.idnumber 200202846 en_US
dc.author.commembers Dr. Faisal Abu Khzam
dc.author.commembers Dr. Toufic Mezher
dc.author.woa OA en_US
dc.description.physdesc 1 bound copy: xvi, 119 leaves; ill.; 30 cm. available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Nashat Mansour
dc.identifier.doi https://doi.org/10.26756/th.2006.37 en_US
dc.publisher.institution Lebanese American University en_US

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