Mining airline data for CRM strategies. (c2006)

Includes bibliographical references (leaves 114-115).

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Maalouf, Lena (author)
التنسيق: masterThesis
منشور في: 2006
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/10725/794
https://doi.org/10.26756/th.2006.37
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author Maalouf, Lena
author_facet Maalouf, Lena
author_role author
dc.creator.none.fl_str_mv Maalouf, Lena
dc.date.none.fl_str_mv 2006
2006-06-27
2011-10-17T09:18:27Z
2011-10-17T09:18:27Z
2011-10-17
dc.identifier.none.fl_str_mv http://hdl.handle.net/10725/794
https://doi.org/10.26756/th.2006.37
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv Lebanese American University
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Data mining
Airlines
Consumer behavior
Customer relations -- Management -- Data processing
dc.title.none.fl_str_mv Mining airline data for CRM strategies. (c2006)
dc.type.none.fl_str_mv Thesis
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/masterThesis
description Includes bibliographical references (leaves 114-115).
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oai_identifier_str oai:laur.lau.edu.lb:10725/794
publishDate 2006
publisher.none.fl_str_mv Lebanese American University
repository.mail.fl_str_mv
repository.name.fl_str_mv
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spelling Mining airline data for CRM strategies. (c2006)Maalouf, LenaData miningAirlinesConsumer behaviorCustomer relations -- Management -- Data processingIncludes bibliographical references (leaves 114-115).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.1 bound copy: xvi, 119 leaves; ill.; 30 cm. available at RNL.Lebanese American University2011-10-17T09:18:27Z2011-10-17T09:18:27Z20062011-10-172006-06-27Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/794https://doi.org/10.26756/th.2006.37eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/7942020-05-18T14:53:46Z
spellingShingle Mining airline data for CRM strategies. (c2006)
Maalouf, Lena
Data mining
Airlines
Consumer behavior
Customer relations -- Management -- Data processing
status_str publishedVersion
title Mining airline data for CRM strategies. (c2006)
title_full Mining airline data for CRM strategies. (c2006)
title_fullStr Mining airline data for CRM strategies. (c2006)
title_full_unstemmed Mining airline data for CRM strategies. (c2006)
title_short Mining airline data for CRM strategies. (c2006)
title_sort Mining airline data for CRM strategies. (c2006)
topic Data mining
Airlines
Consumer behavior
Customer relations -- Management -- Data processing
url http://hdl.handle.net/10725/794
https://doi.org/10.26756/th.2006.37