Browsing by Author "Woubishet Henock"

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    Application of Data Mining Techniques to Support Customer Relationship Management At Ethiopian Airlines
    (Addis Ababa University, 2002-07) Woubishet Henock; Meshesha Million (Ato); Abebe Ermias (Ato); Tadesse Nigussie (Ato)
    The airline industry is highly competitive, dynamic and subject to rap id change. As a result, airlines are being pushed to understand and quickly respond to the individual needs and wants of their customers. Most airlines use frequent flyer incentive programs to win the loyalty of their customers, by awarding points that entitle customers to various travel benefits. Furthermore, these airlines maintain a database o f their frequent flyer customers. Customer relationship management (CRM) is the overall process of exploiting customer- related information and using it to enhance the revenue flow from an existing customer. As part of implementing CRM, airlines use their frequent flyer data to get a better understanding of their customer types and behavior. Data mining techniques are used to extract important customer information from available databases. This study is aimed at testing the application of data mining techniques to support CRM activities at Ethiopian Airlines. The subject of this case study is Ethiopian Airlines' frequent flyer program 's database, which contains individual !"light activity and demographic information of more than 22,000 program members The data mining process was divided in to three major phases. During the first phase, data was collected from different sources since the frequent flyer database lacked revenue data, which was essential for the study's goal of identifying profitable customer segments. The data preparation on phase was next, where a procedure was developed to compute and fill-in for missing revenue values. Moreover, data integration and transformation activities were performed In the third phase, which is model building and evaluation, K-means clustering algorithm was used to segment individual customer records into clusters with similar behaviors. Different parameters were used to run the clustering algorithm before arriving customer segments that made business sense to domain experts. Next, decision tree classification techniques were employed to generate rules that could be used to assign new customer records to the segments. The results from this study were encouraging, which strengthened the belief that applying data mining techniques could indeed support CRM activities at Ethiopian Airlines. In the future, more segmentation stud issuing demographic in formation and employing other clustering algorithms could yield better results.
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    Application of Data Mining Techniques to Support Customer Relationship Management at Ethiopian Airlines
    (Addis Ababa University, 2002-07) Woubishet Henock; Abebe Ermias (Ato); Meshesha Million; Tadesse Nigussie
    The airline industry is highly competitive, dynamic and subject to rapid change. As a result, airlines are being pushed to understand and quickly respond to the individual needs and wants of their customers. Most airlines use frequent flyer incentive programs to win the loyalty of their customers, by awarding points that entitle customers to various travel benefits. Furthermore, these airlines maintain a database of their frequent flyer customers. Customer relationship management (CRM) is the overall process of exploiting customerrelated information and using it to enhance the revenue flow from an existing customer. As part of implementing CRM, airlines use their frequent flyer data to get a better understanding of their customer types and behavior. Data mining techniques are used to extract important customer information from available databases. This study is aimed at testing the application of data mining techniques to support CRM activities at Ethiopian Airlines. The subject of this case study is Ethiopian Airlines’ frequent flyer program’s database, which contains individual flight activity and demographic information of more than 22,000 program members. The data mining process was divided into three major phases. During the first phase, data was collected from different sources, since the frequent flyer database lacked revenue data, which was essential for the study’s goal of identifying profitable customer segments. The data preparation phase was next, where a procedure was developed to compute and fill-in for ix missing revenue values. Moreover, data integration and transformation activities were performed. In the third phase, which is model building and evaluation, K-means clustering algorithm was used to segment individual customer records into clusters with similar behaviors. Different parameters were used to run the clustering algorithm before arriving at customer segments that made business sense to domain experts. Next, decision tree classification techniques were employed to generate rules that could be used to assign new customer records to the segments. The results from this study were encouraging, which strengthened the belief that applying data mining techniques could indeed support CRM activities at Ethiopian Airlines. In the future, more segmentation studies using demographic information and employing other clustering algorithms could yield better results.