Browsing by Author "Güllüoğlu, Batuhan"

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    Customer segmentation for churn management by using ant colony
    (Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, 2011-09) Güllüoğlu, Batuhan; Karahoca, Adem
    Data mining is interested in clustering, by similarities of data. Some of clustering techniques are evolutionary and optimization techniques. Characteristic selection is used for novel hybrid modeling. Customer priorities are very important for companies. Moreover, customer priorities must be determined, and campaigns must be ordered according to these priorities. Customer segmentation was done with Ant Colony algorithm. Shortest path approach is used in Ant Colony algorithm. Moreover, clustering is done by the euclidean distance formula in Ant Colony algorithm. Customer segmentation attributes are mostly related with the satisfaction factors, but some of them were eliminated by using ranker. These results are mostly related with the customer's income, tenure, equip, callcard and reside. These attributes are the most important satisfaction factors not to lose customers as expected. There are many reasons in changing GSM operator for subscribers and it is very important for companies to predict if subscriber will change GSM operator or not. For this reason companies that gives GSM services have to monitor subscribers behavior and predict one step forward. In this study changing subscribers’ GSM operator will be predicted by using data mining techniques.