Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2030
Title: A Case Study for the Churn Prediction in Turksat Internet Service Subscription
Authors: Gök, Mehmet
Özyer, Tansel
Jida, Jamal
143116
Keywords: Customer relationship management
churn prediction
data mining
time series clustering
k-means clustering
hierarchical clustering
classification
support vector machines
recursive partitioning
Issue Date: 2015
Publisher: ASSOC Computing Machinery
Source: Gök, M., Özyer, T., & Jida, J. (2015, August). A case study for the churn prediction in Turksat internet service subscription. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1220-1224). IEEE.
Abstract: Churn prediction is a customer relationship process that predicts for customers who are at the brink of transferring all the business to competitor. It is predicted by modeling customer behaviors in order to extract patterns. An acquaintance of a customer is more costly than retainment of an existing customer. Churn predictions shed light on members about to leave the service and support promotion activities. These attempts are utilized to avoid subscription cancellation of existing customers. Nowadays, telecommunication companies take churn prediction very serious. They strive for monitoring customers in the business by using various applications in systematic approach. Our study is based on leading internet service providing company, Turksat Satellite Communications and Cable TV Operations Company's customer behavior analysis. It is the leading internet service provider of Turkey operating in telecommunications sector. We have created a two-phase solution utilizing data mining techniques. These are time series clustering and classification techniques.
Description: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2015 : Paris; France)
URI: https://dl.acm.org/citation.cfm?doid=2808797.2808821
https://hdl.handle.net/20.500.11851/2030
ISBN: 978-1-4503-3854-7
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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