A Case Study for the Churn Prediction in Turksat Internet Service Subscription

被引:3
|
作者
Gok, Mehmet [1 ]
Ozyer, Tansel [2 ]
Jida, Jamal [3 ]
机构
[1] TOBB Univ, Turksat Corp, Ankara, Turkey
[2] TOBB Univ, Ankara, Turkey
[3] Lebanese Univ, Beirut, Lebanon
关键词
Customer relationship management; churn prediction; data mining; time series clustering; k-means clustering; hierarchical clustering; classification; support vector machines; recursive partitioning;
D O I
10.1145/2808797.2808821
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1220 / 1224
页数:5
相关论文
共 50 条
  • [1] Enterprise Subscription Churn Prediction
    Vadakattu, Ramakrishna
    Panda, Bibek
    Narayan, Swarnim
    Godhia, Harshal
    [J]. PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, 2015, : 1317 - 1321
  • [2] Customer Churn Prediction in an Internet Service Provider
    Duyen Do
    Phuc Huynh
    Phuong Vo
    Tu Vu
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2017, : 3928 - 3933
  • [3] Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques
    Goy, Gokhan
    Kolukisa, Burak
    Bahcevan, Cenk
    Gungor, Vehbi Cagri
    [J]. 2020 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), 2020, : 248 - 253
  • [4] Determinants of subscription time for portable Internet service
    Yoo, SH
    [J]. APPLIED ECONOMICS LETTERS, 2004, 11 (15) : 931 - 934
  • [5] Predicting Customer Churn on OTT Platforms: Customers with Subscription of Multiple Service Providers
    Mohan, Manish
    Jadhav, Anil
    [J]. JOURNAL OF INFORMATION AND ORGANIZATIONAL SCIENCES, 2022, 46 (02) : 433 - 451
  • [6] Customer Churn Prediction for Broadband Internet Services
    Huang, B. Q.
    Kechadi, M-T.
    Buckley, B.
    [J]. DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS, 2009, 5691 : 229 - +
  • [7] Bag of Activities for Customer Churn Prediction in e-Book Subscription Domain
    Drozda, Pawel
    Ropiak, Krzysztof
    Mozalewski, Lukasz
    Malaczynski, Mikolaj
    Frukacz, Mateusz
    [J]. RECENT CHALLENGES IN INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II, ACIIDS 2024, 2024, 2145 : 159 - 170
  • [8] Customer Churn Prediction for a Software-as-a-Service Inventory Management Software Company : A Case Study in Thailand
    Amornvetchayakul, Phongsatorn
    Phumchusri, Naragain
    [J]. 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND APPLICATIONS (ICIEA 2020), 2020, : 514 - 518
  • [9] Customer survival time in subscription-based businesses (case of Internet service providers)
    Mohammed, Z.
    Maritz, J. S.
    Kotze, D.
    [J]. DATA MINING VIII: DATA, TEXT AND WEB MINING AND THEIR BUSINESS APPLICATIONS, 2007, 38 : 303 - +
  • [10] Boosting Internet Card Cellular Business via User Portraits: A Case of Churn Prediction
    Wu, Fan
    Ren, Ju
    Lyu, Feng
    Yang, Peng
    Zhang, Yongmin
    Zhang, Deyu
    Zhang, Yaoxue
    [J]. IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2022), 2022, : 640 - 649