Customer Churn Prediction in Mobile Operator Using Combined Model

Jelena Mamčenko; Jamil Gasimov

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Customer Churn Prediction in Mobile Operator Using Combined Model

Topics: Data Mining

In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3: ICEIS, 233-240, 2014 , Lisbon, Portugal

Authors: Jelena Mamčenko and Jamil Gasimov

Affiliation: Vilnius Gediminas Technical University, Lithuania

Keyword(s): Data Mining, Churn Detection, Neural Network, Cox Regression, Decision Tree, Combined Model.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Data Mining technologies are developing very rapidly nowadays. One of the biggest fields of application of data mining is prediction of churn in service provider companies. Customers who switch to another service provider are called churned customers. In this study are described main techniques and processes of Data Mining. Customer churn is defined, different types and causes of churn are discussed. Social aspects of churn are brought to attention and specifically related to realities of Azerbaijan.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.137.162.21

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Mamčenko, J. and Gasimov, J. (2014). Customer Churn Prediction in Mobile Operator Using Combined Model. In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3: ICEIS; ISBN 978-989-758-027-7; ISSN 2184-4992, SciTePress, pages 233-240. DOI: 10.5220/0004896002330240

@conference{iceis14,
author={Jelena Mamčenko. and Jamil Gasimov.},
title={Customer Churn Prediction in Mobile Operator Using Combined Model},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3: ICEIS},
year={2014},
pages={233-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004896002330240},
isbn={978-989-758-027-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3: ICEIS
TI - Customer Churn Prediction in Mobile Operator Using Combined Model
SN - 978-989-758-027-7
IS - 2184-4992
AU - Mamčenko, J.
AU - Gasimov, J.
PY - 2014
SP - 233
EP - 240
DO - 10.5220/0004896002330240
PB - SciTePress