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Authors: Erinç Albey 1 and Zehra Can 2

Affiliations: 1 Özyeğin University, Turkey ; 2 Özyeğin University, Business Intelligence Team and Turkcell Technology Research and Development Inc., Turkey

ISBN: 978-989-758-255-4

Keyword(s): RFM, Prepaid Subscriber, Telecommunication, Pareto/NBD, Logistic Regression, Mobile.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Predictive Modeling ; Sensor Networks ; Signal Processing ; Soft Computing ; Statistics Exploratory Data Analysis ; Symbolic Systems

Abstract: In telecommunication, mobile operators prefer to acquire postpaid subscribers and increase their incoming revenue based on the usage of postpaid lines. However, subscribers tend to buy and use prepaid mobile lines because of the simplicity of the usage, and due to higher control over the cost of the line compared to postpaid lines. Moreover the prepaid lines have less paper work between the operator and subscriber. The mobile subscriber can end their contract, whenever they want, without making any contact with the operator. After reaching the end of the defined period, the subscriber will disappear, which is defined as “involuntary churn”. In this work, prepaid subscribers’ behavior are defined with their RFM data and some additional features, such as usage, call center and refill transactions. We model the churn behavior using Pareto/NBD model and with two benchmark models: a logistic regression model based on RFM data, and a logistic regression model based on the additional feature s. Pareto/NBD model is a crucial step in calculating customer lifetime value (CLV) and aliveness of the customers. If Pareto/NBD model proves to be a valid approach, then a mobile operator can define valuable prepaid subscribers using this and decide on the actions for these customers, such as suggesting customized offers. (More)

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Paper citation in several formats:
Can, Z. and Albey, E. (2017). Churn Prediction for Mobile Prepaid Subscribers.In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 67-74. DOI: 10.5220/0006425300670074

@conference{data17,
author={Zehra Can. and Erin\c{C} Albey.},
title={Churn Prediction for Mobile Prepaid Subscribers},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={67-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006425300670074},
isbn={978-989-758-255-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Churn Prediction for Mobile Prepaid Subscribers
SN - 978-989-758-255-4
AU - Can, Z.
AU - Albey, E.
PY - 2017
SP - 67
EP - 74
DO - 10.5220/0006425300670074

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