Feature intersection for agent-based customer churn prediction

Publication Date01 July 2019
Date01 July 2019
AuthorSandhya N.,Philip Samuel,Mariamma Chacko
SubjectLibrary & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information & knowledge management,Information & communications technology,Internet
Feature intersection for
agent-based customer
churn prediction
Sandhya N.
Department of Information Technology,
Cochin University of Science and Technology, Kochi, India
Philip Samuel
Department of Computer Science,
Cochin University of Science and Technology, Kochi, India, and
Mariamma Chacko
Department of Ship Technology,
Cochin University of Science and Technology, Kochi, India
Purpose Telecommunication has a decisive role in the development of technology in the current era. The
number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a
significant area in which big data technologies are needed. Competition among the telecommunication
companies is high due to customer churn. Customer retention in telecom companies is one of the major
problems. The paper aims to discuss this issue.
Design/methodology/approach The authors recommend an Intersection-Randomized Algorithm (IRA)
using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication
service providers. The authors use the agent-based model (ABM) to predict the complex mobile user
behaviour to prevent customer churn with a particular telecommunication service provider.
Findings The agent-based model increases the prediction accuracy due to the dynamic nature of agents.
ABM suggests rules based on mobile user variable features using multiple agents.
Research limitations/implications The authors have not considered the microscopic behaviour of the
customer churn based on complex user behaviour.
Practical implications This paper shows the effectiveness of the IRA along with the agent-based model
to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user
churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit.
Originality/value This paper shows the customer churn prediction of complex human behaviour in an
effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce
Algorithm using agent-based model.
Keywords Redundancy, Hadoop, Distributed, MapReduce, Agent-based, Intersection-randomized
Paper type Research paper
1. Introduction
In the modern era, mobile communication has become an integral part of every human being
(Almana et al., 2014). In the telecommunication industry, different customer service providers
attempt to increase the market share for a profitable business. Mobile customer retention is
considered to be a significant factor for investigation, as it is the centre of interest for
developing a profitable relationship with customers. In this telecommunication business, the
telecom service providers are competing for customer satisfaction by offering different
customer services. Mobile customer retention is directly proportional to mobile user satisfaction
(Qureshi et al., 2013). It is costly to entice new mobile users into their service providers than to
maintain existing mobile users (Burez and Van den Poel, 2009). To sustain in the telecom
industry, mobile service providers introduce different service features. By improving the
quality of mobile communication service features, we can enhance customer retention.
Data Technologies and
Vol. 53 No. 3, 2019
pp. 318-332
© Emerald PublishingLimited
DOI 10.1108/DTA-03-2019-0043
Received 21 March 2019
Revised 31 May 2019
Accepted 19 June 2019
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