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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)
Mariamma Chacko (Department of Ship Technology, Cochin University of Science and Technology, Kochi, India)

Data Technologies and Applications

ISSN: 2514-9288

Publication date: 1 July 2019



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.


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.


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.


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.



N., S., Samuel, P. and Chacko, M. (2019), "Feature intersection for agent-based customer churn prediction", Data Technologies and Applications, Vol. 53 No. 3, pp. 318-332.



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