Feature intersection for agent-based customer churn prediction

N. Sandhya · Philip Samuel · Mariamma Chacko

7 citations0/0 files storedData Technologies and Applications

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Customer churn and segmentation Customer Service Quality and Loyalty AI and HR Technologies

Abstract

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.

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References

2002Agent-based modeling: Methods and techniques for simulating human systems 2008Handling class imbalance in customer churn prediction 2005Applying data mining to telecom churn management 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2003A novel evolutionary data mining algorithm with applications to churn prediction 2005Computer assisted customer churn management: State-of-the-art and future trends 2008Social ties and their relevance to churn in mobile telecom networks

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