A Genetic Programming Based Framework for Churn Prediction in Telecommunication Industry

2014 ◽  
pp. 353-362 ◽  
Author(s):  
Hossam Faris ◽  
Bashar Al-Shboul ◽  
Nazeeh Ghatasheh
Keyword(s):  
Genetic Programming ◽  
Telecommunication Industry ◽  
Churn Prediction

scholarly journals Predicting customer churn using targeted proactive retention

2018 ◽  
Vol 7 (2.27) ◽  
pp. 69 ◽  
Author(s):  
B Mishachandar ◽  
Kakelli Anil Kumar
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Business Management ◽  
Big Data Analytics ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Novel Approach ◽  
Fierce Competition ◽  
Identification Strategy

With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this paper provides a comprehensible understanding of the so far employed techniques in predicting customer churn. From that, it is quite evident that less attention has been given to the accuracy and the intuitiveness of churn models developed. Therefore, a novel approach of combining the models of Machine Learning and Big Data Analytics tools was proposed to deal churn prediction effectively. The purpose of this proposed work is to apply a novel retention technique called the targeted proactive retention to predict customer churning behavior in advance and help in their retention. This proposed work will help telecom companies to comprehend the risk associated with customer churn by predicting the possibility and the time of occurrence.  

scholarly journals Optimal customer relationship management in telecalling industry by using data mining and business intelligence

2017 ◽  
Vol 7 (1.1) ◽  
pp. 12
Author(s):  
T. Kamalakannan ◽  
P. Mayilvaghnan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Business Intelligence ◽  
Research Method ◽  
Support Vector ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Positive Rate ◽  
Customer Churn Prediction

Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. It can be handled effectively by introducing the data mining techniques which select the most useful information from the gathered data set from which decision making can be done efficiently. In this research method, telecommunication industry is considered in which customer churn prediction application is focused. The main goal of this research method is to introduce the data mining technique which can select the most useful information from the telecommunication industry dataset. This is done by introducing the Hybrid Genetic Algorithm with Particle Swarm Optimization (HGAPSO) method which can select the most useful information. In this research, the hybrid HGAPSO combines the advantages of PSO and GA optimally. From the selected information, decision making about the customer churn prediction can be done accurately. Finally decision making is done by predicting the customer behaviour using Support Vector Machine classification approach. The performance metrics are considered such as precision, recall, f-measure, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), time complexity and ROC. Experimental results demonstrated that HGAPSO provides highly scalable which is used for prediction examination in the business intelligence.

Improved churn prediction in telecommunication industry by analyzing a large network

2014 ◽  
Vol 41 (15) ◽  
pp. 6575-6584 ◽  
Author(s):  
Kyoungok Kim ◽  
Chi-Hyuk Jun ◽  
Jaewook Lee
Keyword(s):  
Telecommunication Industry ◽  
Large Network ◽  
Churn Prediction

scholarly journals Customer churn prediction in telecommunication industry using data certainty

2019 ◽  
Vol 94 ◽  
pp. 290-301 ◽  
Author(s):  
Adnan Amin ◽  
Feras Al-Obeidat ◽  
Babar Shah ◽  
Awais Adnan ◽  
Jonathan Loo ◽  
...  
Keyword(s):  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction ◽  
Using Data ◽  
Data Certainty

scholarly journals Churn Prediction in Telecommunication Industry using Decision Tree

2017 ◽  
Vol V6 (04) ◽  
Author(s):  
Nisha Saini ◽  
Monika ◽  
Dr. Kanwal Garg ◽  
Keyword(s):  
Decision Tree ◽  
Telecommunication Industry ◽  
Churn Prediction
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