Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example

2014 ◽  
pp. 206-218 ◽  
Author(s):  
Adnan Amin ◽  
Changez Khan ◽  
Imtiaz Ali ◽  
Sajid Anwar
Keyword(s):  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Counter Example ◽  
Customer Churn Prediction

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.

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 Customer Churn Prediction in Telecommunication Industry Using Data Certainty

2020 ◽  
pp. 252-258
Author(s):  
V R Reji Raj ◽  
Rasheed Ahammed Azad .V
Keyword(s):  
Machine Learning ◽  
Market Value ◽  
Machine Learning Algorithms ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
The Past ◽  
Customer Churn Prediction ◽  
Using Data ◽  
Data Certainty

Customer churn is a major problem affecting large companies, especially in telecommunication field. So the telecom industries have to take the necessary steps to retain their customers, to maintain their market value. So companies are seeking to develop methods that predict potential churned customers. We have to find out the factors that increase customer churn for making necessary actions to reduce churn. In the past, different data mining techniques have been used for predicting the churners. Here the most popular machine learning algorithms used for churn predicting are analysed. The conclusions are stated with the help of suitable tables.

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