Customer churn prediction in telecommunication industry through machine learning based Fine-tuned XGBoost algorithm

2021 ◽  
Author(s):  
Swetha P a ◽  
Dayananda R B
Keyword(s):  
Machine Learning ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

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.

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.  

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