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 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 A Critical Examination of Different Models for Customer Churn Prediction using Data Mining

2019 ◽  
Vol 8 (6S3) ◽  
pp. 850-854
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Critical Examination ◽  
Churn Prediction ◽  
Customer Churn ◽  
Learning Techniques ◽  
Customer Churn Prediction ◽  
Using Data

Due to competition between online retailers, the need for providing improved customer service has grown rapidly. In addition to reduction in sales due to loss of customers, more investments are needed to be done to attract new customers. Companies now are working continuously to improve their perceived quality by way of giving timely and quality service to their customers. Customer churn has become one of the primary challenges that many firms are facing nowadays. Several churn prediction models and techniques are proposed previously in literature to predict customer churn in areas such as finance, telecom, banking etc. Researchers are also working on customer churn prediction in e-commerce using data mining and machine learning techniques. In this paper, a comprehensive review of various models to predict customer churn in e-commerce data mining and machine learning techniques has been presented. A critical review of recent research papers in the field of customer churn prediction in e-commerce using data mining has been done. Thereafter, important inferences and research gaps after studying the literature are presented. Finally, the research significance and concluding remarks are described in the end.

scholarly journals Customer Churn Prediction for Imbalanced Class Distribution of Data in Business Sector

2019 ◽  
Vol 5 ◽  
pp. 101-110
Author(s):  
Aayush Bhattarai ◽  
Elisha Shrestha ◽  
Ram Prasad Sapkota
Keyword(s):  
Machine Learning ◽  
Imbalanced Data ◽  
Telecommunications Industry ◽  
Business Case ◽  
Machine Learning Algorithms ◽  
Business Sector ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Customer Churn ◽  
Customer Churn Prediction

Churners are those people who are about to transfer their business to a competitor or simply who cancel a subscription to a service. This paper is based on a specific business sector, which is telecommunication sector. With a churn rate of 30%, the telecommunication sector takes the first place on the list. In this paper, we present some advanced data mining methodologies which predicts customer churn in the pre-paid mobile telecommunications industry using a call detail records dataset. To implement the predictive models, we initially propose and then apply four machine learning algorithms: Random Forest, Naïve Bayes, Logistic Regression, and XG Boost. To evaluate the models, we use various evaluation metrics and find the best model which will be suitable for any class imbalanced data and also our business case. This paper can also be viewed as a comparative study on the most popular machine learning methods applied to the challenging problem of customer churn prediction.

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