CHURN PREDICTION IN TELECOM SECTOR USING MACHINE LEARNING (original) (raw)
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Churn Prediction of Customer in Telecom Industry using Machine Learning Algorithms
International Journal of Engineering Research & Technology (IJERT), 2020
https://www.ijert.org/churn-prediction-of-customer-in-telecom-industry-using-machine-learning-algorithms https://www.ijert.org/research/churn-prediction-of-customer-in-telecom-industry-using-machine-learning-algorithms-IJERTV9IS050022.pdf In the Telecommunication Industry, customer churn detection is one of the most important research topics that the company has to deal with retaining on-hand customers. Churn means the loss of customers due to exiting offers of the competitors or maybe due to network issues. In these types of situations, the customer may tend to cancel the subscription to a service. Churn rate has a substantial impact on the lifetime value of the customer because it affects the future revenue of the company and also the length of service. Due to a direct effect on the income of the industry, the companies are looking for a model that can predict customer churn. The model developed in this work uses machine learning techniques. By using machine learning algorithms, we can predict the customers who are likely to cancel the subscription.Using this, we can offer them better services and reduce the churn rate. These models help telecom services to make them profitable. In this model, we used a Decision Tree, Random Forest, and XGBoost.
2020
Customer acquisition and retention is a key concern for several industries and is particularly acute in fiercely competitive and fast growth businesses. Retaining a loyal customer is far more important than acquiring a new one, thus making customer churn one of the critical concerns for big corporations. Finding factors triggering customer churn is vital to implement necessary remediation to pre-empt and cut back this churn. This research focuses on implementing machine learning (ML) algorithms to identify potential churn customers, categorise them based upon usage patterns, and visualize the analysis results. Results show that Extra Trees Classifier, XGBoosting Algorithm and Support Vector Machine have the best churn modelling performance, particularly for 80:20 dataset distribution with average AUC scores of 0.843, 0.787 and 0.735 respectively and low false negatives. The research demonstrates that ML algorithms can successfully predict potential customer churn and help in devisin...
An Overview of Customer Churn Prediction in Telecom Industry
Journal of Emerging Technologies and Innovative Research (JETIR), 2021
In Telecom Industry customer churn is a big issue and one that impacts their revenue. When customers start to leave a service or subscription, it increases the expenditure for these companies. Businesses have found that acquiring new customers costs them nearly six times more money than retaining existing ones. Therefore, preventing customer churn becomes important when companies are trying to grow their business. The analysis of Customer Behaviour using Machine Learning techniques does provide an effective solution to the problem by predicting which customers are more likely to leave the service or subscription. Predictive analysis of customer behaviour not only helps companies fix issues with their service but also helps them add new features and products so as to keep the customer engaged. The present work provides an overview of the latest works in the field of Customer Churn prediction. Our aim is to provide a simple path to make the future development of novel Churn prediction approaches easier.
Telecommunication subscribers' churn prediction model using machine learning
Eighth International Conference on Digital Information Management (ICDIM 2013), 2013
During the last two decades, we have seen mobile communication becoming the dominant medium of communication. In numerous countries, especially the developed ones, the market is saturated to the extent that each new customer must be won over from the competitors. At the same time, public policies and standardization of mobile communication now allow customers to easily switch over from one carrier to another, resulting in a fluid market. Since the cost of winning a new customer is far greater than the cost of retaining an existing one, mobile carriers have now shifted their focus from customer acquisition to customer retention. As a result, churn prediction has emerged as the most crucial Business Intelligence (BI) application that aims at identifying customers who are about to transfer their business to a competitor i.e. to churn. This paper aims to present commonly used data mining techniques for the identification of customers who are about to churn. Based on historical data, these methods try to find patterns which can identify possible churners. Some of the well-known algorithms used during this research are Regression analysis, Decision Trees and Artificial Neural Networks (ANNs). The data set used in this study was obtained from Customer DNA website. It contains traffic data of 106,000 customers and their usage behavior for 3 months. We also discuss the use of re-sampling method in order to solve the problem of class imbalance. Our results show that in case of the data set used, decision trees is the most accurate classifier algorithm while identifying potential churners.
A Review of Churn Predictive in Telecommunication
Telecommunication field is a competitive industry which provides customer with broaden choice of services. The evolution of this field are likely focusing more on customer based strategy to avoid the risk of customer churn. This is because organizations realize about the fact that customers have the freedom to churn services to their competitors anytime with very minimal cost. Considering this situation, data analytic for churn predictive in telecommunication industry is a necessary to keep up with the competition. However, as the industry growing bigger service provider facing with a lot of challenges due to high dimensional and fraction customer data especially for churn analysis. One of the alternatives to develop an effective prediction model is using machine learning methods. Machine learning has wide application in development of prediction model in many fields not limited to only telecommunication as it has the ability to tackle data issues such as dimensionality and irrelevant dataset. Meanwhile, cloud computing can be a paradigm that apparently give many advantages in issues that related to the churn predictive. Thus, this study will review about the churn prediction methodologies which focusing on the
Customer Churn Prediction in Telecom Sector: A Survey and way a head
2021
The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are explored.
Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms
Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Naïve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models' performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.
Prediction of Customer Churn in Telecom Industry: A Machine Learning Perspective
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The business world is becoming increasingly saturated in today's competitive environment. There is a great deal of competition in the telecommunications industry, especially due to various vibrant service providers. As a result, they have had difficulty retaining their existing customers. As attracting new customers is much more costly than retaining current ones, now is the time to ensure the telecom industry maintains value by retaining customers over acquiring new ones. Numerous machine learning and data mining methods have been proposed in the literature to predict customer churners using heterogeneous customer records over the past decade. This research gives a brief idea on the Customer Churn problem, and explores how various machine learning techniques can be used to predict customer churn via models such as XGBoost, GradientBoost, AdaBoost, ANN, Logistic Regression and Random Forest, and also compare the effectiveness of the models in term of accuracy. Keywords— Machine ...
Machine Learning Approaches to Predict Customer Churn in Telecommunications Industry
IRJET, 2023
This project aims to develop machine learning models for predicting customer churn in the telecommunications industry. The project will analyze various customer behavior and demographic data, such as tenure, payment method,monthly charges, total charges, etc to identify patterns and build predictive models. The project will use advanced techniques, such as logistic regression, decision trees, support vector machine and random forests, to predict customer churn accurately. The study will help the telecommunications industry to understand the reasons behind customer churn and implement effective strategies to reduce customer churn rates. The results of this project can be useful for improving customer retention and enhancing the overall customer experience in the industry.
Dynamic Churn Prediction Using Machine Learning Algorithms on Telecommunication
IJETMS, 2023
Any organization's ability to increase revenue and profit depends heavily on its customers. So, it is crucial for organizational managers to keep a single effective customer relationship management system by choosing the target consumers and maintaining effective relationships with them. This would help them to increase customer satisfaction. Customers are becoming more drawn to the quality of service (QoS) offered by businesses in the present. Yet, the present day shows greater rivalry in offering clients technologically cutting-edge QoS. Yet, effective customer relationship management systems can help the organization attract new clients, preserve client connections, and enhance client retention by generating more revenue for the company's operations. Also, the client retention methods can benefit greatly from the use of machine learning models like support vector machines and Random Forest algorithms. One essential machine learning approach that effectively analyses the data for churn prediction is support vector machine learning. The telecom industries have extensively embraced machine learning as an efficient application of artificial intelligence in assessing and mitigating customer turnover.