Performance Analysis of Machine Learning Algorithms in Customer Churn Prediction (original) (raw)
Related papers
Customer Churn Prediction Using Machine Learning Algorithms
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Customer churn is a serious problem in the telecommunications industry and occurs more often. Customer churn is the percentage of customers that stopped using your company's product or service during a certain time frame. One of the most important problems in predicting customer churn is the imbalanced data, which has been tried to be solved and compared with different methods. The machine learning algorithms will be use in this paper are Decision Tree, Support Vector Machine, Random Forest. Also, the performance of support vector were better than other algorithms.
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.
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.
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...
Predictive Framework for Advanced Customer Churn Prediction using Machine Learning
International Journal of Computer Applications
In recent years, the telecom sector has been burgeoning to satisfy the demand of mobile subscribers and telecom service providers. The increase in number of mobile subscribers and competition among providers, results in the creation of "churners". These are the subscribers who tend to switch from the current telecom service to another. The detection of these churners is called "churn prediction". This prediction has become a major challenge for telecom companies. The main purpose of customer churn prediction is to estimate the number of subscribers those who want to quit the current service provider by providing specific solutions to retain them. This paper proposes methods for the estimation of churners by applying different classification techniques and estimates the differences between them. The performance is measured by taking different parameters like accuracy, precision, recall, etc. In this paper, the various performance measurement and comparison are done by using the dataset collected from American Telecommunication Company. All the proposed work is based on Machine Learning, inculcating the supervised learning. In addition to all, a single test-bed is designed as a user interface to predict the individual customer according to different attributes.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
In current days, the customers are getting more attracted towards the quality of service (QoS) provided by the organizations. However, the current era is evidencing higher competition in providing technologically advanced QoS to the customers. Nevertheless, efficient customer relationship management systems can be advantageous for the organization for gaining more customers, maintaining customer relationships and improve customer retention by adding more profit to the organizational business. Furthermore, the machine learning models such as support vector machine Random Forest algorithms can add more value to the customer retention strategies.
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.
A Comparison of Machine Learning Techniques for Customer Churn Prediction
We present a comparative study on the most popular machine learning methods applied to the challenging problem of customer churning prediction in the telecommunications industry. In the first phase of our experiments, all models were applied and evaluated using cross-validation on a popular, public domain dataset. In the second phase, the performance improvement offered by boosting was studied. In order to determine the most efficient parameter combinations we performed a series of Monte Carlo simulations for each method and for a wide range of parameters. Our results demonstrate clear superiority of the boosted versions of the models against the plain (non-boosted) versions. The best overall classifier was the SVM-POLY using AdaBoost with accuracy of almost 97% and F-measure over 84%.
Customer Churn Prediction Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Rapid technology growth has affected corporate practices. With more items and services to select from, client churning has become a big challenge and threat to all firms. We offer a machine learning-based churn prediction model for a B2B subscription-based service provider. Our research aims to improve churn prediction. We employed machine learning to iteratively create and evaluate the resulting model using accuracy, precision, recall, and F1-score. The data comes from a financial administration subscription service. Since the given dataset is mostly non-churners, we analyzed SMOTE, SMOTEENN, and Random under Sampler to balance it. Our study shows that machine learning can anticipate client attrition. Ensemble learners perform better than single base learners, and a balanced training dataset should increase classifier performance. I.
Customer Churn Prediction on E-Commerce Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
For E-commerce businesses to produce successful marketing plans and customer retention tactics, client churn vaticination is pivotal. In order to handle the longitudinal timeframes and multiple data variables of B2Ce-commerce consumers' buying habits, the authors of this study present a loss vaticination model that integrates k-means client segmentation with support vector machine (SVM) vaticination. guests are divided into three groups according to the approach, which also defines the main customer groupings. In order to anticipate client development, the study analyses the efficacity of logistic retrogression and SVM vaticination. The findings show that client segmentation greatly increases each indicator's capability to read values, emphasizing the significance of k-means clustering segmentation. also, it's demonstrated that SVM vaticination is more accurate than logistic retrogression vaticination. The conclusions of this study have important ramifications for client relationship operation.