User behaviour analysis and churn prediction in ISP (original) (raw)
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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.
Applying Data Mining to Customer Churn Prediction in an Internet Service Provider
International Journal of Computer Applications, 2010
A business incurs much higher charges when attempting to win new customers than to retain existing ones. As a result, much research has been invested into new ways of identifying those customers who have a high risk of churning. However, customer retention efforts have also been costing organizations large amounts of resources. Same is the situation in ISP industry in I.R.Iran. Exploiting the use of demographic, billing and usage data, this study tends to identify the best churn predictors on the one hand and evaluates the accuracy of different data mining techniques on the other. Clustering users as per their usage features and incorporating that cluster membership information in classification models is another aspect which has been addressed in this study.
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...
Customer Churn Prediction in Telecommunication A Decade Review and Classification
Acquisition and the retention of customers are the top most concerns in today's business world. The rapid increase of market in every business is leading to higher subscriber base. Consequently, companies have realized the importance of retaining the on hand customers. It has become mandatory for the service providers to reduce churn rate because the negligence could be resulted as profitability reduction in major perspective. Churn prediction helps in identifying those customers who are likely to leave a company. Telecommunication is coping with the issue of ever increasing churn rate. Data mining techniques enable these telecommunication companies to be equipped with effective methods for reducing churn rate. The paper reviews 61 journal articles to survey the pros and cons of renowned data mining techniques used to build predictive customer churn models in the field of telecommunication and thus providing a roadmap to researchers for knowledge accumulation about data mining techniques in telecom.
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.
Customer churn prediction in telecommunications
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Acquisition and the retention of customers are the top most concerns in today's business world. The rapid increase of market in every business is leading to higher subscriber base. Consequently, companies have realized the importance of retaining the on hand customers. It has become mandatory for the service providers to reduce churn rate because the negligence could be resulted as profitability reduction in major perspective. Churn prediction helps in identifying those customers who are likely to leave a company. Telecommunication is coping with the issue of ever increasing churn rate. Data mining techniques enable these telecommunication companies to be equipped with effective methods for reducing churn rate. The paper reviews 61 journal articles to survey the pros and cons of renowned data mining techniques used to build predictive customer churn models in the field of telecommunication and thus providing a roadmap to researchers for knowledge accumulation about data mining techniques in telecom.
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.
Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate
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Customer churn frequently occurs in the telecommunications industry, which provides services and can be detrimental to companies. A predictive model can be useful in determining and analyzing the causes of churn actions taken by customers. This paper aims to analyze and implement machine learning models to predict churn actions using Kaggle data on customer churn. The models considered for this research include the XG Boost Classifier algorithm, Bernoulli Naïve Bayes, and Decision Tree algorithms. The research covers the steps of data preparation, cleaning, and transformation, exploratory data analysis (EDA), prediction model design, and analysis of accuracy, F1 Score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) score. The EDA results indicate that the contract type, length of tenure, monthly invoice, and total bill are the most influential features affecting churn actions. Among the models considered, the XG Boost Classifier algorithm achieved ...
A Survey on Customer Churn Prediction in Telecom Industry: Datasets, Methods and Metrics
In this competitive world, business is becoming highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore, it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. In the past decade, several data mining techniques have been proposed in the literature for predicting the churners using heterogeneous customer records. This paper reviews the different categories of customer data available in open datasets, predictive models and performance metrics used in the literature for churn prediction in telecom industry.
Customer Churn Prediction in Telecommunication Industry. A Data Analysis Techniques Approach
Postmodern Openings, 2022
Telecommunications is one of the most dynamic sectors in the market, where the customer base is an important pawn in receive safe revenues, so is important to focus attention is paid to maintaining them with an active status. Migrating customers from one network to another varies among telecommunication companies depending on different factors such as call quality, pricing plan, minute consumption, data, sms facilities, customer billing issues, etc. Determining an effective predictive model helps detect early warning signals when churn occurs and assigns to each customer a score called "churn score" that indicates the likelihood that the individual might migrate to another network over a predefined time period. To this extent, the present paper uses more than 10k customers sample of a telecommunication company and tries to analyse the churn behavior. The aim of the paper is both to test the efficiency and performance of the most commonly used data mining techniques to predict the churn behavior and to underline the main indicators that can be used when conducting such analyses. Knowing the magnitude of the churn phenomenon, the company can prevent the instability that is going to occur by applying a series of measure in order to increase the retention of the current customers.