Customer Churn Prediction in Telecommunication A Decade Review and Classification (original) (raw)

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.

A Survey On Data Mining Techniques In Customer Churn Analysis For Telecom Industry

Customer churn prediction in Telecom Industry is a core research topic in recent years. A huge amount of data is generated in Telecom Industry every minute. On the other hand, there is lots of development in data mining techniques. Customer churn has emerged as one of the major issues in Telecom Industry. Telecom research indicates that it is more expensive to gain a new customer than to retain an existing one. In order to retain existing customers, Telecom providers need to know the reasons of churn, which can be realized through the knowledge extracted from Telecom data. This paper surveys the commonly used data mining techniques to identify customer churn patterns. The recent literature in the area of predictive data mining techniques in customer churn behavior is reviewed and a discussion on the future research directions is offered.

Review of Data Mining Techniques for Churn Prediction in Telecom

Journal of information and organizational sciences, 2015

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models.

A Review on: Data Mining for Telecom Customer Churn Management

Customer acquisition and retaining those customers is a matter of concern for all companies and the same applies for telecom sector too. So customer churn is an important area of concern. This research work aims at carryinging out a literature review for the past decade reviewing around 50 research papers in the area of telecom churn with two perspectives: mining technique applied and publication year. Such a survey will be a help to the telecom service providers in determining the appropriate model to identify the prospective churners and accordingly framing the marketing strategies for different customer groups, to reduce the rate of customer turnover.

A Review And Analysis Of Churn Prediction Methods For Customer Retention In Telecom Industries

Customer churn prediction has gathered greater interest in business especially in telecommunications industries. Many authors have presented different versions of the churn prediction models greatly based on the data mining concepts employing the machine learning and meta-heuristic algorithms. This aim of this paper is to study some of the most important churn prediction techniques developed over the recent years. The primary objective is on the churn in telecom industries to accurately estimate the customer survival and customer hazard functions to gain the complete knowledge of churn over the customer tenure. Another objective is the identification of the customers who are at the blink of churn and approximating the time they will churn. This paper focuses on analyzing the churn prediction techniques to identify the churn behavior and validate the reasons for customer churn. This paper summarizes the churn prediction techniques in order to have a deeper understanding of the customer churn and it shows that most accurate churn prediction is given by the hybrid models rather than single algorithms so that telecom industries become aware of the needs of high risk customers and enhance their services to overturn the churn decision.

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 Analysis and Prediction in Telecommunication for Decision Making

2018 International Conference On Business Innovation (ICOBI), 2018

With the rapid development of communication technology, the field of telecommunication faces complex challenges due to the number of vibrant competitive service providers. Customer Churn is the major issue that faces by the Telecommunication industries in the world. Churn is the activity of customers leaving the company and discarding the services offered by it, due to the dissatisfaction with the services. The main areas of this research contend with the ability to identify potential churn customers, cluster customers with similar consumption behavior and mine the relevant patterns embedded in the collected data. The primary data collected from customers were used to create a predictive churn model that obtain customer churn rate of five telecommunication companies. For model building, classified the relevant variables with the use of the Pearson chi-square test, cluster analysis, and association rule mining. Using the Weka, the cluster results produced the involvement of customers, interest areas and reasons for the churn decision to enhance marketing and promotional activities. Using the Rapid miner, the association rule mining with the FP-Growth component was expressed rules to identify interestingness patterns and trends in the collected data have a huge influence on the revenues and growth of the telecommunication companies. Then, the C5.0 Decision tree algorithm tree, the Bayesian Network algorithm, the Logistic Regression algorithm, and the Neural Network algorithms were developed using the IBM SPSS Modeler 18. Finally, comparative evaluation is performed to discover the optimal model and test the model with accurate, consistent and reliable results.

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 model: a case of the telecommunication market

Economics (Bijeljina), 2022

The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company's client base and predict potential customers who could stop to use the company's services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer's churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company's services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

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.