Research trends in customer churn prediction: A data mining approach

This study aims to present a very recent literature review on customer churn prediction based on 40 relevant articles published between 2010 and June 2020. For searching the literature, the 40 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to six main dimensions: Reference; Areas of Research; Main Goal; Dataset; Techniques; outcomes. The research has proven that the most widely used data mining techniques are decision tree (DT), support vector machines (SVM) and Logistic Regression (LR). The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. Therefore, the telecom company can effectively predict the churn of customers, and then avoid customer churn by taking measures such as reducing monthly fixed fees. The present literature review offers recent insights on customer churn prediction scientific literature, revealing research gaps, providing evidences on current trends and helping to understand how to develop accurate and efficient Marketing strategies. The most important finding is that artificial intelligence techniques are are obviously becoming more used in recent years for telecom customer churn prediction. Especially, artificial NN are outstandingly recognized as a competent prediction method. This is a relevant topic for journals related to other social sciences, such as Banking, and also telecom data make up an outstanding source for developing novel prediction modeling techniques. Thus, this study can lead to recommendations for future customer churn prediction improvement, in addition to providing an overview of current research trends.info:eu-repo/semantics/acceptedVersio

Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifie

Projection pursuit random forest using discriminant feature analysis model for churners prediction in telecom industry

A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model

Telecomm Subscriber Management System(TSMS)

In the current fast growing rate of mobile phone users, Telecomm Subscriber Management System (TSMS) leads to a good management of subscribers’ number by indicating the most current status of a subscriber number in telecommunication world. It helps to improve the management of subscriber numbers efficiently and also it helps to increase the revenue generated event (RGE) by the subscriber where lies the goals and objectives of any mobile telecommunication operators. This work is an automation of manual processes whereby we validate data first to ensure that there is no redundant data then we proceed to the subscriber classification. In Parallel there are several concerns that this project will provide an efficient improvement of subscriber number management that helps the telecommunication company to make significant profit in order to ensure its success in the market. There are problems are : inefficient subscriber’s number management which is leading to difficulties in identifying subscriber number’s status as well difficulties to clarify the usage of the block that have owned the company. There are several research papers that have been discussed about this. This work is a significant enhancement of resealed methods procedures of the researches and studies done previously. The implementation of this system will help the company in keeping the database updated, will reduce missing data and data redundancy as well as data inconsistency and unreliable data in the database as well as it has great advantages in their profit margin improvement

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy

Prediction of Customers Churn in Telecommunication Industry

In the developed world, mobile markets have reached saturation on subscriber penetration and connections growth. The challenge for operators has evolved from attracting new customers to retaining existing ones. Various components have an impact on churn. Therefore, it is very important to understand the behaviour of the customers, encourage them in spending more and then predicting the future by preventing their attrition. As the industry is evolving, the biggest challenge for operators is to engage with consumers and retain their loyalty by delivering more competitive and innovative value-added services. While understanding consumer needs remains essential to improve customer retention, other emerging tariffs and services are likely to carry a long-term impact on churn (including national, international and roaming bundles tariffs and mobile services). The churn might be voluntary in cases they want to leave the network they actually are using, or involuntary churn in case of unpaid bills. The methodology used to do the right evaluations in order to achieve strong results in this field is very large and varied. The scope of this thesis is to identify and analyse different appropriate models that can help the data analysts to find the churners in Telecommunication industry. In this thesis we are going to discuss on two important topics in telecommunication markets and their respective predictive models, which tend to understand the customer behaviour towards different competitors: market share in telecommunication industry and customer churn

A data-driven approach to improve customer churn prediction based on telecom customer segmentation

Numerous valuable clients can be lost to competitors in the telecommunication industry, leading to profit loss. Thus, understanding the reasons for client churn is vital for telecommunication companies. This study aimed to develop a churn prediction model to predict telecom client churn through customer segmentation. Data were collected from three major Chinese telecom companies, and Fisher discriminant equations and logistic regression analysis were used to build a telecom customer churn prediction model. According to the results, it can be concluded that the telecom customer churn model constructed by regression analysis had higher prediction accuracy (93.94%) and better results. This study will help telecom companies efficiently predict the possibility of and take targeted measures to avoid customer churn, thereby increasing their profits.Numerous valuable clients can be lost to competitors in the telecommunication industry, leading to profit loss. Thus, understanding the reasons for client churn is vital for telecommunication companies. This study aimed to develop a churn prediction model to predict telecom client churn through customer segmentation. Data were collected from three major Chinese telecom companies, and Fisher discriminant equations and logistic regression analysis were used to build a telecom customer churn prediction model. According to the results, it can be concluded that the telecom customer churn model constructed by regression analysis had higher prediction accuracy (93.94%) and better results. This study will help telecom companies efficiently predict the possibility of and take targeted measures to avoid customer churn, thereby increasing their profits.info:eu-repo/semantics/publishedVersio

A Novel Chimp Optimized Linear Kernel Regression (COLKR) Model for Call Drop Prediction in Mobile Networks

Call failure can be caused by a variety of factors, including inadequate cellular infrastructure, undesirable system structuring, busy mobile phone towers, changing between towers, and many more. Outdated equipment and networks worsen call failure, and installing more towers to improve coverage might harm the regional ecosystems. In the existing studies, a variety of machine learning algorithms are implemented for call drop prediction in the mobile networks. But it facing problems in terms of high error rate, low prediction accuracy, system complexity, and more training time. Therefore, the proposed work intends to develop a new and sophisticated framework, named as, Chimp Optimized Linear Kernel Regression (COLKR) for predicting call drops in the mobile networks. For the analysis, the Call Detail Record (CDR) has been collected and used in this framework. By preprocessing the attributes, the normalized dataset is constructed using the median regression-based filtering technique. To extract the most significant features for training the classifier with minimum processing complexity, a sophisticated Chimp Optimization Algorithm (COA) is applied. Then, a new machine learning model known as the Linear Kernel Regression Model (LKRM) has been deployed to predict call drops with greater accuracy and less error. For the performance assessment of COLKR, several machine learning classifiers are compared with the proposed model using a variety of measures. By using the proposed COLKR mechanism, the call drop detection accuracy is improved to 99.4%, and the error rate is reduced to 0.098%, which determines the efficiency and superiority of the proposed system

An Optimized Approach for Maximizing Business Intelligence using Machine Learning

The subject of study known as business intelligence is responsible for the development of techniques and tools for the analysis of business information with the goal of assisting in the management and decision-making processes of corporations. In the current climate, business intelligence is essential to the process of formulating a strategy and carrying out operations that are data-driven. Throughout the many stages of the company operation, an organization will need assistance evaluating data and making decisions; a decision support system may provide this assistance by including business intelligence as an essential component. The fact that this enormous quantity of data is distributed over a number of different types of platforms, however, makes it a difficult challenge, in particular to understand the information that is actually relevant and to make efficient use of it for business intelligence. One of the most important challenges facing modern society is maximizing business intelligence through the application of machine learning. It offers a full analysis that is based on predictions and is extracted for Business Intelligence techniques along with current application fields. This anomalous gap has been pointed up, and solutions and future research areas have been offered to overcome it in order to create effective business strategies