Query: Churn prediction model in telecom

title: A Customer Churn Prediction Model in Telecom Industry Using Boosting
abstract: With the rapid growth of digital systems and associated information technologies, there is an emerging trend in the global economy to build digital customer relationship management (CRM) systems. This trend is more obvious in the telecommunications industry, where companies become increasingly digitalized. Customer churn prediction is a main feature of in modern telecomcommunication CRM systems. This research conducts a real world study on customer churn prediction and proposes the use of boosting to enhance a customer churn prediction model. Unlike most research that uses boosting as a method to boost the accuracy of a given basis learner, this paper tries to separate customers into two clusters based on the weight assigned by the boosting algorithm. As a result, a higher risk customer cluster has been identified. Logistic regression is used in this research as a basis learner, and a churn prediction model is built on each cluster, respectively. The result is compared with a single logistic regression model. Experimental evaluation reveals that boosting also provides a good separation of churn data; thus, boosting is suggested for churn prediction analysis.

title: A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector
abstract: In the telecom sector, a huge volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining the existing ones. Business analysts and customer relationship management (CRM) analyzers need to know the reasons for churn customers, as well as, behavior patterns from the existing churn customers' data. This paper proposes a churn prediction model that uses classification, as well as, clustering techniques to identify the churn customers and provides the factors behind the churning of customers in the telecom sector. Feature selection is performed by using information gain and correlation attribute ranking filter. The proposed model first classifies churn customers data using classification algorithms, in which the Random Forest (RF) algorithm performed well with 88.63% correctly classified instances. Creating effective retention policies is an essential task of the CRM to prevent churners. After classification, the proposed model segments the churning customer's data by categorizing the churn customers in groups using cosine similarity to provide group based retention offers. This paper also identified churn factors that are essential in determining the root causes of churn. By knowing the significant churn factors from customers' data, CRM can improve productivity, recommend relevant promotions to the group of likely churn customers based on similar behavior patterns, and excessively improve marketing campaigns of the company. The proposed churn prediction model is evaluated using metrics, such as accuracy, precision, recall, f measure, and receiving operating characteristics (ROC) area. The results reveal that our proposed churn prediction model produced better churn classification using the RF algorithm and customer profiling using k means clustering. Furthermore, it also provides factors behind the churning of churn customers through the rules generated by using the attribute selected classifier algorithm.

title: A fuzzy based churn prediction and retention model for prepaid customers in telecom industry
abstract: Accurate and timely identification of the potential churner, also known as churn prediction, is crucial to devise effective retention strategies. A number of churn prediction models have been proposed in the past, however, the existing models suffer from a number of limitations due to which these models are not applicable on real world large size telecom datasets. Firstly, the feature selection methods adopted in majority of the previous models neglected the information rich variables present in Call Details Records (CDRs) Secondly, the present models have been validated mainly with benchmark datasets which don't provide a true representation of a large scale real world data in telecom sector. Thirdly, there is very limited amount of work reported in literature that has extended the prediction models towards automatic and intelligent retention mechanisms. Moreover, categorization and severity of the predicted churners has not been focused in the past for targeted and intelligent retention campaigns. Motivated by the aforementioned limitations, we propose a novel churn prediction and retention model for achieving the aim of accurate identification and targeted retention of churners. Primarily, our contribution is the accurate identification of churners at different severity levels using fuzzy based classifiers. Secondarily, our model automatically generates intelligent retention campaigns by mining customer usage and complaints patterns. Experimental results on real world telecom data of a South Asian company revealed the supremacy of fuzzy classifiers in terms of classification by achieving 98% accuracy of churner class. Moreover, the proposed retention strategy based on churner severity and categorization managed to retain 87% of the potential churners.

title: A churn prediction model for prepaid customers in telecom using fuzzy classifiers
abstract: The incredible growth of telecom data and fierce competition among telecommunication operators for customer retention demand continues improvements, both strategically and analytically, in the current customer relationship management (CRM) systems. One of the key objectives of a typical CRM system is to classify and predict a group of potential churners form a large set of customers to devise profitable and targeted retention campaigns for keeping a long term relationship with valued customers. For achieving the aforementioned objective, several churn prediction models have been proposed in the past for the accurate identification of the customers who are prone to churn. However, these previously proposed models suffer from a number of limitations which place strong barriers towards the direct applicability of such models for accurate prediction. Firstly, the feature selection methods adopted in majority of the past work neglected the information rich variables present in call details record for model development. Secondly, selection of important features was done through statistical methods only. Although statistical methods have been applied successfully in diverse domains, however, these methods alone without the augmentation of domain knowledge have the tendency to yield erroneous results. Thirdly, the previous models have been validated mainly with benchmark datasets which do not provide a true representation of real world telecom data consisting of noise and large number of missing values. Fourthly, the evaluation measures used in the past neglected the True Positive (TP) rate, which actually highlights the ability of a model to correctly classify the percentage of churners as compared to non churners. Finally, the classifiers used in the previous models completely neglected the use of fuzzy classification methods which perform reasonably well for data sets with noise. In this paper, a fuzzy based churn prediction model has been proposed and validated using a real data from a telecom company in South Asia. A number of predominant classifiers namely, Neural Network, Linear regression, C4.5, SVM, AdaBoost, Gradient Boosting and Random Forest have been compared with fuzzy classifiers to highlight the superiority of fuzzy classifiers in predicting the accurate set of churners.

title: Customer churn prediction in telecom using machine learning in big data platform
abstract: Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3% Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST" However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.

title: Telecom Churn Prediction Model Using Data Mining Techniques
abstract: Recently several churn prediction models are being introduced that are able to predict possible churners to the service provider, in order to provide a retention solutions, i.e. offering them some compensations and special packages to keep them attached with their network. Predictive models can correctly identify possible churners. In this paper we are trying to implement a new prediction model that is uses data mining techniques on data obtained from a leading telecom company in Pakistan and then measure the generated benefits. The analysis shows a tremendous cost saving as compared to the expenses incurred in performing the churn management in a traditional way. We have employed the Decision Tree, Support Vector Machine and Neural Network classifiers on a six months subscriber's data sample to predict the churners' behavior.

title: Customers Churn Prediction using Artificial Neural Networks (ANN) in Telecom Industry
abstract: s To survive in the fierce competition of telecommunication industry and to retain the existing loyal customers, prediction of potential churn customer has become a crucial task for practitioners and academicians through predictive modeling techniques. The identification of loyal customers can be done through efficient predictive models. By allocation of dedicated resources to the retention of these customers would control the flow of dissatisfied consumers thinking to leave the company. This paper proposes artificial neural network approach for prediction of customers intending to switch over to other operators. This model works on multiple attributes like demographic data, billing information and usage patterns from telecom companies data set. In contrast with other prediction techniques, the results from Artificial Neural Networks (ANN) based approach can predict the telecom churn with accuracy of 79% in Pakistan. The results from artificial neural network are clearly indicating the churn factors, hence necessary steps can be taken to eliminate the reasons of churn.  Neural Network; ANN; prediction; churn management

title: Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction
abstract: In this paper, we consider the construction of an optimization algorithm designed to identify, in automatic way, the optimal parameters and algorithms for binary classification in the task of customer churn prediction in telecommunication. The currently supported methods for classification include Decision Trees, k Nearest Neighbors, Support Vector Machines, and Back Propagation Artificial Neural Networks. It is shown that in certain cases the standard textbook metrics of classification model quality (e.g. accuracy, precision, recall, and AUC) are not descriptive enough. Thus, we evaluate the algorithms using the modified recall metrics: Weighted Recall metric, and Weighted Recall and Run Time metric. These metrics are appropriate for use in automatic mode without continuous expert supervision. We use genetic algorithms as optimization algorithms. To improve standard implementations, we introduce a combined optimization approach based on D.H. de Vries model of disaster evolution and the island model. Keywords binary classification; artificial neural network; genetic algorithm; combined optimization; performance metric.

title: Fuzzy Poisson Naive Bayes (FPNB) Model for Customer behavior Analysis and Hybrid Cuckoo Harmony Search (HCHS) based Feature Selection for Churn Prediction in Telecom Industry

title: Selective Ensemble Model for Telecom Churn Prediction
abstract: Customer based companies are concerned about costumers who decide to stop using their services (churn) because the cost of acquiring new customer is much higher than satisfying an existing customer. With the rapid development of the telecom industry, churn prediction emerges as one of the fundamental tasks for gaining the competitive advantage in the market. This paper introduces Selective Ensemble Model (SEM) as a powerful technique for churn prediction. Among a set of machine learning models, SEM dynamically selects a combination of models to participate in forming the final outcome. Experimental results show that SEM outperforms its constituent models and the averaging ensemble model.