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Predicting Telecommunication Customer Churn Using Data Mining Techniques

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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 This article aims to predict reasons behind customers‘ churn in the mobile communication Find read and cite Recently market. In this study, different data mining techniques such as logistic Abstract Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM)

Machine Learning Approaches to Predict Customer Churn in ...

Abstract : This study focuses on predicting customer churn in the telecommunication industry researchers worldwide using machine learning techniques. The research explores the use of Decision Tree, Random

Churn Prediction Made Simple & Top 9 ML Techniques

This paper presents an advanced methodology for predicting customers churn in mobile telecommunication industry by applying data mining techniques on a dataset consisting Various techniques have been used by various researchers but use of data mining techniques for predicting customers churn has turned out to be an efficient approach with high accuracy in

In this research paper, we aim to analyze the factors that contribute to customer churn in the telecom industry and develop effective retention strategies to reduce churn rates and improve

Customer churn, the phenomenon of customers terminating their subscription or services with a telecom provider, poses a significant challenge in the telecom industry. Predicting customer Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM)

Customer churn is a critical issue in the telecommunications industry, and accurately predicting churn can help companies implement strategies to retain customers. In We have to find out the factors that increase customer churn for making necessary actions to reduce churn. In the past, different data mining techniques have been used for predicting the

This study aims to improve customer churn prediction by integrating machine learning algorithms and evaluating their performance using criteria like accuracy, profit, and Request most of the companies PDF | On Jan 1, 2013, C. Kirui and others published Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining | Find, read and cite

Recently, data mining techniques have emerged to tackle the challenging problems of customer churn in telecommunication service field (Au et al., 2003, Coussement and den This study applied data mining techniques to predict customer churn in the banking sector using three different classification algorithms, namely: decision tree (J48), In addition, efficient complaints handling plays a key role in winning back customers. However, the problem of the customer churn prediction (CCP) cannot be

A Decade of Churn Prediction Techniques in the TelCo Domain

Keywords Data mining · Customer churn prediction · Machine learning · Supervised learning · SaaS Introduction One of the most significant shares of most

As the market in telecom is fiercely competitive, in that case, companies proactively have to determine the customers churn by analyzing their behavior and try to put effort aims to apply classification algorithms and Recently, data mining techniques have emerged to tackle the challenging problems of customer churn in field (, ). As one of the important measures to retain customers,

Understanding the most successful models allows telecom companies to adopt ML-driven strategies that not only decrease churn rates but also improve customer The paper explores the application of data mining techniques to predict customer churn in the telecommunications to enhance Customer churn or industry, focusing on value-added services (VAS). It highlights the Specifically, this research uses data mining techniques to find a best model of predictive churn from data warehouse to prevent the customers turnover, further to enhance

Customer churn or Customer attrition is a serious issue in the telecommunication industry. Today, most of the examines the companies in the telecom sector face this problem that leads them to lose revenue. To be successful in this

In this paper, process of designing such a decision support system through data mining technique is described. The proposed model is capable of predicting customers churn

Customer churn prediction has been performed using various techniques, including data mining, machine learning, and hybrid technologies.These techniques enable and support companies in

Churn Prediction in Telecommunication Using Data Mining Technology

N.Kamalraj, A.Malathi’ ― A Survey on Churn Prediction Techniques in Communication Sector‖ in IJCA Volume 64– No.5, February 2013 Kiran Dahiya,KanikaTalwar, ―Customer Churn The study conducted in this paper aims to develop a churn prediction model using actual data from a Portuguese software house and incorporate Data Mining techniques for data

Sharma and Panigrahi (2013) applied Gradient Boosting to predict customer churn in the telecom industry, achieving high accuracy and demonstrating its superiority over traditional methods .

Deep learning techniques can be very beneficial and favorable because businesses today deal with large amount of data points that machine learning techniques Abstract Churn prediction is an active topic for research and machine learning approaches have made significant contributions in this domain. Models built to address Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use

Abstract. This research aims to apply classification algorithms to telecommunication customer churn data using Orange Data Mining. The methods used include Support Vector Machine What is Churn prediction? Churn prediction is the process of identifying customers who are likely to stop using a company’s products or services in the near future. This involves

We present a comparative study on the most popular machine learning methods applied to the challenging problem of customer churning prediction in the telecommunications