Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

Download Full-text

One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

Mathematical Problems in Engineering ◽

10.1155/2014/869628 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15

Author(s):

Jin Xiao ◽

Bing Zhu ◽

Geer Teng ◽

Changzheng He ◽

Dunhu Liu

Keyword(s):

Customer Value ◽

Missing Values ◽

Credit Scoring ◽

Customer Relationship ◽

Classifier Ensemble ◽

Ensemble Model ◽

Churn Prediction ◽

Customer Data ◽

Customer Churn ◽

One Step

Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

Download Full-text

Prediction of Customer Churn on e-Retailing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9550.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 5541-5545

Keyword(s):

Customer Relationship ◽

Churn Prediction ◽

Customer Data ◽

Customer Churn ◽

Customer Interaction ◽

Huge Impact ◽

Business To Consumer ◽

Past Data ◽

Customer Services ◽

And Behavior

The technology has always been an instigating factor in progress for human civilization which resulted in driving the customer services to a greater need. The enrichment of technology has amplified and embellished the customer interaction among various business to consumer sectors. These technological upgrading have a huge impact on the retail industry which is an ever-growing market with key competitors around the world. In a consortium of multiple competitors in the same business, the re-engagement of disinterested customers is essential rather than winning a new customer. The sustenance of a customer can be figure out by Churn Prediction. Churn prediction is a new promising method in customer relationship management to analyze customer retention in subscription-based business. It is the activity of identifying customer with a high probability to discontinue the company based on analyzing their past data and behavior. It looks at what kind of customer data are typically used, do some analysis of the features chosen, and initiate a churn prediction model. Thus, churn prediction is a valuable approach in identifying and profiling the customers at risk.

Download Full-text

Customer Churn Prediction using Logistic Regression with Regularization and Optimization Technique

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7219.079920 ◽

2020 ◽

Vol 9 (9) ◽

pp. 334-339

Keyword(s):

Data Mining ◽

Logistic Regression ◽

Optimization Technique ◽

Customer Relationship ◽

Communication Process ◽

Prediction Algorithm ◽

Churn Prediction ◽

Customer Data ◽

Customer Churn ◽

Customer Churn Prediction

Customer Relationship Management (CRM) is a challenging issue in marketing to better understand the customers and maintaining long-term relationships with them to increase the profitability. It plays a vital role in customer centered marketing domain which provides a better service and satisfies the customer requirements based on their characteristics in consuming patterns and smoothes the relationship where various representatives communicate and collaborate. Customer Churn prediction is one of the area in CRM that explores the transaction and communication process and analyze the customer loyalty. Data mining ease this process with classification techniques to explore pattern from large datasets. It provides a good technical support to analyze large amounts of complex customer data. This research paper applies data mining classification technique to predict churn customers in three variant sectors Banking, Ecommerce and Telecom. For Classification, enhanced logistic regression with regularization and optimization technique is applied. The work is implemented in Rapid miner tool and the performance of the prediction algorithm is assessed for three variant sectors with suitable evaluation metrics.

Download Full-text

Deep Convolutional Neural Networks for Customer Churn Prediction Analysis

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2020010101 ◽

2020 ◽

Vol 14 (1) ◽

pp. 1-16

Author(s):

Alae Chouiekh ◽

El Hassane Ibn El Haj

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Use Case ◽

Churn Prediction ◽

Deep Convolutional Neural Networks ◽

Customer Churn

Several machine learning models have been proposed to address customer churn problems. In this work, the authors used a novel method by applying deep convolutional neural networks on a labeled dataset of 18,000 prepaid subscribers to classify/identify customer churn. The learning technique was based on call detail records (CDR) describing customers activity during two-month traffic from a real telecommunication provider. The authors use this method to identify new business use case by considering each subscriber as a single input image describing the churning state. Different experiments were performed to evaluate the performance of the method. The authors found that deep convolutional neural networks (DCNN) outperformed other traditional machine learning algorithms (support vector machines, random forest, and gradient boosting classifier) with F1 score of 91%. Thus, the use of this approach can reduce the cost related to customer loss and fits better the churn prediction business use case.

Download Full-text

Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry

SN Applied Sciences ◽

10.1007/s42452-021-04576-7 ◽

2021 ◽

Vol 3 (6) ◽

Author(s):

C. K. Praseeda ◽

B. L. Shivakumar

Keyword(s):

Customer Relationship Management ◽

Relationship Management ◽

Local Information ◽

Customer Relationship ◽

Churn Prediction ◽

Swarm Optimization ◽

Customer Churn ◽

Kernel Distance ◽

Telecom Industry ◽

Hybrid Kernel

Abstract Customer churn has been considered as one of the key issues in the operations of the corporate business sector, as it influences the turnover directly. In particular, the telecom industries are seeking to develop new approaches to predict potential customer to churn. So, it needs the appropriate algorithms to overcome the increasing problem of churn. This work proposed a churn prediction model that employs both strategies of classification and clustering, that helps in recognizing the churn consumers and giving the reasons after the churning of subscribers in the industry of telecom. The process of information gain and fuzzy particle swarm optimization (FPSO) has been executed by the method of feature selection, besides the divergence kernel-based support vector machine (DKSVM) classifier is employed in categorizing churn customers in the proposed approach. In this way, the compelling guidelines on retention have generated since the process plays a vital role in customer relationship management (CRM) to suppress the churners. After the classification process, the churn customers are divided into clusters through the process of fragmenting the data of churning customer. The cluster-based retention offers have provided by the clustering algorithm of hybrid kernel distance-based possibilistic fuzzy local information C-means (HKD-PFLICM), whereas the measurement of distance have accomplished through the kernel functions such as the hyperbolic tangent kernel and Gaussian kernel. The results reveal that proposed churn prediction model (FPSO- DKSVM) produced better churn classification results compared to other existing algorithms such as K-means, flexible K-Medoids, fuzzy local information C-means (FLICM), possibilistic FLICM (PFLICM) and entropy weighting FLICM (EWFLICM). Article highlights Customer churn is a major concern in most of the companies as it influences the turnover directly. The performance of churn prediction has been improved by applying artificial intelligence and machine learning techniques. Churn prediction plays a crucial role in telecom industry, as they are in the position to maintain their precious customers and organize their Customer Relationship Management.

Download Full-text

Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning

Journal of Business Research ◽

10.1016/j.jbusres.2012.12.008 ◽

2013 ◽

Vol 66 (9) ◽

pp. 1629-1636 ◽

Cited By ~ 53

Author(s):

Kristof Coussement ◽

Koen W. De Bock

Keyword(s):

Beneficial Effect ◽

Ensemble Learning ◽

Online Gambling ◽

Churn Prediction ◽

Gambling Industry ◽

Customer Churn ◽

Customer Churn Prediction

Download Full-text

A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

Discrete Dynamics in Nature and Society ◽

10.1155/2021/7160527 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ming Zhao ◽

Qingjun Zeng ◽

Ming Chang ◽

Qian Tong ◽

Jiafu Su

Keyword(s):

Prediction Model ◽

Empirical Research ◽

Customer Value ◽

Customer Relationship ◽

Churn Prediction ◽

Customer Churn ◽

Data Mining Algorithms ◽

Customer Churn Prediction ◽

Telecom Industry ◽

Logistic Regression Algorithm

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

Download Full-text

Employee Churn Rate Prediction and Performance Using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1134.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 824-826

Keyword(s):

Machine Learning ◽

Cost Effective ◽

Machine Learning Algorithms ◽

Prediction System ◽

Employee Behavior ◽

Churn Prediction ◽

Design Expert ◽

And Performance ◽

Business Cost ◽

Vital Resource

A person working for an organization is the vital resource which is known as an employee. If one of them leaves company suddenly, this could affect and cost massive amount to respective company. And recruitment would consume not only time and money but also the newly joined person needs some time for making particular business cost-effective. This model will help to predict rate at which employees are quitting jobs based on obtained analytic data accessible and use different machine learning algorithms to decrease prediction error. Personalized or individual employee’s prediction is different with respect to environment they are working in. While it has become apparent that employee churn prediction responds differently to salary, depending on their location, lifestyle, and environment, the linked knowledge and understanding remain fragmented. In this paper, we aim to design expert prediction system to deal with problems associated with lack of knowledge of employee behavior, to aware organizations about the importance of employee, to prevent unnecessary employee churn, and to improve growth of both separately

Download Full-text

Customer Churn Prediction for Imbalanced Class Distribution of Data in Business Sector

Journal of Advanced College of Engineering and Management ◽

10.3126/jacem.v5i0.26693 ◽

2019 ◽

Vol 5 ◽

pp. 101-110

Author(s):

Aayush Bhattarai ◽

Elisha Shrestha ◽

Ram Prasad Sapkota

Keyword(s):

Machine Learning ◽

Imbalanced Data ◽

Telecommunications Industry ◽

Business Case ◽

Machine Learning Algorithms ◽

Business Sector ◽

Churn Prediction ◽

Telecommunication Sector ◽

Customer Churn ◽

Customer Churn Prediction

Churners are those people who are about to transfer their business to a competitor or simply who cancel a subscription to a service. This paper is based on a specific business sector, which is telecommunication sector. With a churn rate of 30%, the telecommunication sector takes the first place on the list. In this paper, we present some advanced data mining methodologies which predicts customer churn in the pre-paid mobile telecommunications industry using a call detail records dataset. To implement the predictive models, we initially propose and then apply four machine learning algorithms: Random Forest, Naïve Bayes, Logistic Regression, and XG Boost. To evaluate the models, we use various evaluation metrics and find the best model which will be suitable for any class imbalanced data and also our business case. This paper can also be viewed as a comparative study on the most popular machine learning methods applied to the challenging problem of customer churn prediction.

Download Full-text