scholarly journals Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

2021 ◽  
Author(s):  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
Phong Thanh Nguyen ◽  
Mohamed Elhoseny ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Optimization Algorithm ◽  
Service Providers ◽  
Sampling Rate ◽  
Optimal Sampling ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Multi Objective ◽  
Customer Churn ◽  
Customer Churn Prediction

AbstractCustomer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

scholarly journals Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning

2021 ◽  
Vol 5 (2) ◽  
pp. 62-72
Author(s):  
Asif Yaseen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Service Providers ◽  
Mobile Commerce ◽  
Personal Digital Assistants ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Feature Selection Technique ◽  
Customer Churn ◽  
Feature Selection Approach

With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.

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

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.

A comparison of machine learning techniques for customer churn prediction

2015 ◽  
Vol 55 ◽  
pp. 1-9 ◽  
Author(s):  
T. Vafeiadis ◽  
K.I. Diamantaras ◽  
G. Sarigiannidis ◽  
K.Ch. Chatzisavvas
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Churn Prediction ◽  
Customer Churn ◽  
Learning Techniques ◽  
Customer Churn Prediction

Feature intersection for agent-based customer churn prediction

2019 ◽  
Vol 53 (3) ◽  
pp. 318-332
Author(s):  
Sandhya N. ◽  
Philip Samuel ◽  
Mariamma Chacko
Keyword(s):  
Service Providers ◽  
Mobile User ◽  
Telecommunication Service ◽  
Agent Based Model ◽  
Churn Prediction ◽  
Content Type ◽  
Agent Based ◽  
User Behaviour ◽  
Customer Churn ◽  
Customer Churn Prediction

Purpose Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue. Design/methodology/approach The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider. Findings The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. Research limitations/implications The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour. Practical implications This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit. Originality/value This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.

scholarly journals Customer Churn Prediction using Predictive Analytics in Telecommunication Market: A Review

2019 ◽  
pp. 97-105
Keyword(s):  
Performance Metrics ◽  
Prediction Models ◽  
Service Providers ◽  
Predictive Analytics ◽  
Churn Prediction ◽  
Customer Churn ◽  
Telecommunication Market ◽  
Company Limited ◽  
The Face ◽  
Customer Churn Prediction

In the face of extreme competitive telecommunication market, the cost of acquiring new customer is much more expensive than to retain the existing customer. Therefore, it has become imperative to pay much attention towards retaining the existing customers in order to get stabilize in market comprised of vibrant service providers. In current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. This article reviews the customer churn prediction problem, factors escalating the phenomena, prediction through predictive analytics, steps for processing of predictive analytics and evaluation of performance metrics for various churn prediction models are surveyed. Moreover, the CRM data from Pakistan Telecommunication Company limited as case study to discuss the process of data mining and predictive analytics for customer churn prediction.

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