scholarly journals 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

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.

scholarly journals Churn Prediction using Machine Learning-An Analytical CRM Application

2020 ◽  
Vol 9 (5) ◽  
pp. 1948-1952
Keyword(s):  
Machine Learning ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Churn Prediction ◽  
Support Organizations ◽  
Forecast Models ◽  
Telecom Industry ◽  
Enormous Number

CRM represents (Customer Relationship Management).It is a classification of programming that covers many arrangement of utilizations that are intended to support organizations and furthermore to oversee huge numbers of the business forms like client information. CRM framework models incorporate stages worked to oversee advertising, deals, client support, and backing, all associated with assistance organizations work all the more viably. With a CRM framework, organizations can dissect client collaborations and improve their client connections. The data based forecast models utilizing AI systems have increased monstrous prevalence during the most recent couple of decades. These models have been applied in enormous number of areas like clinical conclusion, wrongdoing expectation, films rating, and so forth. Thus it is utilized in telecom industry where models of expectation have been applied for the forecast of not fulfilled clients who are probably going to change the administrations and furthermore the specialist organization. In telecom the money related expense of client agitate is tremendous henceforth numerous organizations have examined different variables, (for example, cost of the call, nature of the call, client assistance reaction time and so on.) utilizing different AI strategies. This work proposes different ML strategies for client agitate expectation.

From Local Information Systems to Global Customer Relationship Management

2014 ◽  
pp. 299-305 ◽  
Author(s):  
Ulas Akkucuk
Keyword(s):  
Eastern Europe ◽  
Customer Relationship Management ◽  
Information Technologies ◽  
Relationship Management ◽  
Local Information ◽  
Customer Relationships ◽  
Customer Relationship ◽  
The World ◽  
Flow Of Information ◽  
Opening Up

Advances in computer and information technologies have been utilized by companies all over the world since the 1990s. Corresponding roughly to the same period, global trade has increased dramatically. The opening up of large markets like China and the Eastern Europe contributed to this trend. National companies turned global and had to manage operations in a number of different countries. Companies strived to maintain better customer relationships through CRM programs aimed at managing the flow of information, interacting with the customers, and in the end, formulating individualized offerings for them. Globalization has led to the development of the new notion of Global Customer Relationship Management as opposed to having independent local CRM programs operating in the subsidiaries. This chapter presents the issues facing the implementation of such Global CRM programs and provides the important conceptual frameworks proposed in the literature.

ROI for a Customer Relationship Management Initiative at GST

2017 ◽  
pp. 1-22
Author(s):  
Mark Jeffery ◽  
Robert J. Sweeney ◽  
Robert J. Davis
Keyword(s):  
Customer Relationship Management ◽  
Return On Investment ◽  
Real Life ◽  
Relationship Management ◽  
Customer Relationship ◽  
Customer Acquisition ◽  
Net Income ◽  
Case Scenario ◽  
Customer Churn ◽  
Strategic Objective

In this return on investment (ROI) for customer relationship management (CRM) case scenario, students must calculate the ROI for analytic CRM enabled by an enterprise data warehouse. The case is based upon a real-life consulting engagement with a major Fortune 100 telecommunications company. In this case the executive management team's strategic objective is to grow the customer base by 5 percent annually by customer acquisition. The internal rate of return calculated from the data given in the case is more than 800 percent for one year, and sensitivity analysis shows this is a robust projection, suggesting it should be funded without question. However, the strategy of the firm is customer acquisition in an environment of high customer churn. As a result of these dynamics, the revenues and net income of the firm are actually decreasing by hundreds of millions of dollars each year. A better solution would realize that the executive team has the incorrect strategic objective. Customer acquisition is the wrong approach in an environment of high customer churn and executives should focus on customer retention and cross-sell and up-sell to high-value customers. The case discussion therefore takes students beyond CRM ROI to focuses on the key strategic concepts of customer relationship management.Students learn how to calculate return on investment (ROI) for analytic customer relationship management (CRM) initiatives. The case also discusses in detail the difference between operational CRM and analytic CRM. The case solution is relatively straightforward with a very good ROI. However, the true learning of the case is for students to understand the strategic context of analytic CRM and to question assumptions in any ROI model.

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

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.

From Local Information Systems to Global Customer Relationship Management

2015 ◽  
pp. 759-765
Author(s):  
Ulas Akkucuk
Keyword(s):  
Eastern Europe ◽  
Customer Relationship Management ◽  
Information Technologies ◽  
Relationship Management ◽  
Local Information ◽  
Customer Relationships ◽  
Customer Relationship ◽  
The World ◽  
Flow Of Information ◽  
Opening Up

Advances in computer and information technologies have been utilized by companies all over the world since the 1990s. Corresponding roughly to the same period, global trade has increased dramatically. The opening up of large markets like China and the Eastern Europe contributed to this trend. National companies turned global and had to manage operations in a number of different countries. Companies strived to maintain better customer relationships through CRM programs aimed at managing the flow of information, interacting with the customers, and in the end, formulating individualized offerings for them. Globalization has led to the development of the new notion of Global Customer Relationship Management as opposed to having independent local CRM programs operating in the subsidiaries. This chapter presents the issues facing the implementation of such Global CRM programs and provides the important conceptual frameworks proposed in the literature.

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