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

C K Praseeda,B L Shivakumar

doi:10.1007/s42452-021-04576-7

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 highlightsCustomer 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.

Highlights

In developed countries, the telecom industry has been established as one of the leading industrial sectors, in which the level of competitions is in peak, in terms of technology advancements as well as the growing number of service providers [1]
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
Industries are required to diminish the possibility of customer churn in terms of enforcing this approach, namely "the customer movement from one provider to another" [5].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 (CRM) [6, 7]

Summary

Introduction

The telecom industry has been established as one of the leading industrial sectors, in which the level of competitions is in peak, in terms of technology advancements as well as the growing number of service providers [1]. It can be concluded that the third strategy is foremost beneficial, while assessing all the three approaches by considering each of their Return on Investment (RoI) value [2]. Moroever, it shows that the existing customer retention cause lesser expense than obtaining new customer [3]. It shows that the existing customer retention cause lesser expense than obtaining new customer [3] It requires solely a minimal effort than upselling approach [4]. The approaches of data preprocessing is developed to resolve these problems, which eradicates the data noise and enable the approach to categorize the data in a proper manner, and eventually, with better efficiency

Methods

Results

Conclusion