Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees

The goal of the paper is to present the framework for combining clustering and classification for churn management in telecommunications. Considering the value of market segmentation, we propose a three-stage approach to explain and predict the churn in telecommunications separately for different market segments using cluster analysis and decision trees. In the first stage, a case study churn dataset is prepared for the analysis, consisting of demographics, usage of telecom services, contracts and billing, monetary value, and churn. In the second stage, k-means cluster analysis is used to identify market segments for which chi-square analysis is applied to detect the clusters with the highest churn ratio. In the third stage, the chi-squared automatic interaction detector (CHAID) decision tree algorithm is used to develop classification models to identify churn determinants at the clusters with the highest churn level. The contribution of this paper resides in the development of the structured approach to churn management using clustering and classification, which was tested on the churn dataset with a rich variable structure. The proposed approach is continuous since the results of market segmentation and rules for churn prediction can be fed back to the customer database to improve the efficacy of churn management.

Review of Data Mining Techniques for Detecting Churners in the Telecommunication Industry

The telecommunication sector has been developed rapidly and with large amounts of data obtained as a result of increasing in the number of subscribers, modern techniques, data-based applications, and services. As well as better awareness of customer requirements and excellent quality that meets their satisfaction. This satisfaction raises rivalry between firms to maintain the quality of their services and upgrade them. These data can be helpfully extracted for analysis and used for predicting churners. Researchers around the world have conducted important research to understand the uses of Data mining (DM) that can be used to predict customers' churn. This paper supplies a review of nearly 73 recent journalistic articles starting in 2003 to introduce the different DM techniques used in many customerbased churning models. It epitomizes the present literature in the field of communications by highlighting the impact of service quality on customer satisfaction, detecting churners in the telecoms industry, in addition to the sample size used, the churn variables used and the results of various DM technologies. Eventually, the most common techniques for predicting telecommunication churning such as classification, regression analysis, and clustering are included, thus presenting a roadmap for new researchers to build new churn management models.

Customer segmentation for churn management by using ant colony algorithm

It is extremely important for companies to set customer priorities and act in line with these priorities. The ant colony algorithm is used to perform customer segmentation. To do this, the shortest path approach was chosen. Besides, clustering is done by the Euclidean distance formula in the ant colony algorithm. The customer segmentation attributes are mostly related to the satisfaction factors, but some of them were eliminated by using ranker. These results are mostly related to the customer’s income, tenure, equip call card and reside. These attributes are the most important satisfaction factors not to lose customers as expected. There are many reasons in changing GSM operator for subscribers, and it is very important for companies to predict if subscriber will change GSM operator or not. For this reason, companies that give GSM services have to monitor subscribers’ behaviour and predict one step forward. In this study, changing subscribers’ GSM operator will be predicted by using data mining techniques. Keywords: Ant colony, churn management, customer segmentation, data mining. Categories: I.2.1, I.2

The Impact of Customer Churn Factors (CCF) on Customer's Loyalty

This article analyzes the impact of customer churn factors on improving the customer loyalty towards telecommunication service providers in Egypt. To accomplish this, a descriptive method is used. 1500 unique e-mails of customers of telecommunication service providers who have used telecommunication services of these providers were randomly selected. With a 25.6% response rate, the questionnaires were distributed through email and self-administered for data collection. Linear regression analysis was used on the responses. The results showed that there is a statistically significant relationship between customer churn factors and customer loyalty to improve factors and increase loyalty achievement to the telecommunication service providers in Egypt, The implications of the study are that the providers should better manage their relationships with the customers as a competitive policy in the telecommunication service marketplace. It can do that by customer churn management to decrease churn rate and increase customer loyalty.