Applying Data Mining to Customer Churn Analysis: A Case Study on the Insurance Industry of Iran

2013 ◽  
Vol 1 (1) ◽  
pp. 8
Author(s):  
Keyvan Vahidy Rodpysh
Keyword(s):  
Data Mining ◽  
Insurance Industry ◽  
Customer Churn ◽  
Churn Analysis

scholarly journals Case Studies in Applying Data Mining for Churn Analysis

2017 ◽  
Vol 5 (2) ◽  
pp. 22-33
Author(s):  
Susan Lomax ◽  
Sunil Vadera
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Case Studies ◽  
Decision Tree Learning ◽  
Customer Churn ◽  
Use Of Data ◽  
Churn Analysis ◽  
First Case ◽  
Product Comparison

The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This paper presents two case studies that utilize decision tree learning methods to develop models for predicting churn for a software company. The first case study aims to predict churn for organizations which currently have an ongoing project, to determine if organizations are likely to continue with other projects. While the second case study presents a more traditional example, where the aim is to predict organizations likely to cease being a subscriber to a service. The case studies include presentation of the accuracy of the models using a standard methodology as well as comparing the results with what happened in practice. Both case studies show the significant savings that can be made, plus potential increase in revenue by using decision tree learning for churn analysis.

The Application of Data Mining Technology in the Customer Churn Prewarning

2014 ◽  
Vol 644-650 ◽  
pp. 2198-2201
Author(s):  
Li Chang Zhen ◽  
Xin Gao ◽  
Yi Ming Wang ◽  
Yong Chun Gao
Keyword(s):  
Data Mining ◽  
Telecommunication Industry ◽  
Mining Technology ◽  
Tree Algorithm ◽  
Data Mining Techniques ◽  
Customer Churn ◽  
Meaningful Information ◽  
Churn Analysis

With the further reform and market division in the telecommunication industry, there are more and more choices for customers to select telecom products and operators, which lead to the fiercer competition for customers between telecom operators. As the technical method to identify customers churn, the data mining can help the telecom competitors to analyze some seemingly unrelated data into meaningful information. On the basis of the research on the vital problems in the telecom companies, this paper explains how to apply data mining techniques to customer churn analysis, proposes the specific procedures and technology solutions to prevent the customer churn and builds the models of the data mining by analyzing the related algorithm. Finally, based on the systematical analysis on theory and method to data mining, the paper draws the conclusion that the customers churn listing and tree algorithm can solve the practical problems of the customer churn in telecommunication industry.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

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