Predicting Customer Churn in the Insurance Industry: A Data Mining Case Study

2016 ◽  
pp. 747-751 ◽  
Author(s):  
Francesco Schena
Keyword(s):  
Data Mining ◽  
Insurance Industry ◽  
Customer Churn

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.

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

Agent-Enriched Data Mining: A Case Study in Brain Informatics

2009 ◽  
Vol 24 (3) ◽  
pp. 38-45 ◽  
Author(s):  
Ning Zhong ◽  
Shinichi Motomura
Keyword(s):  
Data Mining
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