Case studies in applying data mining for churn analysis

Lomax, S and Vadera, S 2017, 'Case studies in applying data mining for churn analysis' , International Journal of Conceptual Structures and Smart Applications, 5 (2) , pp. 22-33.

[img]
Preview
PDF - Published Version
Download (458kB) | Preview

Abstract

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.

Item Type: Article
Additional Information: This paper appears in the International Journal of Conceptual Structures and Smart Applications, edited by Simon Polovina and Simon Andrews. Copyright 2017 IGI Global, www.igi-global.com. Posted with permission of the publisher.
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: International Journal of Conceptual Structures and Smart Applications
Publisher: IGI Global
ISSN: 2166-7292
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 24 Oct 2017 08:08
Last Modified: 25 Oct 2017 18:16
URI: http://usir.salford.ac.uk/id/eprint/44004

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year