Case Studies in Applying Data Mining for Churn Analysis

Case Studies in Applying Data Mining for Churn Analysis

Susan Lomax (University of Salford, Salford, UK) and Sunil Vadera (University of Salford, Salford, UK)
DOI: 10.4018/IJCSSA.2017070102
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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.
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Background

Churn, also known as turnover, defection or attrition is the loss of clients or customers. Many domains such as banks, mobile phone companies, internet service providers and supermarkets use churn analysis and churn rates as a key business metric as it has been shown that the cost of retaining an existing customer is less than the cost of acquiring new customers (Wei & Chiu 2002; Hung et al., 2006; Huang et al., 2012). These existing customers tend to purchase more than new customers and it is more efficient to deal with existing customers than dealing with new customers (Fornell & Wernerfelt 1987; 1988; Reichheld & Sasser 1990; Bolton 1998).

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