A novel cost-sensitive framework for customer churn predictive modeling

Alejandro Correa Bahnsen · Djamila Aouada · Björn Ottersten

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Customer churn predictive modeling deals with predicting the probability of a customer defecting using historical, behavioral and socio-economical information. This tool is of great benefit to subscription based companies allowing them to maximize the results of retention campaigns. The problem of churn predictive modeling has been widely studied by the data mining and machine learning communities. It is usually tackled by using classification algorithms in order to learn the different patterns of both the churners and non-churners. Nevertheless, current state-of-the-art classification algorithms are not well aligned with commercial goals, in the sense that, the models miss to include the real financial costs and benefits during the training and evaluation phases. In the case of churn, evaluating a model based on a traditional measure such as accuracy or predictive power, does not yield to the best results when measured by the actual financial cost, ie. investment per subscriber on a loyalty campaign and the financial impact of failing to detect a real churner versus wrongly predicting a non-churner as a churner. In this paper, we present a new cost-sensitive framework for customer churn predictive modeling. First we propose a new financial based measure for evaluating the effectiveness of a churn campaign taking into account the available portfolio of offers, their individual financial cost and probability of offer acceptance depending on the customer profile. Then, using a real-world churn dataset we compare different cost-insensitive and cost-sensitive classification algorithms and measure their effectiveness based on their predictive power and also the cost optimization. The results show that using a cost-sensitive approach yields to an increase in cost savings of up to 26.4 %.

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References

2008Application of data mining techniques in customer relationship management: A literature review and classification 2006Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models 2010Marketing metrics: the definitive guide to measuring marketing performance 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 1999Trust and concern in consumers’ perceptions of marketing information management practices 2005CUSTOMER LIFETIME VALUE, CUSTOMER PROFITABILITY AND THE TREATMENT OF ACQUISITION SPENDING 2008Modeling churn using customer lifetime value 2012A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models

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2023Hyper-Personalization for Enhancing Customer Loyalty and Satisfaction in Customer Relationship Management (CRM) Systems 2018Profit Optimizing Churn Prediction for Long-Term Loyal Customers in Online Games 2021Giant fight: Customer churn prediction in traditional broadcast industry 2020Profit-driven churn prediction for the mutual fund industry: A multisegment approach

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