Profit maximizing logistic regression modeling for customer churn prediction

Eugen Stripling · Seppe vanden Broucke · Katrien Antonio · Bart Baesens · Monique Snoeck

20 citations0/0 files storedIEEE International Conference on Data Science and Advanced Analytics (DSAA)

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

Abstract

The selection of classifiers which are profitable is becoming more and more important in real-life situations such as customer churn management campaigns in the telecommunication sector. In previous works, the expected maximum profit (EMP) metric has been proposed, which explicitly takes the cost of offer and the customer lifetime value (CLV) of retained customers into account. It thus permits the selection of the most profitable classifier, which better aligns with business requirements of end-users and stake holders. However, modelers are currently limited to applying this metric in the evaluation step. Hence, we expand on the previous body of work and introduce a classifier that incorporates the EMP metric in the construction of a classification model. Our technique, called ProfLogit, explicitly takes profit maximization concerns into account during the training step, rather than the evaluation step. The technique is based on a logistic regression model which is trained using a genetic algorithm (GA). By means of an empirical benchmark study applied to real-life data sets, we show that ProfLogit generates substantial profit improvements compared to the classic logistic model for many data sets. In addition, profit-maximized coefficient estimates differ considerably in magnitude from the maximum likelihood estimates.

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References

2003Benchmarking state-of-the-art classification algorithms for credit scoring 2000Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior 2006Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2010Building comprehensible customer churn prediction models with advanced rule induction techniques 2012A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models 2014Analytics In A Big Data World: The Essential Guide To Data Science And Its Applications

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2019Customer Churn Prediction Model Based on LSTM and CNN in Music Streaming 2018Profit driven decision trees for churn prediction

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