DocumentCode :
3703593
Title :
Profit maximizing logistic regression modeling for customer churn prediction
Author :
Eugen Stripling;Seppe vanden Broucke;Katrien Antonio;Bart Baesens;Monique Snoeck
Author_Institution :
Department of Decision Sciences and Information Management, KU Leuven Naamsestraat 69, B-3000 Leuven, Belgium
fYear :
2015
Firstpage :
1
Lastpage :
10
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.
Keywords :
"Logistics","Loss measurement","Performance evaluation","Training","Genetic algorithms"
Publisher :
ieee
Conference_Titel :
Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on
Print_ISBN :
978-1-4673-8272-4
Type :
conf
DOI :
10.1109/DSAA.2015.7344874
Filename :
7344874
Link To Document :
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