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TreeLogit Model for Customer Churn Prediction

Authors: Jiayin Qi; Yangming Zhang; Yingying Zhang; Shuang Shi;

TreeLogit Model for Customer Churn Prediction

Abstract

For the purpose of improving the predictive accuracy and interpret ability of churn prediction model, TreeLogit model, which integrates the advantage of AD tree model and logistic regression model, is proposed in this paper to predict customers' churn propensities. Compared with TreeNetreg, a model which won the gold prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament) , the overall predictive accuracy of TreeLogit model is not inferior. In fact, the predictive accuracy of TreeLogit is superior to the one of TreeNetreg on two important observation points (i.e. the captured rates in 10% and 50% of the most likely churn groups respectively)

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citations
The total number of citations received by all articles of the researcher of interest.
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Average
Top 10%
Average
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