Advances and Applications in Statistics
Volume 18, Issue 1, Pages 41 - 55
(September 2010)
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STATISTICAL MODELS FOR CUSTOMER LIFETIME VALUE
Silvia Figini and Paolo Giudici
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Abstract: We consider the problem of estimating customer lifetime value on the basis of survival analysis models adapted to a different environment, the estimation of customer�s life cycles. In such a context, a number of statistical modeling challenges arise. We present our methodological approach, based on bayesian survival models and compare it with classical churn models on a real data set coming from a media service company that aims to predict churn behaviours, in order to entertain appropriate retention actions. |
Keywords and phrases: churn models, statistical data mining, multidimensional data analysis, predictive models. |
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