| Churn {bayesQR} | R Documentation |
Customer Churn Data
Description
This dataset is a stratified random sample from all active customers (at the end of June 2006) of a European financial services company. The dependent variable in this dataset is the churn behavior of the customers in the period from July 1st until December 31th 2006. Here a churned customer is defined as someone who closed all his/her bank accounts with the company. Note that all predictor variables are standardized. This dataset is a small subset of the dataset used in Benoit and Van den Poel (2013). The dataset is structured as a dataframe with 400 observations and 5 variables.
Usage
data("Churn")Format
The data frame has the following components:
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churn : churn (yes/no)
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gender : gender of the customer (male = 1)
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Social_Class_Score : social class of the customer
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lor : length of relationship with the customer
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recency : number of days since last purchase
Source
Benoit, D.F. and Van den Poel, D. (2013). Quantile regression for database marketing: methods and applications. In: Coussement, K., De Bock, K.W. and Neslin, S.A. (eds.). Advanced database marketing: Innovative methodologies and applications for managing customer relationships. Gower Publishing: London (UK). <doi:10.4324/9781315565682>
References
Benoit, D.F. and Van den Poel, D. (2013). Quantile regression for database marketing: methods and applications. In: Coussement, K., De Bock, K.W. and Neslin, S.A. (eds.). Advanced database marketing: Innovative methodologies and applications for managing customer relationships. Gower Publishing: London (UK). <doi:10.4324/9781315565682>
Benoit, D.F and Van den Poel, D. (2017). bayesQR: A Bayesian Approach to Quantile Regression, Journal of Statistical Software, 76(7), 1-32. <doi:10.18637/jss.v076.i07>