empChurn: empChurn

Description

Estimates the EMP for customer churn prediction, considering constant CLV and a given cost of contact f and retention offer d.

Usage

empChurn(scores, classes, alpha = 6, 
beta = 14, clv = 200, d = 10, f = 1)

Arguments

scores

A vector of predicted probabilities.

classes

A vector of true binary class labels.

alpha

Alpha parameter of unimodel beta distribution.

beta

Beta parameter of unimodel beta distribution.

clv

Constant CLV per retained customer.

Constant cost of retention offer.

Constant cost of contact.

Value

An EMP object with four components.

The Maximum Profit of the ROC curve at MPfrac cutoff.

MPfrac

The percentage of cases that should be excluded, that is, the percentual cutoff at MP profit.

EMP

The Expected Maximum Profit of the ROC curve at EMPfrac cutoff.

EMPfrac

The percentage of cases that should be excluded, that is, the percentual cutoff at EMP profit.

References

Verbraken, T., Wouter, V. and Baesens, B. (2013). A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models. Knowledge and Data Engineering, IEEE Transactions on. 25 (5): 961-973. Available Online: http://ieeexplore.ieee.org/iel5/69/6486492/06165289.pdf?arnumber=6165289 Verbraken, T., Bravo, C., Weber, R. and Baesens, B. (2014). Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research. 238 (2): 505 - 513. Available Online: http://www.sciencedirect.com/science/article/pii/S0377221714003105

Examples

Run this code

# NOT RUN {
# Construct artificial probability scores and true class labels
score.ex <- runif(1000, 0, 1)
class.ex <- unlist(lapply(score.ex, function(x){rbinom(1,1,x)}))

# Calculate EMP measures for customer churn prediction
empChurn(score.ex, class.ex)

# Calculate EMP measures for customer churn prediction with
# lower clv and higher costs
empChurn(score.ex, class.ex, clv = 100, d = 30, f = 5)
# }