Projecting Customer Retention Based on Fader and Hardie Probability Models

Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class Discrete Weibull Models.This package is based on Fader and Hardie (2007) and Fader and Hardie et al. (2018) .

foretell

Project Customer Retention based on Fader and Hardie et. al. Probability Mixture Models

Installation

You can install the stable version from CRAN.

install.packages('foretell', dependencies = TRUE)

You can install the development version from Github

# install.packages("devtools")
devtools::install_github("forecaster18/foretell")

Usage

library(foretell)


# Beta Geometric
surv_value <- c(100,86.9,74.3,65.3,59.3)
h <- 6
BG(surv_value,h)

# Beta Discrete Weibull
surv_value <- c(100,86.9,74.3,65.3,59.3)
h <- 6
BdW(surv_value,h)

# Latent Class Discrete Weibull
surv_value <- c(100,86.9,74.3,65.3,59.3,55.1,51.7,49.1,46.8,44.5,42.7,40.9,39.4)
h <- 6
LCW(surv_value,h)

References

• Fader P, Hardie B. How to project customer retention. Journal of Interactive Marketing. 2007;21(1):76-90.
• Fader P, Hardie B, Liu Y, Davin J, Steenburgh T. "How to Project Customer Retention" Revisited: The Role of Duration Dependence. Journal of Interactive Marketing. 2018;43:1-16.

License

This package is free and open source software, licensed under GPL-3.