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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.

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Version

Install

install.packages('foretell')

Monthly Downloads

156

Version

0.2.0

License

GPL-3

Maintainer

Srihari Jaganathan

Last Published

April 8th, 2019

Functions in foretell (0.2.0)

BG

Beta Geometric (BG) Model for Projecting Customer Retention.
exltrend

Excel based trendlines for projecting customer retention.
LCW

Latent Class Weibull (LCW) Model for Projecting Customer Retention
persistency_data

Drug persistency (retention) rates by different therapeutic class.
customer_retention

Observed % Customers Surviving at Least 0-12 Years
BdW

Beta discrete Weibull (BdW) Model for Projecting Customer Retention.