LCW: Latent Class Weibull (LCW) Model for Projecting Customer...

In sriharitn/foretell: Projecting Customer Retention Based on Fader and Hardie Probability Models

Latent Class Weibull (LCW) Model for Projecting Customer Retention

Description

LCW is a latent class weibull model implementation based on Fader and Hardie probability based projection methedology. The survivor function for LCW is

wS(t|t1,c1)+(1-w)S(t|t2,c2), 0<w<1

Usage

LCW(
  surv_value,
  h,
  lower = c(0.001, 0.001, 0.001, 0.001, 0.001),
  upper = c(0.99999, 10000, 0.999999, 10000, 0.99999),
  subjects = 1000
)

Arguments

surv_value	a numeric vector of historical customer retention percentage should start at 100 and non-starting values should be between 0 and less than 100
h	forecasting horizon
lower	lower limit used in R optim rotuine. Default is c(0.001,0.001,0.001,0.001,0.001).
upper	upper limit used in R optim rotuine. Default is c(0.99999,10000,0.999999,10000,0.99999).
subjects	Total number of customers or subject default 1000

Value

fitted:	Fitted Values based on historical data
projected:	Projected h values based on historical data
max.likelihood:	Maximum Likelihood of LCW
params - t1, t2, c1, c2, w:	Returns t1,c1,t2,c2,w paramters from maximum likelihood estimation

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.

Examples

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)

sriharitn/foretell documentation built on Feb. 2, 2025, 10:25 a.m.