foretell (version 0.2.0)

LCW: 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))

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).

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

# NOT RUN {
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)

# }