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

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

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

Description

BdW is a beta discrete weibull model implemented based on Fader and Hardie probability based projection methedology. The survivor function for BdW is

Beta(a,b+t^c)/Beta(a,b)

Usage

BdW(
  surv_value,
  h,
  lower = c(0.001, 0.001, 0.001),
  upper = c(10000, 10000, 10000),
  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(1e-3,1e-3).
upper	upper limit used in R optim rotuine. Default is c(10000,10000,10000).
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 Beta discrete Weibull
params - a, b and c:	Returns a and b paramters from maximum likelihood estimation for beta distribution and c

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)
h <- 6
BdW(surv_value,h)

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