mbgcnbd.pmf: (M)BG/CNBD-k Probability Mass Function

In BTYDplus: Probabilistic Models for Assessing and Predicting your Customer Base

Description

Uses (M)BG/CNBD-k model parameters to return the probability distribution of purchase frequencies for a random customer in a given time period, i.e. P(X(t)=x|r,alpha,a,b).

Usage

mbgcnbd.pmf(params, t, x)

bgcnbd.pmf(params, t, x)

Arguments

params	A vector with model parameters k, r, alpha, a and b, in that order.
t	Length end of time period for which probability is being computed. May also be a vector.
x	Number of repeat transactions for which probability is calculated. May also be a vector.

Value

P(X(t)=x|r,alpha,a,b). If either t or x is a vector, then the output will be a vector as well. If both are vectors, the output will be a matrix.

References

(M)BG/CNBD-k: Reutterer, T., Platzer, M., & Schroeder, N. (2020). Leveraging purchase regularity for predicting customer behavior the easy way. International Journal of Research in Marketing. doi: 10.1016/j.ijresmar.2020.09.002

Examples

## Not run: 
data("groceryElog")
cbs <- elog2cbs(groceryElog)
params <- mbgcnbd.EstimateParameters(cbs)
mbgcnbd.pmf(params, t = 52, x = 0:6)
mbgcnbd.pmf(params, t = c(26, 52), x = 0:6)

## End(Not run)

Example output

         0          1          2          3          4          5          6 
0.37402021 0.15535694 0.09439949 0.06625482 0.05002957 0.03943061 0.03194107 
          26         52
0 0.41709817 0.37402021
1 0.18160807 0.15535694
2 0.10953134 0.09439949
3 0.07397624 0.06625482
4 0.05272559 0.05002957
5 0.03872847 0.03943061
6 0.02898367 0.03194107

BTYDplus documentation built on Jan. 21, 2021, 5:10 p.m.