mbgcnbd.EstimateParameters: (M)BG/CNBD-k Parameter Estimation

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

(M)BG/CNBD-k Parameter Estimation

Description

Estimates parameters for the (M)BG/CNBD-k model via Maximum Likelihood Estimation.

Usage

mbgcnbd.EstimateParameters(
  cal.cbs,
  k = NULL,
  par.start = c(1, 3, 1, 3),
  max.param.value = 10000,
  trace = 0
)

bgcnbd.EstimateParameters(
  cal.cbs,
  k = NULL,
  par.start = c(1, 3, 1, 3),
  max.param.value = 10000,
  trace = 0
)

mbgnbd.EstimateParameters(
  cal.cbs,
  par.start = c(1, 3, 1, 3),
  max.param.value = 10000,
  trace = 0
)

Arguments

cal.cbs	Calibration period customer-by-sufficient-statistic (CBS) data.frame. It must contain a row for each customer, and columns x for frequency, t.x for recency , T.cal for the total time observed, as well as the sum over logarithmic intertransaction times litt, in case that k is not provided. A correct format can be easily generated based on the complete event log of a customer cohort with elog2cbs.
k	Integer-valued degree of regularity for Erlang-k distributed interpurchase times. By default this k is not provided, and a grid search from 1 to 12 is performed in order to determine the best-fitting k. The grid search is stopped early, if the log-likelihood does not increase anymore when increasing k beyond 4.
par.start	Initial (M)BG/CNBD-k parameters. A vector with r, alpha, a and b in that order.
max.param.value	Upper bound on parameters.
trace	If larger than 0, then the parameter values are is printed every trace-step of the maximum likelihood estimation search.

Value

A vector of estimated parameters.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ijresmar.2020.09.002")}

Batislam, E. P., Denizel, M., & Filiztekin, A. (2007). Empirical validation and comparison of models for customer base analysis. International Journal of Research in Marketing, 24(3), 201-209. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ijresmar.2006.12.005")}

See Also

bgnbd.EstimateParameters

Examples

## Not run: 
data("groceryElog")
cbs <- elog2cbs(groceryElog)
(params <- mbgcnbd.EstimateParameters(cbs))

## End(Not run)

mplatzer/BTYDplus documentation built on April 9, 2024, 3:11 a.m.