BTYDplus (version 1.2.0)

mbgcnbd.ConditionalExpectedTransactions: (M)BG/CNBD-k Conditional Expected Transactions

Description

Uses (M)BG/CNBD-k model parameters and a customer's past transaction behavior to return the number of transactions they are expected to make in a given time period.

Usage

mbgcnbd.ConditionalExpectedTransactions(params, T.star, x, t.x, T.cal)
bgcnbd.ConditionalExpectedTransactions(params, T.star, x, t.x, T.cal)

Arguments

params

A vector with model parameters k, r, alpha, a and b, in that order.

T.star

Length of time for which we are calculating the expected number of transactions.

x

Number of repeat transactions in the calibration period T.cal, or a vector of calibration period frequencies.

t.x

Recency, i.e. length between first and last transaction during calibration period.

T.cal

Length of calibration period, or a vector of calibration period lengths.

Value

Number of transactions a customer is expected to make in a time period of length t, conditional on their past behavior. If any of the input parameters has a length greater than 1, this will be a vector of expected number of transactions.

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. 10.1016/j.ijresmar.2020.09.002

Examples

# NOT RUN {
data("groceryElog")
cbs <- elog2cbs(groceryElog)
params <- mbgcnbd.EstimateParameters(cbs, k = 2)
# estimate transactions for next 12 weeks
xstar.est <- mbgcnbd.ConditionalExpectedTransactions(params,
  T.star = 12, cbs$x, cbs$t.x, cbs$T.cal)
head(xstar.est) # expected number of transactions for first 6 customers
sum(xstar.est) # expected total number of transactions during holdout
# }