bgbb.ConditionalExpectedTransactions {BTYD} | R Documentation |

Calculates the number of expected transactions in the holdout period, conditional on a customer's behavior in the calibration period.

bgbb.ConditionalExpectedTransactions(params, n.cal, n.star, x, t.x)

`params` |
BG/BB parameters - a vector with alpha, beta, gamma, and delta, in that order. Alpha and beta are unobserved parameters for the beta-Bernoulli transaction process. Gamma and delta are unobserved parameters for the beta-geometric dropout process. |

`n.cal` |
number of transaction opportunities in the calibration period. Can also be a vector of calibration period transaction opportunities - see details. |

`n.star` |
number of transaction opportunities in the holdout period, or a vector of holdout period transaction opportunities. |

`x` |
the number of repeat transactions made by the customer in the calibration period. Can also be vector of frequencies - see details. |

`t.x` |
recency - the transaction opportunity in which the customer made their last transaction. Can also be a vector of recencies - see details. |

E(X(n, n+n*) | alpha, beta, gamma, delta, x, t.x, n). This function requires the holdout period to immediately follow the calibration period.

`n.cal`

, `n.star`

, `x`

, and `t.x`

may be vectors. The standard rules for
vector operations apply - if they are not of the same length, shorter vectors
will be recycled (start over at the first element) until they are as long as
the longest vector. It is advisable to keep vectors to the same length and to
use single values for parameters that are to be the same for all
calculations. If one of these parameters has a length greater than one, the
output will be a vector of probabilities.

The number of transactions a customer is expected to make in the
`n.star`

transaction opportunities following the calibration period,
conditional on their behavior during the calibration period.

Fader, Peter S., Bruce G.S. Hardie, and Jen Shang.
"Customer-Base Analysis in a Discrete-Time Noncontractual Setting."
*Marketing Science* 29(6), pp. 1086-1108. 2010. INFORMS.
Web.

params <- c(1.20, 0.75, 0.66, 2.78) # the number of transactions a customer is expected # to make in the 10 transaction opportunities # following the calibration period, which consisted # of 6 transaction opportunities (during which they # made 3 transactions, the last of which occurred # in the 4th opportunity) bgbb.ConditionalExpectedTransactions(params, n.cal=6, n.star=10, x=3, t.x=4) # We can also use vectors as input: bgbb.ConditionalExpectedTransactions(params, n.cal=6, n.star=1:10, x=3, t.x=4) bgbb.ConditionalExpectedTransactions(params, n.cal=6, n.star=10, x=1:4, t.x=4)

[Package *BTYD* version 2.4.3 Index]