bgbb.PlotTrackingCum {BTYD} | R Documentation |

Plots the actual and expected cumulative total repeat transactions by all customers for the calibration and holdout periods. Also returns a matrix with this comparison.

bgbb.PlotTrackingCum( params, rf.matrix, actual.cum.repeat.transactions, xlab = "Time", ylab = "Cumulative Transactions", xticklab = NULL, title = "Tracking Cumulative Transactions" )

`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. |

`rf.matrix` |
recency-frequency matrix. It must contain columns for frequency ("x"), recency ("t.x"), number of transaction opportunities in the calibration period ("n.cal"), and the number of customers with this combination of recency, frequency and transaction opportunities in the calibration period ("custs"). Note that recency must be the time between the start of the calibration period and the customer's last transaction, not the time between the customer's last transaction and the end of the calibration period. |

`actual.cum.repeat.transactions` |
vector containing the cumulative number of repeat transactions made by customers in all transaction opportunities (both calibration and holdout periods). Its unit of time should be the same as the units of the recency-frequency matrix used to estimate the model parameters. |

`xlab` |
descriptive label for the x axis. |

`ylab` |
descriptive label for the y axis. |

`xticklab` |
vector containing a label for each tick mark on the x axis. |

`title` |
title placed on the top-center of the plot. |

The holdout period should immediately follow the calibration period. This
function assumes that all customers' calibration periods end on the same date,
rather than starting on the same date (thus customers' birth periods are
determined using `max(n.cal) - n.cal`

rather than assuming that they are all 0).

Matrix containing actual and expected cumulative repeat transactions.

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.

data(donationsSummary) # donationsSummary$rf.matrix already has appropriate column names rf.matrix <- donationsSummary$rf.matrix # starting-point parameters startingparams <- c(1, 1, 0.5, 3) # estimated parameters est.params <- bgbb.EstimateParameters(rf.matrix, startingparams) # get the annual repeat transactions, and transform them into # a cumulative form actual.inc.repeat.transactions <- donationsSummary$annual.trans actual.cum.repeat.transactions <- cumsum(actual.inc.repeat.transactions) # set appropriate x-axis x.tickmarks <- c( "'96","'97","'98","'99","'00","'01","'02","'03","'04","'05","'06" ) # plot actual vs. expected transactions. The calibration period was 6 periods long. bgbb.PlotTrackingCum(est.params, rf.matrix, actual.cum.repeat.transactions, xticklab=x.tickmarks)

[Package *BTYD* version 2.4.3 Index]