BTYD: Implementing Buy 'Til You Die Models

Description Usage Arguments Details Value References Examples

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

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bgbb.PlotTrackingInc(params, rf.matrix, actual.inc.repeat.transactions,
  xlab = "Time", ylab = "Transactions", xticklab = NULL,
  title = "Tracking Incremental 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 the number of transactions opportunities in the calibration period ("n.cal"), and the number of customers with this number of transaction opportunities in the calibration period ("custs"). Columns for frequency and recency may be in the matrix, but are not necessary for this function since it relies on `bgbb.Expectation`, which only requires the number of transaction opportunities.
`actual.inc.repeat.transactions`	vector containing the incremental 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 assume 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 it is 0).

Matrix containing actual and expected incremental 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. http://www.brucehardie.com/papers/020/

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
actual.inc.repeat.transactions <- donationsSummary$annual.trans

# 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.PlotTrackingInc(est.params, rf.matrix, actual.inc.repeat.transactions, xticklab=x.tickmarks)