BTYD (version 2.4.3)

bgbb.PlotFrequencyInCalibration: BG/BB Plot Frequency in Calibration Period

Description

Plots the actual and expected number of customers who made a certain number of repeat transactions in the calibration period. Also returns a matrix with this comparison.

Usage

bgbb.PlotFrequencyInCalibration(
  params,
  rf.matrix,
  censor = NULL,
  plotZero = TRUE,
  xlab = "Calibration period transactions",
  ylab = "Customers",
  title = "Frequency of Repeat Transactions"
)

Arguments

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.

censor

optional. Any calibration period frequency at this number, or above it, will be binned together. If the censor number is greater than the maximum recency in the recency-frequency matrix, the maximum recency will be used as the censor number.

plotZero

If FALSE, the histogram will exclude the zero bin.

xlab

descriptive label for the x axis.

ylab

descriptive label for the y axis.

title

title placed on the top-center of the plot.

Value

Calibration period repeat transaction frequency comparison matrix, actual vs. expected.

References

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.

Examples

# NOT RUN {
data(donationsSummary)

rf.matrix <- donationsSummary$rf.matrix
# donationsSummary$rf.matrix already has appropriate column names

# starting-point parameters
startingparams <- c(1, 1, 0.5, 3)
# estimated parameters
est.params <- bgbb.EstimateParameters(rf.matrix, startingparams)

# plot actual vs. expected frequencies in the calibration period
bgbb.PlotFrequencyInCalibration(est.params, rf.matrix)
# }