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

In BTYD: Implementing BTYD Models with the Log Sum Exp Patch

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

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)

Example output

Loading required package: hypergeo
                          0        1        2        3        4        5
actual.frequency   3464.000 1823.000 1430.000 1085.000 1036.000 1063.000
expected.frequency 3454.814 1888.599 1348.864 1113.393 1017.963 1027.222
                          6
actual.frequency   1203.000
expected.frequency 1253.144

BTYD documentation built on Nov. 18, 2021, 1:10 a.m.