bgbb.rf.matrix.DERT: BG/BB Discounted Expected Residual Transactions using a...

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

Description

Computes the number of discounted expected residual transactions by a customer, conditional on their behavior in the calibration period.

Usage

bgbb.rf.matrix.DERT(params, rf.matrix, d)

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.
d	discount rate.

Value

The present value of the expected future transaction stream for a particular customer.

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. See equation 14.

See Also

bgbb.DERT

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)

# compute DERT for a customer from every row in rf.matrix,
# discounted at 10%.
bgbb.rf.matrix.DERT(est.params, rf.matrix, d = 0.1)

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