BTYD: Implementing Buy 'Til You Die Models

Description Usage Arguments Details Value References

Computes the (l,m)th product moment of the joint posterior distribution of P (the Bernoulli transaction process parameter) and Theta (the geometric dropout process parameter).

1	bgbb.PosteriorMeanLmProductMoment(params, l, m, x, t.x, n.cal)

`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.
`l`	moment degree of P
`m`	moment degree of Theta
`x`	number of repeat transactions a customer made in the calibration period, or a vector of calibration period transaction frequencies.
`t.x`	recency - the last transaction opportunity in which a customer made a transaction, or a vector of recencies.
`n.cal`	number of transaction opportunities in the calibration period, or a vector of calibration period transaction opportunities.

E((P)^l(Theta)^m | alpha, beta, gamma, delta, x, t.x, n)

x, t.x, and n.cal may be vectors. The standard rules for vector operations apply - if they are not of the same length, shorter vectors will be recycled (start over at the first element) until they are as long as the longest vector. It is advisable to keep vectors to the same length and to use single values for parameters that are to be the same for all calculations. If one of these parameters has a length greater than one, the output will be also be a vector.

The expected posterior (l,m)th product moment.

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/

See equation 17.

retina-dot-ai/BTYD documentation built on May 22, 2019, 12:17 p.m.